OPTIMIZATION OF HYBRID RENEWABLE

ENERGY BASED ELECTRIC VEHICLE

CHARGING STATION

A thesis submitted to the

Department of Electrical and Electronic Engineering (EEE)

of

Dhaka University of Engineering & Technology, Gazipur

In partial fulfillment of the requirement for the degree of

MASTER OF SCIENCE IN ELECTRICAL AND ELECTRONIC ENGINEERING

by

Ashish Kumar Karmaker

(Student ID.: 122219- P)

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING (EEE)

DHAKA UNIVERSITY OF ENGINEERING & TECHNOLOGY, GAZIPUR

January 2019

ii

The thesis titled “Optimization of Hybrid Renewable Energy based Electric Vehicle

Charging Station” submitted by Ashish Kumar Karmaker, Student ID: 122219-P, Session:

2012-2013, has been accepted as satisfactory in partial fulfillment of the requirement of the

degree of Master of Science in Electrical and Electronic Engineering on 14 January 2018.

Board of Examiners

1.

Dr. Md. Raju Ahmed

Professor& Head

Department of Electrical and Electronic Engineering

Dhaka University of Engineering & Technology, Gazipur

Chairman (Supervisor)

&

Member (Ex-Officio)

2.

Dr. Md. Anwarul Abedin

Professor

Department of Electrical and Electronic Engineering

Dhaka University of Engineering & Technology, Gazipur

Member

3.

Dr. Md. Saifuddin Faruk

Professor

Department of Electrical and Electronic Engineering

Dhaka University of Engineering & Technology, Gazipur

Member

4.

Dr. Masuma Akter

Associate Professor

Department of Electrical and Electronic Engineering

Dhaka University of Engineering & Technology, Gazipur

Member

5.

Dr. Mohammad Rubaiyat Tanvir Hossain

Professor

Department of Electrical and Electronic Engineering

Chittagong University of Engineering & Technology, Chittagong

Member (External)

i

Declaration

It is hereby declared that this thesis or any part of it has not been submitted elsewhere for the

award of any degree or diploma.

Signature of the candidate

------------------------------

Ashish Kumar Karmaker

(Student ID. 122219-P)

ii

Dedication

To my parents

iii

Acknowledgements

At first, I would like to express my gratitude to almighty. Then, I would like to express my

sincere gratitude to my thesis supervisor, Prof. Dr. Md. Raju Ahmed, for his continued

encouragement and support. His extreme enthusiasm toward research has motivated me

during my entire research life. I am eternally grateful for the things- both academic and

nonacademic- I have learnt from my supervisor. I will always remember the countless hours

(even in the middle of the night) we spent together discussing our research works and ideas.

I am also indebted to Prof. Dr. Md. Anwarul Abedin for his help, support, and guidelines.

My sincere gratitude and thanks to Prof. Dr. Md. Saifuddin Faruk for his valuable

guidelines throughout the research period.

Finally I would like to thanks all the faculty and staff of the EEE department for their

cooperation and motivations throughout the research.

At last, I would like to thank my father and my wife for their continuous support, love and

positive attitude towards my research life.

iv

Abstract

The sudden proliferation of Electric Vehicles (EV) like Easy Bike, Auto-Rickshaw, Electric-

Bike plays a vital role in producing energy crisis in the worldwide. As a developing country,

Bangladesh is also facing difficulties due to excess energy consumption. Although, Electric

Vehicles are opening a new dimension in the transportation sector with several benefits such

as- cheapest mode of transportation & lower greenhouse gas (GHG) emission, however the

generation of huge energy required for charging the batteries in every day is not very easy.

In addition, lack of charging station in Bangladesh hampers the time and takes higher cost

from EV owner. Owing to this reason, almost all of the EV owner takes power from the

residential connection illegally and pays the bill as the residential consumer. Thus, the

power sector is on system loss. Moreover, the non-linear characteristics of EV charger

affect the power quality by producing harmonics, voltage fluctuation and causing power

loss. To overcome the problems mentioned earlier, this research focuses on the utilization of

available renewable resources for EV charging. Bangladesh Rural Electrification Board

(BREB) and Bangladesh Power Development Board (BPDB) have already established few

solar charging stations throughout the country. These charging stations are not sufficient and

depend only on solar energy which is absent on rainy day & foggy environment. As

Bangladesh has a great potential of biogas/biomass resources, solar and biogas based

Electric Vehicle Charging Station (EVCS) would increase the effective operational hours.

This research investigates the feasibility of solar–biogas based EVCS using HOMER Pro

software. The impacts of existing EVCS on the power system network are also simulated &

analyzed by MATLAB simulation. A comparison is also demonstrated between the results

obtained from HOMER Pro software and mathematical analysis. Finally, the optimization

algorithm is developed using fuzzy logic (Mamdani and Sugeno) based on different

parameters like as output power availability, power demand, period & duration of charging.

Besides, a performance comparison is made on Mamdani and Sugeno fuzzy logic controller

after applying the same fuzzy rules to those controllers.

v

TABLE OF CONTENTS

Declaration I

Dedication II

Acknowledgements III

Abstract IV

Chapter 1 Introduction

1.1 Introduction 1

1.2 Literature Review 3

1.3 Objectives 5

1.4 Thesis Overview 6

Chapter 2 Challenges and Impacts of Electric Vehicles in Bangladesh

2.1 Introduction 7

2.2 Electric Vehicles in Bangladesh 7

2.3 Benefits and Drawbacks of Electric Vehicles 9

2.4 Charging Infrastructures in Bangladesh 10

2.5 Analysis of Challenges for Adoption of EV by Different Method 10

2.5.1 PORTER’s five forces model 10

2.5.2 PESTEL analysis 12

2.5.3 SWOT analysis 13

2.6 Challenges for Electric Vehicle adoption in Bangladesh 14

2.6.1 Shortage of power supply/load shedding 15

2.6.2 Lack of charging stations 16

2.6.3 Battery charging affects power quality issues 16

2.6.4 Battery price and capacity 16

2.6.5 High charging cost and time 17

2.6.6 Battery life time, maintenance and technology/material used 17

2.6.7 Low EV speed and Range 17

2.6.8 Frequent accident and quality of road 17

2.6.9 Lack of government support 18

2.6.10 Non-licensed vehicle 18

2.7 Impact Assessment of Electric Vehicle Charging Station 18

2.7.1 Modeling of EVCS parameters 19

2.7.2. MATLAB SIMULINK model of the EVCS 21

2.7.3 Impact Analysis 24

2.8 Policies Recommended for EV Adoption 28

2.9 Summary 30

vi

Chapter 3 Feasibility Assessment of Hybrid Renewable Energy Based EVCS

3.1 Introduction 32

3.2 Potential of renewable resources in Bangladesh 32

3.2.1 Solar Energy Potential in Bangladesh 32

3.2.2 Potentials of Biogas Energy Resources in Bangladesh 33

3.3 Mathematical modeling 37

3.3.1 Mathematical Model of Technical Parameters 37

3.3.2 Mathematical Model of the Economic Parameters 40

3.3.3 Modeling of the Environmental Parameters 42

3.4 System Component 44

3.5.1 Design of the Grid Connected Hybrid Renewable Energy Based EVCS 48

3.5.2 Design of EVCS using HOMER Pro Software 50

3.6 Technological Feasibility Analysis 51

3.7 Economic Feasibility Analysis 54

3.8 Environmental Feasibility of the Proposed EVCS 56

3.9 Comparison of results between HOMER analysis and mathematical analysis 59

3.10 Socio-Economic Aspects of the Proposed EVCS 60

3.11 MATLAB SIMULINK modeling for solar and biogas based generation 61

3.11 Summary 63

Chapter 4 Fuzzy Optimization of Proposed EVCS

4.1 Introduction 65

4.2 Block diagram of the proposed EVCS 66

4.3 Fuzzy Optimization Model 67

4.4 Input and output Variables 68

4.5 Optimization Algorithm 70

4.6 Fuzzy Rule Viewer 72

4.7 ANFIS (Adaptive Neuro Fuzzy Inference System) Model structure 73

4.8 Result and discussion 74

4.9 Summary 82

Chapter 5 Conclusion and Future Works

5.1 Conclusion 83

5.2 Future Works 84

References 85

Publications Related to Thesis 92

vii

LIST OF FIGURES

Fig. No. Figure Caption Page No.

Fig. 2.1 Electric Vehicles in Bangladesh 8

Fig. 2.2 Charging stations in Bangladesh: (a) Keraniganj and (b) Chandra,

Gazipur 10

Fig. 2.3 PORTER’S Five Forces Model 11

Fig. 2.4 PESTEL analysis for EV adoption challenges in Bangladesh 13

Fig. 2.5 SWOT Analysis for EV adoption 14

Fig. 2.6 Load curve [Source: Power Grid Company Bangladesh Ltd. (PGCB)] 15

Fig. 2.7 Impacts of Electric Vehicle 19

Fig. 2.8 Block diagram of an Electric Vehicle Charging Station 19

Fig. 2.9 MATLAB SIMULINK model of EVCS 22

Fig. 2.10 MATLAB SIMULINK Model for EV Battery Charger 23

Fig. 2.11 Battery Discharge characteristics 23

Fig. 2.12 Charging profile of an EVCS located in Gazipur district, Bangladesh 24

Fig. 2.13 Harmonics, when single EV (a), 3 EV (b) and 5 EV (c) is connected

at a charging station 25

Fig. 2.14 a) Input Voltage, before connecting charger 26

Fig. 2.14 b) Input Voltage, after connecting charger 27

Fig. 3.1 Solar irradiation in different cities of Bangladesh 33

Fig. 3.2 Types of MSW in Bangladesh 35

Fig. 3.3 Maximum electricity generation from biogas/biomass resources 36

Fig. 3.4 Grid Connected Hybrid Renewable Energy Based EVCS 48

Fig. 3.5 Block diagram of EVCS designed by HOMER Pro software 50

Fig. 3.6 Solar irradiation profile used in HOMER 50

Fig. 3.7 Temperature Curve at various seasons used in HOMER 51

Fig. 3.8 Daily available Biomass (Cow dung, poultry waste and MSW) used in

HOMER 51

Fig. 3.9 Load profile (daily, seasonal) used in HOMER. 52

Fig. 3.10 Annual energy production in kWh by resource type 53

Fig. 3.11 Percentage share of total generation by resources 53

Fig. 3.12 Annual cash flow by resources 55

Fig. 3.13 Payback period and life time of the proposed EVCS 56

Fig. 3.14 Carbon dioxide generation in renewable and conventional systems 57

Fig. 3.15 Comparison of CO2 emission from grid based EVCS and proposed

EVCS 58

Fig. 3.16 Solar PV based system model 62

Fig. 3.17 I-V & P-V Characteristics curve of PV based Model 63

Fig. 3.18 Biogas Generation output in m3 64

Fig. 4.1 Block Diagram of the Proposed EVCS 66

Fig. 4.2 Fuzzy (Mamdani) Optimization Model 67

viii

Fig. 4.3 a) Input variable “Power_ Availability” with membership functions

b) Input variable “Power_ Demand” with membership functions

c) Input variable “Period_of_Charging” with membership functions

d) Input variable “Duration_of_Charging” with membership functions

e) Output variable “Charging_Rate” with membership functions

68-70

Fig. 4.4 Optimization Algorithm for EVCS

72

Fig. 4.5 Fuzzy rule viewer 73

Fig. 4.6 ANFIS Model structure 74

Fig. 4.7 Surface view of charging rate, Power availability, power demand, time

of charging and duration of charging, (a), (b), (c) and (d) 75-76

Fig. 4.8 Variation of charging rate with (a) power availability (b) power

Demand (c) time of charging (d) duration of charging from Mamdani

fuzzy logic controller

77-78

Fig. 4.9 Variation of charging rate with (a) Power availability (b) Power demand

(c) time of charging (d) duration of charging from Sugeno fuzzy logic

controller

79

Fig. 4.10 Comparison of charging cost by fuzzy logic system with conventional

electricity price 81

ix

LIST OF TABLES

Table No. Table Title Page No.

Table 2.1. Specifications of Electric Vehicles 8

Table 2.2. Benefits and drawbacks of EV 9

Table 2.3 Transformer output at different EV load 27

Table 3.1 MSW generation scenarios of urban cities in Bangladesh 35

Table 3.2 Bio-waste to electricity conversion 36

Table 3.3 Specifications of the PV module (Canadian Solar Dymond CS6K-

285M-FG) 44

Table 3.4 Cost of the PV panels 45

Table 3.5 Digester size and cost according to IDCOL 45

Table 3.6 Cost and size of the biogas generator 46

Table 3.7 Technical parameters of the Suntree 10,000 TL 10 kW Converter 46

Table 3.8 Technical parameters of the 60, 038 MF-12 V, 100 Ah lead-acid Battery 47

Table 3.9 Technical specifications of an EV charger 47

Table 3.10 Calculation of the economic parameters 54

Table 3.11 CO2 emission rate on non-renewable sources 56

Table 3.12 CO2 generation by renewable energy 56

Table 3.13 Comparison of results from HOMER and Mathematical analysis 59

Table 3.14 Charging cost and monthly income summary of an EV 60

Table 4.1 (a) Charging rate variation with power availability in Mamdani &

Sugeno

(b) Charging rate variation with power demand in Mamdani & Sugeno

(c) Charging rate variation with time/period of charging in Mamdani &

Sugeno

(d) Charging rate variation with duration of charging in Mamdani &

Sugeno

80

10

1

Chapter 1

Introduction

1.1 Introduction

Bangladesh is an energy-starved country where natural gas and petroleum products are the

main sources of energy. Energy harvesting is performed significantly by transportation sector

which is 46.46% of the total petroleum consumption and 6% of the total natural gas

consumption. In addition, only 8% of the total demand of petroleum is reserved in

Bangladesh and every year about 1.2 million tons of crude oil and 2.6 million tons of refined

petroleum products need to import [1, 2]. So,the government has to pay a high amount of

budget to import these from abroad. Also, the Green House Gas (GHG) emission from

petroleum resources is a major environmental issue. Besides this scenario, GHG emissions

due to the transport sector increases significantly [3]. Growing concern about the GHG

emissions to the environment and low energy consumption facilities encourage peoples to use

Electric Vehicles (EV) which is also the economical mode of transportation [4].

The transport sector of Bangladesh plays a significant role in producing GHG emissions.

Major portion of these GHG emissions is CO2. A report published by International Energy

Agency showed that, approximately 23% of the GHG emitted to the environment comes from

transport vehicles [5]. Besides, the energy and agricultural sectors are accelerating the CO2

emission process [6]. The rapid increase in the number of transport vehicles to support the

huge population throughout the country is an alarming sign of environmental pollution as

well as fuel consumption. Moreover, the use of EVs like Auto-rickshaws and Easy Bikes

increases day by day [7]. These EVs produce less emission and no fumes. The Bangladesh

Road Transport Authority (BRTA) has no clear statistics about these types of EV [8].

However, the rapid increases of EV require approximately 500 MW power per day from the

national grid of Bangladesh [9].

Nowadays, EVs are charged in residential areas and the electricity bill is paid by residential

consumers. In that case, the power sector fails to gain any profit from charging those EVs.

Meanwhile, these EVs are producing a huge pressure on the national grid of Bangladesh [10].

2

EV batteries may operate from a single phase or three phase supply system. Because of wide

availability of single supply points, EV chargers are connected with this system. However,

three phase supply system gives larger power with fast charging. These EV chargers are

basically power electronic converter similar to non-linear load. This non-linear characteristic

of EV charger produce harmonics in the current and affect the voltage profile of the power

network [11]. High non-linear loading can be a cause for non-linear voltage drop and thus

voltage waveform might be distorted. On the other hand, non-linear load can affect the

performances of distribution transformer by increasing power losses in the winding and

thereby reducing its power output [12]. Thus, EV chargers when integrated with the power

grid or distribution network, it hampers the power quality. When large number of chargers is

connected with the distribution networks, the power quality problem arises [13].

In order to solve the above problems regarding EV charging, it is necessary in Bangladesh to

develop sufficient charging infrastructure, their charging coordination scheme and charging

policy. In addition, to minimize the pressure on the national grid and maintain quality power

throughout the country, a cost-effective alternative approach for generating electricity is

required [14]. However, to best of my knowledge, no initiatives to design hybrid renewable

energy-based Electric Vehicle Charging Station (EVCS) from the perspective of Bangladesh

exist, which has motivated me to do the research presented herein.

Performance of an EVCS depends upon different factors such as- power availability, load

demand, battery capacity, charging cost, charging time etc. In this research, the charging cost

optimization is performed using fuzzy logic while maximizing the utilization of renewable

resources. An energy management algorithm will be developed using fuzzy if-then rules.

Several researches were performed using fuzzy logic although hybrid renewable energy

based EVCS optimization is new one. In Bangladesh, electricity tariff is different for peak

and off-peak hour. Thus, the charging time, battery capacity, power demand and power

availability are taken as the input of the fuzzy system where charging cost is the only output

of the system. This type of optimization will save the charging cost as well as maintain the

proper use of electric energy.

3

1.2 Literature Review

The rapid increase in EV market penetration around the world has promoted the energy

consumption sector as a new research area. This section mainly reviews the challenges &

impacts of existing EVCS on power grid, utilization of renewable resources and optimization

techniques etc. The sudden proliferation of EVs make the power system more vulnerable

especially in peak hour period. The major problems of the EV deployment are insufficient

charging stations, battery technology, higher charging time & cost etc. [15]. On the other

hand, non-linear EV charger affects the power system by producing harmonics, voltage

fluctuation and power loss etc. [16]. The environmental pollution regarding EV acceptance

demonstrates that, the GHG emission would be greatly reduced if the EVs are charged by

renewables rather than coal based electricity [17].

As it is known that, increasing cost of limited fossil fuel affects the electricity &

transportation sector worldwide. In addition, these two sectors are the main contributor of the

GHG emission in the world. Thus it is right time to choose renewable energy resources

instead of petroleum resources for sustainable development in Bangladesh [18]. Limited

fossil fuel and its increasing cost is a great problem for transportation and electricity

generating sectors. To promote sustainable development in urban areas as well as rural areas

it is necessary to fulfill the electricity demand. In rural areas where grid electricity is

unavailable, electricity demand can meet using renewable resources like solar, biogas, wind

etc. [19]. Renewable resources like solar, biogas/biomass, wind are available in Bangladesh

which can be used for generating electricity for charging EVs [20].

Solar energy is available in all over the country to generate electricity effectively for 5-6

hours with solar irradiation of 4 to 6.5 kWh /m2-day [21]. Since the solar radiation is absent

in rainy, foggy days and night time, thus the operational hours for charging EV is decreased.

This is the major drawback of the solar stand-alone system [22]. In Bangladesh, necessary

wind speed is limited to only coastal and off-shore areas [23].

Moreover, the biogas resources are available throughout the country irrespective of location

& time. Das et al. explored the biomass potential of Bangladesh through gasification

technology and concluded that agriculture residues, rice husks, bagasse, wheat straw, jute

stalks, maize residues, coconut shells, forest residues and Municipal Solid Waste (MSW) are

4

the main sources of biomass [24]. Biogas production from poultry waste, animal waste/cow

dung is calculated from the data obtained from the Department of Livestock Services and

Food and Agriculture Organization (FAO) statistics. MSW data is calculated from the

Bangladesh waste-related database and the website of Sustainable and Renewable Energy

Development Agency (SREDA), Bangladesh. M. S. Shah et al. estimated the 7.6775 billion

m3 biogas potential in the fiscal year of 2012-13 which can be used as bio-CNG of 5.088

billion m3 [25]. As an agricultural country, biomass energy has a great potential to strengthen

the industrial and manufacturing sectors in Bangladesh [26]. Thus, the proper use of

biogas/biomass resources for electricity generation increases the effective operation hours

compared to solar & wind potential [27].

Charging stations in Bangladesh depend on grid electricity, but in the case of off-grid remote

areas, this type of EV charging is quite impossible. So, there is a need for stand-alone hybrid

renewable power generation in Bangladesh [28]. Rural electrification projects sometimes

may fail due to a lack of attention in the financial, technical and environmental feasibility

aspects. Rahman et al. proposed a standardized approach for decision making concerned with

the extension of electricity into remote areas to evaluate the economic, technical and

environmental feasibility [29]. Design and feasibility (technical, economic and

environmental) analysis of the combination of solar PV-biogas-diesel-wind-battery for

electrification of stand-alone remote area/island is investigated by using the HOMER

software tool [30-32]. The results are related to parameters, cost of energy, net present cost,

payback period and annual cash flow [30-32]. A feasibility study regarding a solar energy-

based Electric Vehicle charging station in Shenzhen, China was analyzed by HOMER. This

proposed model can mitigate the grid energy-based problem by integrating solar energy and

can meet the large demand needed for EVs [33]. Another feasibility study was conducted

based on solar powered charging stations for EVs in the north central region of Bulgaria [34].

However, from the research conducted in [33] and [34] explored that, in the case of rainy and

foggy days, there is no alternate option for power generation. Thus it hampers the EV

charging.

Energy management algorithm for an EVCS offers maximization of renewable energy use

with minimum charging cost. A research performed on real time energy management

algorithm helps to determine EV charging cost involving renewable energy [35]. Another

research accomplished on EV charging demonstrates that, proposed model based on fuzzy

5

logic decreases the waiting time with better performance than traditional charging station

[36]. EVCS based on fuzzy inference system exhibits optimal charging or discharging rate

considering dynamic electricity tariff and battery SOC [37]. Different algorithm has taken

battery SOC and dynamic electricity tariff as the fuzzy inputs whereas charging cost

minimization is the main objectives [38-40]. However, other factors which affect the

performance of the EVCS such as- power availability, power demanded by EVs, period and

duration of charging etc. are not taken into consideration. In Bangladesh, maximum EVs are

placed for charging at peak-hour period and load shedding is happened frequently at that

time. The EV can be recharged the batteries during off-peak hour at cheaper rates while

helping to absorb excess electricity generation. Thus, it is quite difficult for effective

charging management of EVs. Besides, output power availability from the hybrid renewable

resources for EV charging should be considered in fuzzy inputs. For these reason, the output

power availability, power demand, period of charging (peak/off peak hour) and duration of

charging are considered as fuzzy inputs and charging rate as output. This type of energy

management algorithm will help to optimize charging cost with maximization of renewable

energy use.

1.3 Objectives

The main goal of this thesis is to analyze the feasibility with technical, financial,

environmental and socio-economic aspects and design of a hybrid renewable energy based

EVCS with energy management algorithm.

The following specific objectives will be taken into consideration in the present study:

i. To analyze the challenges and impacts of EVs on the power system;

ii. To identify the prospects of the solar and biogas-based hybrid power generation

scheme in Bangladesh;

iii. To analyze the technical, financial, environmental and socio-economic

feasibility of the proposed EVCS using HOMER Pro software;

iv. To design a model of proposed EVCS suitable for Bangladesh with fuzzy

optimization technique using MATLAB.

6

1.4 Thesis Overview

This thesis is divided into five chapters-

Chapter 1 provides a general introduction followed by the background, literature review,

objectives and methodology of this research.

Chapter 2 covers a brief description of the present status of EVs, charging infrastructures. In

addition, challenges of EV adoption are analyzed by three methods i.e. PORTER’s five forces

model, PESTEL analysis, SWOT analysis etc. Then, the analysis of impact of EVs in the

power sector of Bangladesh is performed using MATLAB simulation and finally, several

policies are recommended for increasing EV acceptance in the context of Bangladesh.

Chapter 3 demonstrates the feasibility assessment & design of hybrid renewable energy

based electric vehicle charging station in the context of Bangladesh. The prospects of hybrid

power generation using solar & biogas is analyzed in this section according to the

technological, financial and environmental aspects. The results obtained from HOMER pro

software is compared with the results of mathematical analysis of the proposed EVCS.

Moreover, few model of power generation are simulated using MATLAB SIMULINK based

on solar and biogas.

Chapter 4 illustrates mathematical modeling of optimization and designing of a fuzzy logic-

based energy management algorithm for EVCS. Furthermore, differentiate between the

Mamdani and Sugeno type fuzzy controller based on their performances.

Chapter 5 contains the concluding remarks followed by few suggestions on the possible

future work.

7

Chapter 2

Challenges and Impacts of Electric Vehicle Charging

Station in Bangladesh

2.1 Introduction

The sudden proliferation of the Electric Vehicles (EV) in all corners of Bangladesh brings a new

sector in the field of transportation. Although it has numerous benefits to the users but there are

lot of difficulties appeared in EV adoption. In this chapter, the challenges of EV adoption in

Bangladesh are investigated & analyzed using PORTER’s five forces model, PESTEL analysis

and SWOT analysis. In addition, the MATLAB SIMULINK model for measuring the impacts of

the existing EVCS is developed. The MATLAB model will provide results corresponding to the

power quality disturbances during EV charging. Finally, several policies are recommended for

mitigating the impacts and overcoming challenges in the context of EV adoption in Bangladesh.

2.2 Electric Vehicles in Bangladesh

Battery run EVs were first introduced in Bangladesh in 2009. These are now extensively used in

almost all corners of the country. They are three types—Easy Bikes (which carry 4–5

passengers), Auto-rickshaws (which carry 2 passengers) and Electric rickshaw vans (which carry

goods). A fully charged Easy Bike can travel approximately 80–100 km and its market price

around $1500 whereas Auto rickshaws and Electric rickshaw vans can travel 50–70 km with a

fully charged battery and price around $ 750. At first these were imported from China but now-a-

days these are produced by domestic companies. A fully charged EV can move 80–100 km per

day and it consumes 8–11 kWh per day. According to the commercial tariff, per unit cost of

electricity is $0.1225. So, the cost per km run of an electric car is approximately $ 0.0168 and the

energy consumed per km run is approximately to 0.1375 kWh. Fig. 2.1 shows the three type of

EVs which are available throughout the country.

8

Fig. 2.1: Electric Vehicles in Bangladesh

Table 2.1 shows the specifications of Electric Vehicles in Bangladesh. This type of transport is

environmentally friendly, producing almost zero fumes and noise pollution and creating less CO2

emissions. It has bought a silent revolution to the transport sector and become popular for the

cheaper fares compared to other modes of road transportation and thus increasing the happiness

among low income people, including the drivers of the vehicles.

Table 2.1. Specifications of Electric Vehicles [41]

Easy Bike Auto-Rickshaw and Electric Rickshaw Van

Power > 800 W Power >500 W

Voltage 60 V (5 batteries of 12 V

each) Voltage 36/48 V

Load bearing capacity 300–350 kg Load bearing capacity 120–160 kg

Continued trip mileage 80–100 km Continued trip mileage 50–70 km

Charging time 6–8 h/day Charging time 4–6 h/day

Power consumption 8–11 kWh Power consumption 3.0–4.5 kWh

Electric powered Easy Bikes and Auto-rickshaw vehicles are launched by some private

initiatives, and have grown a certain level of popularity in rural area and near big cities. These

types of EVs are consuming a lot of energy from the grid but there many initiatives are being

taken to reduce the pressure on the grid. However the improvements are yet to be widespread

9

because the power rickshaws have been claimed to consume a considerable amount of electrical

energy from our national power grid.

For this thesis, a survey was conducted at Trishal upazilla which is 100 km away from Dhaka

city and 20 km from Mymensingh city. Here more than 250 various types of Easy Bikes, Auto

rickshaws and Electric rickshaw vans running every day. Only 20 manually driven rickshaws

were found. Most of the Easy Bikes are battery driven and these are charged by electricity from

residential connections which is not approved by the Govt. of Bangladesh. A few of the EVs are

Compressed Natural Gas (CNG)-driven. They have to go 10 to 15 km to refueling their cylinders

with CNG. Another problem is that the CNG station takes time to refuel these EVs because there

is a huge pressure on that station. Therefore, time and energy are wasted. On the other hand, load

shedding occurs in that area very frequently every day. Thus it is difficult to charge EVs during

that period.

In these circumstances, if the facilities were built to charge the batteries using other reliable

sources at the adjacent area, it would be better for those people to run their battery driven EVs

easily. Although there are some difficulties in charging EVs, they have had a revolutionary

impact on the transportation vehicle sector, especially in sub-rural and rural areas.

2.3 Benefits and Drawbacks of EV

There are lots of benefits using EV but mass adoption of EVs may be a threatening issue for the

power sector, transport policy regulations. The benefits & drawbacks with respect to technical

and socio-economic reasons are given below in Table 2.2.

Table 2.2: Benefits and drawbacks of EV

Benefits Drawbacks

1. Cheaper to run

2. Better for the environment

3. Easy to drive

4. Energy security

1. Low range and speed

2. Lack of charging station

3. Battery need to change after few month of continuous use

4. Frequent accident etc.

10

2.4 Charging Infrastructures in Bangladesh

Although huge number of battery EVs are running all over the country, the charging

infrastructures are not available in everywhere. Recently, government has established few solar

based charging stations in different corners of Bangladesh which seems very less in quantity.

The solar charging stations in Bangladesh shown in Fig. 2.2 are operated by the Bangladesh

Rural Electrification Board (BREB).

Fig. 2.2: Solar Charging stations in Bangladesh: (a) Keraniganj and (b) Chandra, Gazipur

These charging stations are established to recharge 15-20 EVs per day but due to several

drawbacks it turns into a loss project. As on rainy day and foggy environment, the solar energy is

absent and thus the EV battery recharging process is hampering. Also, at night time the solar

energy is not present, thus charging batteries at that time is quite difficult. For this reason, excess

battery requires to store solar energy which makes the system expensive. In this circumstance,

the solar charging stations should be modified including another renewable resource which is

sustainable throughout day and night period.

2.5 Analysis of Challenges for EV Adoption by Different Method

In this section, the PORTER’s Five Forces model, PESTEL analysis and SWOT analysis are

carried out in analyzing the challenges for acceptance of EVs in Bangladesh.

2.5.1 PORTER’s five forces model

PORTER’s five forces model is designed for competitive analysis of an industry/ organization.

Five competitive forces are used for this modeling where three forces (i.e. entrants, substitutes,

and established rivals etc.) are horizontal and two forces (i.e. supplier power and buyer power)

are vertical. This model framework had been developed by Michael E. Porter in the year of

11

1979. This model expresses the profitability of the industry and the behavior of competitive

forces for market analysis. This model is used in electric vehicle market competitiveness in a

research [42]. Fig. 2.3 shows the PORTER’s five forces model for analyzing barriers in the EV

adoption.

Fig. 2.3: PORTER’S Five Forces Model

The PORTER’s five forces model provides an analysis regarding different threats for EV adoption in

Bangladesh which are given in below.

A. Threat of Entrants: Entry of new entrant into the electric vehicle market makes it

unprofitable. Due to lack of charging infrastructure and incentives for electric vehicle, it is

barrier for entering into electric vehicle market.

B. Threat of Rivalry: Electric vehicle has to compete with the established model of

conventional car industries. The more industries in the electric vehicle market make it

competitive for market penetration.

12

C. Threat of Substitutes: Different new modeled vehicles are running in roads now-a-days

such as- CNG and LPG. These vehicles are displayed as a substitute of electric vehicles.

D. Buyer Power: Electric vehicle in Bangladesh like easy bike, auto rickshaw is cheaper than

other conventional cars. However, for long range electric vehicle it is expensive for new

buyers. Although in Bangladesh these light battery electric vehicles are not expensive, but

after sales, repair & maintenance services are very poor. Battery lifetime is also a major

concern for new buyers. Because, expensive EV batteries are need to change after 1 year.

E. Supplier Power: High technology cost and lack of manufacturing industries is the hindrance

of supplier. Although in Bangladesh, many industries are engaged to manufacture electric

vehicles & their accessories, thus it will be strong factor for EV adoption. Lack of

government policy for Electric vehicle industries, the supplier have to face different problem

regarding electric vehicles supplying.

2.5.2 PESTEL analysis

A PESTEL (Political, Economic, Social, Technological, Environmental and Legal) analysis is a

framework or tool that is used to analyze and monitor the marketing environmental factors of an

organization. The results obtained from PESTEL analysis can be used to determine the threats &

weakness of the organization in SWOT analysis. This analysis consists of the following

directions- political, economic, social, technological, environmental and legal. Macro

environmental analysis of EV market was performed in a research using PESTEL method and it

showed that, the retired batteries may be used to overcome the cost of EV penetration [43]. The

challenges for EV acceptance in Bangladesh by PESTEL analysis are shown in Fig. 2.4.

13

Fig. 2.4.: PESTEL analysis for challenges of EV adoption in Bangladesh.

2.5.3 SWOT analysis

SWOT (Strength, Weakness, Opportunities and Threats) analysis is a strategic planning system

that can be used for categorizing strengths, weakness, opportunities and threats of electric

vehicle diffusion in Bangladesh. In this thesis, the SWOT analysis is executed to know the

challenges and then describe how these challenges can be overwhelmed. The SWOT analysis for

the EV adoption is shown in Fig. 2.5.

14

Fig. 2.5: SWOT Analysis for EV Adoption.

2.6 Challenges for Electric Vehicle Adoption in Bangladesh

Mass adoption of EVs is eventually reliant on consumer’s readiness to purchase the technology.

Mainly the EV consumers count on several factors when taking a decision to accept EVs. It

includes price, range, robust and battery life. Although the numbers of EV are increasing, it will

not be sustainable and profitable if the several factors are keeping untreated. Analyzing the EV

adoption by different method i.e. PORTER’s five forces model, PESTEL analysis and SWOT

analysis, the several main factors are found as the threats & hindrance in Bangladesh.

Strengths

•Environment friendly due to less GHG emission

• Low running and maintenance cost

•Reduce the dependence on foreign oil imports

•Easy to drive

• Energy efficient transportation system

Weakness

•Limited range and low speed vehicle

• High charging time

•Battery need to change

•Low consumer awareness

• Frequent road accident

Threats

•Lack of government support

•Available compitors in electrc vehicle manufacturers

• Lack of charging stations

• Insufficient spare parts compared to the conventional vehicles

• Non-licensed vehicle

• Poor roads & Traffic conditions

Opportunities

• Increase employment opportunities

• Availabiity of skilled labor at low cost

• Improvement in battery technology

• Integration of renewable resources for electric vehicle charging

• Rising fuel cost

•Greater opportunities for research and developemnt

• Rising awareness of environmental factors for sustainable development

15

2.6.1 Shortage of power supply/load shedding

Bangladesh has a great electricity demand where only 67% of the people get electricity access.

According to the BPDB, there are 25,26,594 electricity consumer has serve demand of 10,958

MW power on 30 June 2018 [44]. The difference between maximum demand and the supply, the

load shedding occurs in different areas of Bangladesh. Also, the power loss due to the auxiliary

use at generating station, transmission & distribution networks is around 9%. Thus the supply

and demand cannot fulfill and thus load shedding arises. The daily load curve for Bangladesh

electricity sector is given in Fig. 2.6. The load curve shows that, the load increases in the peak

hour period (5 P.M. to 11 P.M.) whereas it was minimum at off-peak hour (11 P.M. to 5 P.M.).

However, the increased electricity demand causes by EVs add an extra pressure to the grid

especially at peak-hour period. It creates a great problem for minimizing demand and load-

shedding occurs. Electric auto-rickshaw and tri-cycle mainly run at villages, upazilla and small

cities in Bangladesh. In these places, load shedding occurs frequently in a day. Thus, the

shortage of power supply hampers the EV charging.

Fig. 2.6: Load curve [Source: Power Grid Company Bangladesh Ltd. (PGCB)].

16

2.6.2 Lack of charging stations

One of the major difficulties for EV adoption is insufficient charging infrastructure. To promote

EV adoption, there should have a lot of charging stations throughout the all corners of

Bangladesh. However, these charging stations are very unsatisfactory. Almost all the charging

stations are private and they have taken higher rate for EV charging. Also, the national grid is

under pressure for such type of EV charging demand especially at peak hour [45]. There is a

problem of finding free space for EV charging anytime due to long queue. Thus, the EV owner

has to wait. It kills the time which also drops the income of the EV driver. Recently there is a

trend in power sector to establish some public charging stations throughout the country. For an

example, BREB established 6 EVCS based on solar energy in Gazipur, Dhaka, Sylhet and

Chittagong. DPDC also established 21 kW solar EVCS in Keranigonj, Dhaka. These are the

positive signs and hope for the charging stations in Bangladesh.

2.6.3 Power quality issues due to battery charging

Battery is the main component of an EV from where it takes the required power. The AC power

is converted into DC power using a converter/charger which is a non-linear load. This nonlinear

load affects the power system by producing various problems such as- harmonics, current and

voltage unbalance, voltage sag and swelling, flickers and phase shifting [46]. When EVs are

connected for charging their batteries in a network, it produces harmonics and voltage & current

fluctuation. Thus the power quality falls and it’s a hindrance of EV penetration in Bangladesh

[47].

2.6.4 Battery price and capacity

There are several types of battery lead-acid, lithium ion, Ni-Cd, Zn/air, Ni-Zn, Ni-MH, Na/S

batteries. In Bangladesh, lead acid batteries are popular due to its low cost. Although lead acid

batteries have a number of drawbacks such as- it cannot discharge more than 20% of the rated

capacity, low power density, heavy weight, lower life cycle etc. However, lithium ion batteries

are advantageous over lead acid batteries such as- high power density, long life time, good

performance at higher temperature etc.[48]. Lithium ion batteries have few disadvantages like

high cost, recharging takes large time. Ni-Cd battery has long battery life, fully dischargeable,

recyclable but it’s costlier in case electric vehicles. Another problem of using Ni-Cd battery, it

17

pollutes the environment when not disposed properly. NiMH batteries have double energy

density than lead acid battery.

2.6.5 High charging cost and time

EV adoption depends on another great factors that are cost & hours of charging. In Bangladesh,

everyday $1.5 to $1.875 is required for full charging of electric auto-rickshaw/easy bike. There

are also different tariffs for EV charging which is increasing every year. In addition, this

charging takes 6 to 8 hours daily. Thus, it’s a big problem that deals with the shrinkage of the EV

adoption.

2.6.6 Battery life time, maintenance and technology/material used

Mass adoption of EV depends upon the battery life time, costing and maintenance. These

batteries are very much costly and life time is not so long. Thus the EV owner has to change it

periodically. EV adoption can be increased if the battery technology & their performances are

improved [49]. Although some of the batteries can recover their performance by maintenance.

2.6.7 Low EV speed and Range

Most of the electric vehicle running in Bangladesh has a problem regarding speed. People are

eagerly waiting for the techno logy which can solve these problems. EVs in Bangladesh have a

speed on average of 20 km/hour. This low speed deteriorates the chance of mass EV deployment

in Bangladesh. The range problems in Electric Vehicle discourage it to use. Almost, all Electric

Vehicles have range on average 60-80 km on full charging. Thus, after traveling this distance it

requires to recharge the batteries. So, long distance traveling would be hampered using these

EVs.

2.6.8 Frequent accident and quality of road

Now-a-days, it is seen everywhere in Bangladesh that due to EVs road accident happens

frequently. As there is no separate road for these vehicles exists and the EV drivers are not much

experienced in driving, road accident happening every day.

EV requires smooth and healthy road for economic transportation. As it is sure that maximum

portion of the EVs are used in rural areas where road transportation system are not healthy with a

18

lot of disturbances. Thus, mass EV penetration would require high quality road for safe and

smooth running.

2.6.9 Lack of government support

According to the government of Bangladesh, using of EV is prohibited and government

discourages to use these EVs. As these EV consume a huge amount of electricity, it is a burden

on the power sector. EV has no clear database and registration procedure. However in recent

observation, there are number of EV charging station running in different corners of Bangladesh

on behalf of the power sector. Thus the lack of government support, it is difficult to deploy EVs

in a higher penetration [50].

2.6.10 Non-licensed vehicle

There are no rules for registering EV in BRTA. But there should be a legal framework for

promoting sustainable environment through energy efficient method of EV use. The rules and

regulations for EV licensing should be designed by technical and some other means as like

motorized vehicle. As the electric vehicle is unlicensed vehicle as per government indication, it

acts as an obstacle for EV penetration in Bangladesh [51].

2.7 Impact Assessment of Electric Vehicle Charging Station

As the EV load increases rapidly, thus the impact of EVs should be analyzed. The impact of

mass EV penetration on power system is expressed in Fig. 2.7 below. Although EV penetration

has cheapest transportation system, lower GHG emission facility, smart grid facilities. But

negative impacts on power system network are very much significant.

Power quality is the ability of a power grid network to supply a sustainable and clean power

supply with sinusoidal wave shape, noise free within the standard limit of voltage & current

harmonics. Harmonics, voltage sag/swelling are the common problems related to power quality.

EV chargers are the components that causing these problems when connected with grid. The EV

adoption in Bangladesh, is not only provides its negative impacts but also have some positive

impacts. Here in this thesis, I have discussed only the negative impacts of EV penetration on

power system.

19

Fig. 2.7: Impacts of Electric Vehicle.

2.7.1 Modeling of EVCS parameters

EVCS consists of the following parts basically- transformer, rectifier and converter etc. Fig. 2.8

shows a block diagram of an EVCS which comprises transformer, rectifier and converter.

Basically, rectifier and converter make a charger which used for EV charging.

Fig. 2.8: Block diagram of an Electric Vehicle Charging Station.

As a non-linear load, EV charger produces harmonics, low voltage profile and power loss in

distribution transformer. In Bangladesh, for EV charging level 2 type AC charging scheme is

used where maximum current rating is 16 A and maximum power rating is 3.3 kW. Most of the

EVs have power ranges from 0.5 kW to 1 kW and all of them use single phase 240 V, 50 Hz

20

supply system. In this section, mathematical modeling is developed for harmonics, voltage

profile and transformer overloading due to EV charging.

A. Power demand

Electric Vehicle battery takes charge from the power distribution system. The increased power

demand affects the stability of the system due to non-linearity. The power demand by an EV can

be expressed as

D

BattEV

T

SOCSOCCP

)(* minmax (2.1)

where CBatt is the battery capacity, TD is the duration of charging. Battery SOC is a factor

whether the EV takes high or small power. The gross power demand of the EVs is the

summation of individual power demand of all EVs which likely signifies as

N

N

EVGross PP1

(2.2)

B. Harmonics

The rise in high frequency components of voltage and current with compared to fundamental

frequency is defined as harmonics. Harmonics distorts the voltage & current waveforms and

thereby affecting power quality. It can be measured by total harmonic distortion (THD) of

current & voltage.

%1001

2

2

I

I

THD

N

n

n

i (2.3)

%1001

2

2

V

V

THD

N

n

n

v (2.4)

Equation (3) & (4) express the Total Harmonic Distortion (THD) for current and voltage

respectively. For slow charging THDi, THDv will be less than the fast charging. Thus, the EV

with low SOC will have a great chance to produce harmonics.

21

C. Voltage profile

The low voltage profile becomes a threatening issue induced by EV charging. Voltage stability

refers to the ability that the power network being stable after the sudden increase or decrease in

the loads. EV loads take large amount of power at a very short duration. Thus, voltage profile

will be degraded and grid will be unstable.

D. Transformer performance

Mass deployment of EVs creates an additional stress on distribution transformers and their life

cycles. Another problem is that, the EV charging rate should be limited per day and charging

stations should keep far away from transformer for reducing power loss. Harmonic current is

responsible for occurring load losses in transformer whereas harmonic voltage incurs no load

loss. Due to these harmonic losses, heating is increased relative to the pure sinusoidal wave. This

harmonic withstand capability can be measured by a factor called k- factor. The equ. expresses

K-factor as

2

1

2 ][R

nN

n I

InfactorK

, (2.5)

where In is the current related to nth harmonic and IR is the rated load current. The presence of

harmonics causes overheating in the transformer. Thus, the transformer should be selected

according to the withstand capability at higher harmonic current for non-linear loading [52].

2.7.2. MATLAB SIMULINK model of EVCS

When EVs are connected to the utility grid for recharging the batteries, it would hamper the

power quality. In this paper, the impacts of EVCS on utility grid are analyzed using MATLAB

SIMULINK model shown in Fig. 2.9. In this modeling, the three phase source is used as utility

grid and the battery ratings are taken as the EVs running in Bangladesh. Charger consist mainly a

rectifier and a DC-DC converter circuit. Switched Mode Power Supply (SMPS) based battery

charger provides constant voltage constant current for charging batteries.

22

Fig. 2.9: MATLAB SIMULINK model of EVCS.

A. Battery Charger model

Typical switched model power supply or battery charger incorporates a front-end AC to DC

rectifier for producing unregulated DC voltage. A high frequency chopper (IGBT/ MOSFET)

then chops the input DC voltage according to the duty cycle. After that, a high frequency

transformer isolates, step down and converts square wave DC to square wave AC output. It is

then rectified and filtered to produce ripple free smooth DC output voltage. Pulse width

modulation technique is generally employed in this conversion. Fig. 2.10 shows the Switching

mode power supply based battery charger model of designed in MATLAB SIMULINK.

The lead acid battery is used in almost all the EVs in Bangladesh. These are 48/60 V. Each

battery unit consist 4 or 5 battery where each battery rating is 12 V and 20 Ah.

23

Fig. 2.10: MATLAB SIMULINK Model for EV Battery Charger.

Battery discharge characteristic is shown in Fig. 2.11. When the battery discharges the maximum

ampere-hour, the value of voltage reached to zero. It means SOC of the battery going down. Also

for different current rating the discharge phenomenon of the battery is shown in Fig. 2.11.

Fig. 2.11: Battery discharge characteristics.

24

2.7.3 Impact Analysis

Growing popularity of Electric Vehicles due to several positive impacts is admirable but its

detrimental impacts on the grid power quality cannot be neglected. To analyze the impacts, in

this research MATLAB based SIMULINK model for EVCS connected with utility grid is

demonstrated.

A. Increased power demand

The increased electricity demand causes by electric vehicles add an extra pressure to the grid.

The daily load curve for Bangladesh power sector is given in Fig. 2.6. The charging profile for

EVs in a charging station is shown in Fig. 2.12. This graph indicates that, at the time of peak

hour, the demand of EV charging increases. Thus, for mass penetration of EV leads to the huge

demand during peak hour in all corners of Bangladesh.

Fig. 2.12.: Charging profile of an EVCS located in Gazipur district, Bangladesh

The increased power demand can be a cause of load-shedding and also hampers the power

quality. If the EV charging is scheduled and maintain strictly at peak and off-peak period, then

the problem arises with power demand will be minimized.

0

2

4

6

8

10

12

14

6 A.M. 8 A.M. 10 A.M.12 A.M. 2 P.M. 4 P.M. 6 P.M. 8 P.M. 10 P.M. 12 P.M. 2 A.M. 4 A.M.

Load

in

kW

Hour

25

B. Harmonics disturbance

Harmonics are the disturbances of a power system. EV charger is non-linear load and when it

connected in the power system then it generates harmonics. As the EV charger normally

connected at the power distribution network for charging, the aggregated effects of harmonics

can be threat for the whole power system.

(a)

(b)

(c)

Fig. 2.13 (a),(b) & (c): Harmonics, when single EV (a), 3 EV (b) and 5 EV (c) is connected at a

charging station.

26

According to the IEEE Standard 519-1992 Harmonics level, the value of THD should not exceed

5% for ensuring power quality. In this analysis, it is found that, the harmonics level is greater

than the accepted level for power quality. In the MATLAB Simulink modeling, the harmonics

generated at the different ratio of EV charging is shown in Fig. 2.13 (a), (b) & (c). The THD

value is different because in this experiment, I have used different categories of battery with

different rating.

C. Voltage Disturbances

Voltage at the distribution end also reduces when multiple EV chargers are connected. The

overloading due to large number of EVs causes this problem. The voltage profile variation

before connecting EV charger and after connecting EV charger is shown in Fig. 2.14 (a) & (b).

Fig. 2.14 (b) shows that, the voltage is affected by harmonics disturbance compared to the

voltage without connection of EV chargers in Fig. 2.14 (a). In the Fig. 2.14 (b), it is seen that

voltage fluctuation occurs with harmonic disturbances.

Fig. 2.14 (a): Input Voltage, before connecting charger

27

Fig. 2.14 (b): Input Voltage, after connecting charger

D. Transformer power loss

Clustered EV charging can be a cause of transformer overloading and thereby increasing the

power loss. The overloading scenario of a distribution transformer obtained from MATLAB

simulation with different EV load is shown in Table 2.3.

Table 2.3: Transformer output at different EV load

The transformer power loss due to harmonic effects can be minimized by selecting transformer

with higher k-factor. More the EVs connected with the distribution transformer, the losses will

be more and thereby the efficiency of the power system decreased.

Output kVA under Rated current Output kVA under Harmonic current

200 191.80 (1 EV)

200 188.75 (3 EV)

200 185.45 (5 EV)

28

2.8 Policies Recommended for EV adoption

Although electric vehicle has a huge demand of electricity and most of the times power sector

falls into a problem with shortcomings of profit. In other cases, the electric vehicle has several

positive impacts on environment, fossil fuel reduction, improved socio-economic status of EV

owner and decreases the unemployment. To become sustainable in power sector, it is very urgent

to increase the EV penetration in Bangladesh. Analyzing challenges of EV adoption, authors

suggest few recommendations for increasing it. The recommendations are given below.

i. At first, EV i.e. Auto-rickshaw, Electric bike, Electric bi-cycle, Electric tri-cycle needs

registration in the national website. When it is completed then the total number of charging

station required in different corners of Bangladesh can be calculated easily. Charging rate of

EV should be selected according to the energy consumption.

ii. As an environment friendly vehicle and cheapest mode of transportation, government should

prioritize this vehicle. It can be done by applying no tax on accessories of the EVs,

establishing more charging infrastructures. Locations of charging stations need to be

situated at a suitable place where transportation is easy & known for all. Also charging

stations should be far away from distribution transformers for avoiding power loss &

distortion.

iii. As the maximum number of EV chargers are connected at grid simultaneously, it affects the

power quality issues by producing harmonics, voltage fluctuation etc. Coordinate charging

scheme can be helpful for charging EV to reduce power quality problems.

iv. To cut pressure on the national grid via EV charging, the huge potentiality of renewable

resources such as- solar, biogas and wind should be utilized. Few Solar based Charging

stations are already established in different corners of Bangladesh by the government. These

charging stations are designed to charge 20 to 30 EVs per day. The Government has set a

tariff $0.5 to $0.625 for recharging Easy bike and Electric rickshaw respectively. As the

solar can provide power only at day time and is absent in cloudy & foggy days, thus hybrid

29

system (such as solar, biogas or solar, wind) is mandatory for sustainable production of

electricity for EV charging [53].

v. Vehicle to Grid (V2G) technology is required to EV management for proper utilization of

renewable resources and also for improving efficiency. Thus excess energy will be used

effectively.

vi. The EV owner should use updated battery technology. Also, battery management system

should be used to monitor SOC when charging for avoiding overcharge. In Bangladesh,

maximum EV owner uses lead-acid batteries. However, lithium ion batteries are more

efficient and have higher life cycle than lead acid batteries. In addition, lead acid batteries

are more vulnerable to the environment due to disposal of lead. Thus, it is necessary to use

lithium ion batteries instead of lead acid batteries.

vii. The used batteries of EVs can be used for backup and load leveling purposes. The used

batteries when no longer usable in EVs, their residual capacity still has significant value.

During off-peak hour, the excess electricity generation can be stored using these batteries. In

addition, the EV owner earns some extra money by selling these batteries. The battery

recycling policy developed by the government will help environmentally and financially.

viii. Grid based charging station produces more CO2 emission than charging station based on

renewable energy [54]. Thus, the in case of considering lower GHG emission by EVCS

renewable based charging station is necessary for increasing sustainable EV adoption.

ix. Training facilities should be provided by the government institution to the EV driver for

better performance and safe driving management. For this purpose, BRTA can arrange

training & workshop facilities.

30

x. In case of highway, there should be separate lane for electric vehicles to decreasing frequent

road accident. Also, the quality of the road especially for rural areas should be improved.

Due to awkward, low quality & damaged road causes road accident and more energy

consumption for EVs.

xi. The government should encourage establishing more research center on these transportation

vehicle for improving technology. Such type of research center can help to extend range of

EVs as well as enhancing battery capacity.

xii. Finally, awareness should be grown up by publishing the environmental benefits of using

EVs can help more adoption in Bangladesh.

2.8 Summary

As the EVs acceptance is growing in rapid manner and it induces several disturbances to the

power quality, thus it is an important issue now-a-days. The government and respective planning

section is very much worried about this EV adoption. In this scientific era, this type of problems

should be overcome by technical advancement. In this chapter, the different model for analyzing

challenges are developed and found that, numerous factors are behind for this EV adoption. Main

problems are found by such analysis as: no governmental policy, high charging cost, lack of

charging infrastructures, high charging time and less investment to this sector makes EV

adoption difficult to the user.

The increased power demand due to the EV charging makes power sector to be more lagging

beyond the power generation. As the load shedding occurs basically at evening to mid night

period (peak hour) and maximum EV is gone to the charging station at that time. EVs battery can

be able to supply power up to 8-10 hours and their range is also limited i.e. 70-100 km. So, if an

EV starts running from morning hour, it will go for next charging at evening hour. Thus, the

scheduled charging especially at off peak hour is required for EVs. It will improve the

performance as well as reduce the demand at peak hour.

31

Mathematical modeling for EVCS is also developed in this chapter which demonstrates the

significance of the EVCS parameters. The MATLAB Simulink model showed that, the power

quality factors i.e. harmonics, voltage fluctuation (sag/swelling) and transformer power loss due

to EV charging is a threatening issue. This simulation shows that, the integration of one EV

charger creates THD = 4.82%, for 3 EV charger, THD = 12.35% and for 5 EV chargers, THD =

19.69%. In addition, if the number of EV chargers is connected to the utility grid, it produces

voltage fluctuations with significant power loss in distribution transformer which is given in this

chapter.

32

Chapter 3

Feasibility Assessment of Hybrid Renewable Energy

Based EVCS

3.1 Introduction

Electric Vehicle Charging Station (EVCS) basically connected with the utility grid for battery

charging purposes. However due to sharp increasing in the EVs worldwide, it becomes a threat

to the power sector. On the other hand, Bangladesh has a great potential of renewable resources

like solar, biogas, wind etc. As the wind energy resources are not available throughout the

country, in this research only solar and biogas based hybrid generation is taken to analyze. In this

chapter, the prospect of hybrid power generation through solar & biogas/biomass resources is

analyzed. Then, the feasibility assessment of the hybrid renewable energy based EVCS is

performed according to the technological, financial and environmental aspects using HOMER

Pro software. The comparison of the results obtained from HOMER Pro software and

mathematical analysis are shown in this chapter. In addition, socio-economic aspects of this type

of proposed EVCS is demonstrated. Finally MATLAB Simulink based PV and biogas based

Electricity generation scheme is formed and described.

3.2 Potential of Renewable Resources in Bangladesh

In this research, two types of renewable energy resources are proposed for the purpose of

electricity generation, i.e., solar and biogas.

3.2.1 Solar Energy Potential in Bangladesh

Bangladesh has a geographical position from 20°34′N to 26°38′N and from 88°01′E to 92°41′E.

Variation of the solar insolation ranges from 4 to 6.5 kWh/(m2·day) and the average insolation is

5 kWh/(m2·day). Consequently, during winter and summer time, the average temperatures are 20

°C and 27.75 °C, respectively. In this research, the NASA monthly average global radiation chart

has been used to estimate the solar system capability. The solar radiation is maximum during the

months of March–April and minimum during December–January. A study on the daily sunlight

33

hours in Bangladesh suggested that 7–10 h of daily solar radiation is available in Bangladesh but

due to rainfall, cloudy and foggy environments, this is reduced further by 54% and finally it is

4.6 h daily [55].

Fig. 3.1 shows that Rajshahi is identified as the highest solar intensity region vis-a-vis Sylhet

being the lowest. Due to having enough resources, solar energy is proposed in this research to

establish a solar powered electric vehicle charging station that is a crying need to meet the

increasing demand for electricity.

Fig. 3.1: Solar irradiation in different cities of Bangladesh [55].

In Bangladesh, 40% of the rural population has no access to electricity. To provide electricity by

using renewable solar energy to households, the government introduced a scheme called Solar

Home System (SHS). The Govt. of Bangladesh is working towards ensuring 100% electricity

access by 2021 with the SHS program. A solar power plant of 15 MW is already installed in

Bangladesh and the government is committed to producing 10% of the total power generation

from renewable energy by 2021 [56].

3.2.2 Potential of Biogas Energy Resources in Bangladesh

Biogas refers to a renewable energy source consisting of gas produced by the biological

breakdown of organic biodegradable materials in the absence of oxygen. Suitable raw materials

include biomass, human waste, animal waste, poultry droppings, Municipal Solid Waste (MSW),

green waste, plant materials, crops, etc.

It typically consists of methane (CH4: 50%–75%), carbon dioxide (CO2: 25%–50%), nitrogen

(N2: 0%–10%), hydrogen (H2: 0%–1%) and hydrogen sulfide (H2S: 0%–3%). It may contain

34

small amounts of oxygen (O2: 0%–0.5%) and moisture, etc. The gases contained in biogas like

methane, hydrogen, and carbon monoxide (CO) can be further combusted or oxidized with

oxygen in a combustion chamber. The released energy allows the use of biogas as a fuel. This

biogas can be used for some heating applications as well as for electricity generation. In

Bangladesh, there are three main sources of biogas: poultry waste from the poultry industry,

municipal solid waste and animal waste.

A. Poultry Waste

According to the Bangladesh Division of Livestock Statistics annual report on livestock for the

fiscal year 2015–2016, there are more than 53,000 poultry farms in different regions of the

country. The number of poultry farms is increasing rapidly due to their perceived profitability.

They serve the purpose of egg and meat production and also produce fertilizer and fish feed. The

wastes from poultry can be a good source of bio-energy.

A study conducted by the U.N. Food and Agriculture Organization (FAO) in 2013 suggested that

in Bangladesh there are approximately 245 million chickens and 46 million ducks which produce

12.9 million tons of waste per day [57]. This poultry waste could be collected and converted into

biogas. The extracts from the biogas plants could be an additional source of fertilizer and fish

feed.

B. Animal Waste

Animal waste in the form of cattle or buffalo dung can be used as a source of biogas. According

to a study conducted by the Bangladesh Livestock Department, there are 24 million cows and 1.5

million buffalos which produce 102.6 million tons of waste per day [58]. After collecting this

waste it can be utilized as a biogas source and extracts from the plant can be used as a good

fertilizer.

C. Municipal Solid Waste

Municipal solid waste (MSW) is also referred to as trash or garbage, refuse or rubbish. These are

the everyday items discarded by the public after use. MSW contains mainly food waste, paper,

cardboards, textiles, plastics, glass/metal/ceramics, etc. The MSW produced in Bangladesh are

classified as given below in Fig. 3.2.

Per capita waste generation in a day is about 0.5 kg. In Dhaka city, the daily waste production

capacity is 4200 tons. Major six cities produce waste per day is equal to 7890 tons. In addition,

35

the rate of waste generation is increasing rapidly. Thus, the extrapolation result shows that, it will

be 47,064 tons per day by 2025 which is shown in Table 3.1.

Fig. 3.2: Types of MSW in Bangladesh [59]

Table 3.1: MSW generation scenarios of urban cities in Bangladesh [59]

Year Urban

Population

% of total

Population

Waste

Generation Rate

Total Waste

Generation/Day (Ton)

1991 20,872,204 20.15 0.49 9873.5

2001 28,808,477 23.39 0.5 11,695

2004 32,765,152 25.08 0.5 16,382

2015 54,983,919 34.20 0.5 27,492

2025 (Projected) 78,440,000 40.00 0.6 47,064

These wastes can be used as a source of biogas to generate electricity. Firstly, waste is collected

from different areas, next these wastes are fed to a combustion chamber and burnt. This burning

produces steam which turns a turbine to generate electricity. These wastes have high potential as

MSW

Domestic waste

Industrial

waste

Medical waste

Biodegradable

(Food waste)

Non-Biodegradable

(Polythene, plastics)

Hazardous

(Benzene)

Non-

hazardous

(Fuel oil)

Hazardous

(Blood/syringe)

36

an important source for recovering energy and meeting the electricity demand. Table 3.2 shows

the maximum amount of electricity generation from different types of bio-waste.

Table 3.2: Bio-waste to electricity conversion

Waste

Type

Waste produced

per day (kg)

Biogas produced

per day (m3)

* Maximum electricity

production per day (kW)

Poultry 12,900,000 954,600 1344.50

Cow dung 102,600,000 3,488,400 4913.24

MSW 27,492,000 2,089,392 2942.81

* Maximum amount of electricity generation.

kW

WE

B

BWP

. , (3.1)

where W, BW and BkW stands for total waste in kg, biogas production per kg of waste and biogas

required for 1 kW electricity generation, respectively. It is assumed that for poultry waste &

MSW biogas production per kg of waste is 0.074 m3 and for cow dung biogas produced per kg is

0.034 m3. It is also assumed that biogas required for 1 kW electricity generation is 0.71 m

3. Fig.

3.3 shows the percentage share of maximum electricity generation in Bangladesh from biogas

resources like MSW, poultry and cow dung.

Fig. 3.3: Maximum electricity generation from biogas/biomass resources.

Poultry 15%

Animal waste 53%

MSW 32%

37

3.3 Mathematical Modeling

This section describes modeling of technical, economic and environmental parameters related to

the Electric Vehicle Charging Station.

3.3.1 Mathematical Model of Technical Parameters

A. Power demand by an Electric Vehicle

Power consumption of an EV depends on the distance travelled, battery capacity and the mode of

driving. An electric vehicle consumes power which can be calculated as below

d kD

K EP

T

, (3.2)

where, Kd is the number of kilometers driven, Ek is the energy required per kilometer and T is the

time required to charge the vehicle battery. T is the difference between the departure and arrival

time of the EV at a charging station. T depends on the SOC of the vehicle’s battery. Power

demand of an electric vehicle can be represented by using battery capacity, SOC and its charging

time [43]:

max(SOC SOC)batD

QP

T

, (3.3)

where Qbat indicates the battery capacity, SOCmax is the upper limit of the battery SOC and T is

charging duration. Therefore, power demanded by the Nth

electric vehicle will be:

1

N

D

i

P P

(3.4)

B. PV Array Output

The PV array output is determined by equ. (3.5):

PVP SI A , (3.5)

where SI is the average annual solar insolation in kWh/m2, η is the PV module efficiency and A

is the surface area of the proposed solar PV system [33]. If the temperature of the PV cells

increases, the output power drops. This is taken into consideration by the use of a temperature

de-rating factor, ηt for calculating PV system efficiency.

38

1 [ ( )]t C ST T , (3.6)

( )[1 ( )]CPV t C S

S

GP W T T

G , (3.7)

where W is the rated capacity of the PV panel in kW, β is the temperature coefficient (%/°C), GC

is the solar insolation in practical conditions, GS is the solar insolation at Standard testing

conditions in kW/m2. TC and TS are the temperature of the PV cell at current conditions and

standard conditions respectively in °C [33].

C. Digester Size and Biogas Production

Digester size is an important factor for biogas plants. The volume of the digester can be

determined by as

d i RV S T , (3.8)

where Vd is the digester volume, Si is the daily substrate input and TR is the retention time in

days. Retention time depends upon the digesting temperature. It will be at least 40 days for a

simple biogas plant but in practical scenarios it can be 60–80 days. An extra-long retention time

will increase the gas yield by 40%. The substrate input depends on how much water has to be

added to the substrate in order to arrive at a solids content of 4–8%.

iS B W , (3.9)

where B and W stand for biomass and amount of water added, respectively. The mixing ratio for

animal waste/poultry droppings and water amounts is between 1:3 and 2:1 for a biogas plant.

Biogas generation per day, G (m3 /day), is calculated on the basis of the specific gas yield, Gy of

the substrate and the daily substrate input Si. The biogas production, G in m3 /day can be

calculated as:

G = VS × Gy (solid); VS is the volatile solid content

G = B × Gy (moist mass); B is the weight of the moisture mass

G = Number of LSU × Gy (species); LSU is the standard gas yield values per livestock unit.

Biogas yield from a digester depends on the temperature and retention time. Thus, it can be

represented by using the equ. (3.10) given below:

39

( , ) ( , )y R RG T T m f T T , (3.10)

where m = mass of the volatile solids content, f(T, TR) = Function of digester temperature T and

retention time TR [60].

The ratio of the daily total solid input or volatile solid input, Si and the digester volume, Vd is

called as digester loading, LD.

d

iD

V

SL ; (3.11)

D. Gas Storage Design

Gas storage is used in the biogas plant to hold the biogas. Its size depends on the rate of

generation and consumption. It must be designed as so that it can cover the maximum

consumption rate (>Vg1) and can hold the gas for the longest period with zero consumption

(>Vg2). For safety margin, 15% should be added to the original size as indicated in equ. (3.12)

given below [60].

),max(15.121 ggg VVV , (3.12)

The ratio of the volume of digester and gasholder is important for designing a biogas plant.

Typically it is 3:1 and 10:1.

E. Biogas Generator Output

The total power generated by the biogas system consists of Biogas generator 1, Biogas generator

2 and Biogas generator 3:

Bio poultry Cowdung MSWP P P P 0.71

PW CD MSWG G G , (3.13)

where GPW, GCD and GMSW are the amount of biogas produced from the poultry waste, cow dung

and MSW, respectively.

F. Battery Modeling

In this system, two types of batteries are used - one is the lead acid battery for storing the surplus

energy generated by the system and another one is the vehicle battery. Charging time and

charging rate both depend on the state of charge (SOC) of the battery. The charge level of the

vehicle battery can be described by two ways. One is SOC and another is Depth of Discharge

(DOD). If a battery is fully charged, it is called 100% SOC. when it is fully discharged, it is said

that the cell has 100% DOD. For maximum battery life cycle, the 100% DOD situation must be

40

avoided. In this model, the SOC is taken as 20% and maximum DOD is assumed as 80%. The

battery energy can be calculated by using equ. (3.14):

0

SOC d

t

Battery bat batQ V I t , (3.14)

where SOC is the initial charge of the battery [20].

State of charge (SOC) of a battery:

SOC

,max

100%bat

bat

QB

Q , (3.15)

The initial SOC of the battery can be considered as a random variable depending on the distance

travelled by an EV is as -

2

2

(ln )

2

d

1( ; ; ) .e

2Π

d

d

d

d d

d

f dd

, (3.16)

where d (distance travelled)> 0, μd is the mean distance and σd is the standard deviation of the

random variable [61]. The battery initial SOC can be expressed by using distance travelled by the

EV and its maximum travelling distance range:

max

1d

Ed

; 0 < d < dmax (3.17)

G. Converter Modeling

A bi-directional converter is used to convert from DC power into AC and vice-versa. In this

proposed model, only PV output DC power is converted to AC power by the converter. The

output power of the converter is given by:

AC Conv DCP P . (3.18)

The converter efficiency taken into consideration in the proposed system is 97%.

3.3. 2 Mathematical Model of the Economic Parameters

Mathematical modeling of economic parameters such as cost of energy, net present value,

internal rate of return, expected payback period and profitability index is used in this research.

41

A. Cost of Energy (COE)

It is the average cost per kWh electricity production in a system. The ratio of annual cost of

energy production to the total energy production/year gives the value of COE. HOMER uses the

following equation for calculating COE:

COE Annual

Served

C

E

(( ) / (1 ) )NPVCR n

TL Sold

F i r r T

E E

, (3.19)

where FCR is the capital recovery factor, in is the nominal interest rate, r is the annual inflation

rate, T is the project life time, NPV is the net present value, ETL is the total load served

(kWh/year) and ESold is the energy sold to the grid (kWh/year) [33]. Levelized Cost of Energy

(LCOE) depends on several factors such as plant size, capital cost, the solar radiation and

biomass collection, lifetime, O&M cost, capital recovery factor and degradation of the modules

used, etc.

B. Net Present Value

This is a measurement of profit determined by subtracting the present values of cash outflows

(including initial cost) from the present values of cash inflows over a period of time. The net

present value can be calculated as-

0

Nt

1tt

t CC

NPV

r)(1

, (3.20)

where Ct is the net cash inflow during the period t, C0 is the initial investment or capital cost, r is

the discount rate and t is the time period in years [34].

C. Internal Rate of Return (IRR)

It is a discount rate that makes the net present value (NPV) of all cash flows from a particular

project equal to zero. When the NPV is equal to zero, then the IRR will be determined by using

the formula:

1 2 3 40 1 2 3 4

NPV 0 ......(1 ) (1 ) (1 ) (1 ) (1 )

t t t t ti

T

C C C C CC

r r r r r

, (3.21)

where value of ‘r’ will be equal to the IRR.

42

D. Expected Payback Period

Payback period is an important parameter for project selection. It is the time after the total

project cost will be vanished by project cash inflows. It indicates that the project will be

profitable after this period.

& Re .

inf

EBPBCap O M pl

Cash low

C C C

C

, (3.22)

where CCap ,CO&M and CRepl, Ccashinflow are the capital cost, O&M cost, replacement cost and

annual cash inflows respectively. It should always less than the project lifetime, T for a feasible

and profitable project.

E. Profitability Index (PI)

The profitability index plays a vital role for a project to determine if one should proceed with an

investment or not. The profitability index rule states that if the profitability index or ratio is

greater than 1, the project is profitable and may receive the green signal to proceed. In contrast, if

the profitability ratio or index is below, the project should be rejected:

inf

. & Re .

PI Cash low

Cap O M pl

T C

C C C

, (3.23)

where T = lifetime of a project in years.

PIEPBP

T , (3.24)

If EPBP < T the project is feasible. If EPBP ≥ T, then the project will be a failure and thus it

should not proceed.

3.3.3 Modeling of the Environmental Parameters

Environmental feasibility is assessed by calculating the total GHG gases emitted from a power

plant. These gases are CO2, SO2, NO and other pollutants, which increase environmental

pollution endangering human life and putting wildlife at risk. In this paper, CO2 emissions from

the hybrid renewable energy-based EVCS are calculated. Emission factor and total CO2

emissions from the charging station are the important parameters for this feasibility test.

43

A. Emission Factor

It represents a value that attempts to relate the quantity of pollutants (i.e., GHG gases) released to

the environment per unit generation of power. It is usually expressed in kg/kWh:

Emission, (1 )RE P EF , (3.25)

where P is the total generation capacity, EF is the emission factor, ηR is the overall pollutant

reduction efficiency of the system [62].

B. Life Cycle Emission Factor

i &GWP ( )

LCEFi Ci Oi Di B C

i

Net

E E E E E

P

, (3.26)

where i is the type of GHG; GWP is the global warming potential factor for each GHG; EF =

direct emissions caused by the combustion of fossil fuels; EC = emissions regarding construction

of the plant; EO = emissions at O&M works; ED = emissions caused by decommissioning the

plant; EB&C = emissions caused by the battery storage and charging apparatus; PNet = net output

of electricity during the lifetime of the system.

Total CO2 emission in a renewable energy based power plant can be calculated as-

.&..,2,2,,222 , recyclingconstBattBattBatttdthermal

d t

i

i

Total COCapCOEdCOCapiCOCO

(3.27)

Here, dCO2 is the direct emissions caused by fuel combustion; iCO2 is the annualized indirect

emissions from the system; Capi is the capacity of the plant, Ethermal is the energy produced at

time t of day d; CO2 Batt is the CO2 emissions produced by the battery; CapBatt is the capacity of

the battery and CO2, Batt is the CO2 generated by the battery construction & recycling processes

[63]. In a hybrid renewable energy-based EVCS, CO2 generation per kWh energy should be less

than the CO2 generation from the conventional power generating system. Otherwise,

environmental feasibility result will be negative and the proposed model fail or be rejected.

The batteries normally used in the EVs are lead acid, lithium ion type. Although these batteries

do not responsible for emit GHG emission but the manufacturing/ recycling process of these

44

batteries contribute to the GHG emission a lot. That means as the more EVs are penetrated, the

GHG emissions are more.

Another point of consideration is that the extracts generated from the biogas system should be

utilized properly. They can be in the form of solid ash or fly ash. Proper use of these extracts can

reduce the pollution regarding waste generated from the hybrid system.

3.4 System Component

The major components for the hybrid renewable energy powered EVCS are the solar

photovoltaic (PV) module, digester, biogas generator, converter, battery, hybrid charge controller

and electric vehicle charging apparatus. For our technical, economic and environmental

feasibility study by using HOMER, it is necessary to know the number of units to be used,

capital cost, O&M cost, replacement cost, lifetime, operating hours, biomass and solar resources.

3.4.1 PV Module

A solar PV module consists of a number of solar cells connected in series and parallel. Four

types of solar cells: mono-crystalline, poly-crystalline, thin film and amorphous silicon are

available nowadays. In this project, mono-crystalline solar cells are used due to their higher

efficiency (14%–20%) and the fact they require less space. Electrical parameters are taken from

the standard test conditions at air mass AM 1.5, irradiance 1000 W/m2, cell temperature 25 °C.

The specifications of solar PV module are given in Table 3.3.

Table 3.3: Specifications of the PV module (Canadian Solar Dymond CS6K-285M-FG)

Characteristics Value

Maximum Power (Pmax) 275 W

Voltage at Maximum Power (Vmp) 31.3 V

Current at Maximum Power (Imp) 8.8 A

Open Circuit Voltage (Voc) 38.3 V

Short Circuit Current (Isc) 9.31 A

Panel Efficiency (ηp) 16.72%

Power Tolerance +2%

Dimension of the Panel 1658 × 992 × 5.8 mm

Operating Temperature Range −45 °C–85 °C

45

At 1000 W/m2 irradiance, solar module can produce = Efficiency × Irradiance × Panel Area =

0.1672 × 1000 × (1.658 × 0.992) = 275 W. The number of modules required for a 10 kW system

= 37.

Table 3.4 shows different types of cost and ratings of the PV panels.

Table 3.4: Cost of the PV panels

Capital Cost $10,000

Replacement Cost $5000

O&M Cost/Year $1.00

Life Time 25 years

De-Rating Factor 80%

3.4.2 Digester

The digester is the main component for biogas production. The animal waste is supplied to the

digester with proper mixing of water. It will only start producing biogas after effective

combustion. The size of the digester depends on the retention time and substrate input of the

daily waste supply. The amount of substrate input is the sum of water and the biomass in cubic

meters. In this system, three digesters are required for different wastes. Ratings of the biogas

generator according to the input waste material cow dung, poultry waste and municipal solid

waste are 4 kW, 4 kW and 2 kW, respectively. Digester size and cost according to the data taken

from IDCOL is given in Table 3.5.

Table 3.5: Digester size and cost according to IDCOL

Type of

Biomass

Biomass

Required(kg)

Digester

Size(m3)

Operating

Hours/Day

Cost of the

Digester

Cow Dung 130 4.8

10–12 $650

Poultry Waste 68 4.8 10–12 $650

MSW 50 3.2 7–8 $537.5

3.4.3 Biogas Generator

In this research, three biogas generators are required for the power generation. Generators are

driven by the biogas from the digester where the average operating hours will be 8–10. The

46

O&M cost is minimized by selling fertilizer which comes from the digester after biogas

production. The rating of the biogas generator, different cost, life time is mentioned in Table 3.6

below.

Table 3.6: Cost and size of the biogas generator

Generator Rating Capital

Cost

Replacement

Cost

O&M

Cost/Year Lifetime

4 kW (Bio 1) $2000 $1000 $500 5 years

4 kW (Bio 2) $2000 $1000 $500 5 years

2 kW (Bio 3) $1000 $800 $100 5 years

3.4.4 Converter

A bidirectional converter is used for converting DC power into AC power and vice versa. It is

connected to the AC bus. In the proposed system, all the biogas generators AC output (4 + 4+ 2

= 10 kW) is connected to the AC bus. The parameters regarding selection of a 10 kW converter

for the proposed system is shown in Table 3.7.

Table 3.7: Technical parameters of the Suntree 10,000 TL 10 kW Converter

Parameter Value

Efficiency & Capacity 98% & 95%

Output Voltage Range & Current 360–440 V & 15.2 A

Max. Output Power 10 kW

MPPT Voltage Range 250–800 V

THD <3% (at nominal Pout)

Dimensions (W × L × H) 470 mm × 185 mm × 585 mm

Net Weight 35 kg

However, the PV array (10 kW) output is connected to the DC bus. Most electric vehicles are

charged by AC systems. Therefore, to convert the DC output from the PV array a 10 kW

converter is needed for an optimum solution. The capital, replacement and O&M cost are

assumed as $2000, $1000 and $2.00, respectively, and the lifetime is 20 years.

47

3.4.5 Battery

Energy from the proposed hybrid system is stored by using a battery bank or in the batteries used

in the electric vehicles. For charging purposes, the electric vehicle is connected with the AC bus

through a charger. In this research, a generic 1 kWh lead acid battery is used. The technical

parameters of a lead acid battery are shown in Table 3.8 below. The battery lifespan of an EV is

affected by different factors such a high temperature, dirty environment, low driving, defects and

driving habits, etc. The battery lifetime decreases with the increase in the operating temperature.

Table 3.8: Technical parameters of the 60, 038 MF-12 V, 100 Ah lead-acid Battery

Characteristics Value

Battery material Lead acid

Nominal voltage & capacity 12 V & 81–100 Ah

Total weight 24 kg

Dimensions 350 mm × 175 mm × 190 mm

Lifetime 5–8 years

3.4.6 Charging Apparatus for Electric Vehicles

In the proposed charging station, there will be several points to recharge the vehicle battery from

the AC voltage. The cost of the charger is assumed around $50 and the average durability of the

charger is 2–4 years. Table 3.9 shows the technical specifications of an EV charger.

Table 3.9: Technical specifications of an EV charger

Characteristics Value

Charging Voltages 48/60/72 V

Charging Current Range 3–8 A

Input Voltage 180–250 V AC

Battery Capacity 10–100 Ah

Maximum Charging Power 1000 W

Efficiency 98%

Weight 2.2 kg

Dimension 260 mm × 140 mm × 130 mm

48

3.5.1 Design of the Grid Connected Hybrid Renewable Energy Based EVCS

Figure 3.4 shows a grid connected hybrid powered Electric Vehicle Charging Station where the

PV panel produces 10 kW power whose maximum power point is tracked by a MPPT. Three

biogas generators run by biogas resources obtained from animal waste (cow dung), poultry waste

and municipal solid waste (MSW), respectively.

Fig. 3.4: Grid Connected Hybrid Renewable Energy Based EVCS

A charge controller regulates the voltage and current into the batteries. When the batteries are

charged fully then it stops charging and sends the excess power to the converter. A bidirectional

converter is used to convert the DC into AC. If the hybrid power is unavailable, the power comes

from the national grid, especially during the off peak period. Excess energy can also be sold to

the national grid. Electric vehicles are charged from the AC bus through a charging apparatus.

49

3.5.2 Design of EVCS using HOMER Pro Software

HOMER is a free software application developed by the National Renewable Energy Laboratory

in the United States. This software application is used to design and evaluate technically and

financially the options for off-grid and on-grid power systems for remote, stand-alone and

distributed generation applications. The HOMER (Hybrid Optimization of Multiple Energy

Resources) model greatly simplifies the task of designing hybrid renewable micro-grid, whether

remote or attached to a larger grid. HOMER's optimization and sensitivity analysis algorithms

allow you to evaluate the economic and technical feasibility of a large number of technology

options and to account for variations in technology costs, electric load, and energy resource

availability.

Figure 3.5 shows the block diagram of EVCS designed by HOMER Pro software. In this

demonstration, solar PV module CS6K-285M-FG (10 kW), Biogas generator Bio1, Bio 2, Bio 3

(10 kW), deferrable electrical load (88 kWh/day), electric load (11.26kWh/day), converter (10

kW) and battery storage red T15-75 is used. This model uses the solar irradiation and

temperature data from the NASA surface meteorology department.

Fig. 3.5: Block diagram of EVCS designed by HOMER Pro software.

50

The solar irradiation and temperature curve are given in Fig. 3.6 and Fig. 3.7 respectively. Fig.

3.7 shows the temperature varies from 200C to 35

0C in Bangladesh. The average solar irradiation

is assumed as 4.95 kWh/m2/day.

Fig. 3.6: Solar irradiation profile used in HOMER.

The temperature variation at different season is given in Fig. 3.7. Temperature affects the solar

output energy production. In addition, a research performed on biogas production showed that,

the 220C to 35

0C temperature acts as a catalyst of the biogas generation because it boosts up the

biogas generation. In Bangladesh, the usually temperature varies from 250C to 35

0C except cold

season.

Fig. 3.7: Temperature Curve at various seasons used in HOMER.

The biomasses available in Bangladesh are cow dung, poultry waste and MSW which are

mentioned earlier in this research. The average value of biomass collection per day from these

51

resources is approximately 250 kg/day. These resources are very cheap and found everywhere in

Bangladesh. The available biomass in ton/day is shown in Fig. 3.8 which is used in HOMER Pro

software.

Fig. 3.8: Daily available Biomass (Cow dung, poultry waste and MSW) used in HOMER.

3.6 Technological Feasibility Analysis

This research deals with the utilization of available renewable energy resources like solar and

biogas for the purpose of charging EVs in Bangladesh. The proposed EVCS consists of a PV

module, and three biogas generators for electric power generation. The HOMER Pro

optimization software tool is used for designing and analysis of the economic analysis and

sensitivity analysis. The average solar irradiation obtained from NASA is 4.95 kWh/(m2·day).

The daily, monthly and yearly load profile is shown in Fig. 3.9 where the daily estimated load is

assumed as 99.26 kWh. The load varies during different seasons in Bangladesh. Every day 15–

20 EVs can be a reasonable load for the charging station. Charging hours are inversely related to

the state of charge (SOC) of the vehicles’ batteries. In the proposed design, the electric power is

supplied by the biogas generators when solar energy is unavailable to increase the effective

operational hours to 8–10 h. The hybrid renewable energy generated by the PV module and

biogas generators is supplied to recharge the EVs’ batteries.

The annual energy production scenario by resource type is shown in Fig. 3.10. Total energy

produced in a year by the system is approximately 40,170 kWh. The generated electricity then

applied to EV charger.

52

Fig. 3.9: Load profile (daily, seasonal) used in HOMER.

Every day 15–20 EVs can be recharged from this charging station according to the SOC of the

vehicle battery. According to the configuration, this EVCS is capable of charging 5 EVs

simultaneously. So, it is considered that all the EVs will arrive at different time interval in the

charging station not altogether. In the case of holidays, when vehicle consumption decreases,

excess energy can be sold to the national grid through vehicle to grid (V2G) technology. There is

also an option for purchasing electricity from the grid in the case of rainy days or during periods

of less biomass collection. The electricity generated by proposed hybrid power generation

scheme is then applied to EV charger which converts the power into constant DC Voltage.

Fig. 3.10: Annual energy production in kWh by resource type

kW

h g

ener

ati

on

53

In the hybrid renewable energy based charging station, the PV module, biogas generator 1, 2 and

3 contribute 39.41%, 23.65%, 25.26% and 11.66% of the total power generation, respectively, as

shown in Fig. 3.11.

Fig. 3.11: Percentage share of total generation by resources.

3.7 Economic Feasibility Analysis

The solar PV module of 10 kW and total generation of 10 kW from biogas resources are

expressed in financial terms in this section. The total cost of installation and replacement gives

the Net Present Cost (NPC) and O&M cost, respectively. The return of the investment is

assessed by the terms payback period and annual cash flow summary. Finally the Profitability

Index (PI) is used to determine the feasibility study of the proposed charging station.

It is assumed that an electric vehicle is used 26 days/month and consumes an average of 12 kWh

daily for traveling 80 km, so 0.15 kWh electric power is required per km run. The monthly

electric bill for charging an electric vehicle is about $38.12. Solar radiation is available for

producing electricity only for 5 to 6 h a day. Thus, the solar energy can generate electric power

only during those hours, whereas, biogas generators can be used 6–8 h daily. Calculation of

economic parameters is shown in Table 3.10.

54

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Cash

-Flo

w i

n U

SD

PV Bio1 Bio2 Bio3

Table 3.10: Economic parameters obtained from HOMER Pro software

Component

Name & Size

Active

Hours

Energy

Production

(kWh/Year)

Lifetime

(Years)

Annual

Cash-

Flow

Payback

Period

(Year)

Profitability

Index (PI)

Solar PV

(10kW) 5–6 15,350 25 $1880 10.1

>1

Biogas Gen.

(4 kW) 6–8 10,220 5 $1252 3.0

Biogas Gen.

(4 kW) 6–8 9,490 5 $1162 3.10

Biogas Gen.

(2 kW) 6–8 5,110 5 $626 3.72

In Fig. 3.12, the annual cash flow according to resource type is shown where PV is the highest

contributor to the energy generation and Bio 1, Bio 2 and Bio 3 are the next. For this charging

station, the value of the profitability index is greater than one, which indicates the project is

financially feasible.

Fig. 3.12: Annual cash flow by resources.

The net present cost (NPC) for the 10 kW PV, two 4 kW biogas generators, a 2 kW biogas

generator, 10 kW converter, 10 kWh lead acid battery system and charging assemblies for five

55

electric vehicles is $56,202 which is economically feasible. The operating cost of the proposed

design is found as $2,630 and the levelized Cost of Energy/kWh is found as $0.1302. The O&M

cost is significantly lower in case of the proposed EVCS as there is no use of fuel like diesel and

gas. An additional advantage of the proposed system is that the extracts obtained from the biogas

generators can be used as fish feed and fertilizer which helps to minimize the O&M cost.

The cost of energy in the proposed EVCS is less than that of a conventional grid-based charging

station. Although it is difficult to bear the initial cost of installation of such an EVCS, it will be a

profitable option after the payback period. In this system, a solar PV and three biogas generators

give the payback period of 10.1 years, 3.0 years, 3.10 years and 3.72 years, respectively. In Fig.

3.12, it is clear that the payback period is very much less than the lifespan of the system and that

indicates the system will be a profitable option for its developer.

Fig. 3.13: Payback period and life time of the proposed EVCS.

3.8 Environmental Feasibility of the Proposed EVCS

Electric vehicles have several key benefits, including reduction of GHG emissions, fumes and

noise pollution which are hazardous to the environment. The American Council for an Energy

Efficient Economy conducted a study which predicted that GHG emissions from electric vehicles

will be greater if the EVs are charged by the power generated from the coal-fired power plant

0

5

10

15

20

25

30

PV Bio 1 (4 kW) Bio 2 (4 kW) Bio 3 (2 kW)

Lifetime

Payback period

56

[38]. Table 3.11 and Table 3.12 show the CO2 emission rate by non-renewable sources and

renewable sources respectively [64].

Table 3.11: CO2 emission rate on non-renewable sources

Type of Fuel CO2 Emission (g/kWh)

Coal 800 to 1050

Natural Gas 469 to 600

Diesel 570 to 650

Furnace Oil 640 to 765

Table 3.12: CO2 generation by renewable energy

Type of Technology CO2 Emission (g/kWh)

Biomass-Dedicated 130 to 420

Solar PV-Utility Scale 18 to 180

Solar PV-Rooftop 26 to 60

Geothermal 6.0 to 79

Concentrated Solar Power 8.8 to 63

Wind Offshore 8.0 to 35

Wind Onshore 7.0 to 56

Nuclear 3.7 to 110

Hydropower 1.0 to 22

Figure 3.14 represents that the non-renewable sources produce higher CO2 than renewable

energy sources. These scenarios encourage the establishment of hybrid renewable energy-based

power plants.

57

Fig. 3.14: Carbon dioxide generation in renewable and conventional systems

Local power plants in Bangladesh produce approximately 0.64 kg/kWh of carbon dioxide [65].

Thus for 0.15 kWh energy, there will be CO2 emissions of 96 g, but for the same amount of

power generation, the proposed EVCS will produce 33.30 g of CO2. An issue related to non-

renewable power generation is the usage of land and water. It hampers the wildlife, affects the

rivers and sea water by disposing of the residues from the power generating stations. Renewable

energy based system requires less land and water use. Also it does not hamper the wildlife. But

biogas based plant requires more land space and water. However, in case of the proposed EVCS,

the effective use of the land space decreases due to use it with solar system for same amount of

power. The biogas resources such as poultry waste, MSW and cow dung, are very noxious to

human health. Electrical energy can be produced by utilizing these potential wastes. Byproduct

from the biogas plant can be used as fertilizer and fish feed, thus the environmental pollution is

significantly reduced. The EVCS can be a promising window to explore new dimensions in

engineering business especially for the unemployed strata of the society. It can improve the

socio-economic standards of the EV drivers and the engineers who work for the development of

the hybrid renewable energy based EVCS.

0

100

200

300

400

500

600

700

800

900

1000

CO

2 e

mis

sion

in

g/k

Wh

58

Fig. 3.15: Comparison of CO2 emission from grid based EVCS and proposed EVCS.

Environmental benefits of the proposed EVCS are the reduction of carbon dioxide emissions and

other pollutants. In this system, the amount of CO2 emission found for generating1 kWh energy

is about 220 g, whereas a conventional system produces 640 g of CO2. That means there is a

significant reduction of CO2 emission (65.62%) by this system which validates the project to be

of a sustainable environmental standard. Figure 3.15 shows the comparison of the CO2 emission

from the grid based EVCS and biogas based EVCS. In the proposed EVCS, yearly CO2 emission

is about 8,837.40 kg whereas for the same demand grid based charging station produces

25708.80 kg of CO2.

3.9 Comparison of Results between HOMER Analysis and Mathematical

Analysis

HOMER analysis gives the result based on input renewable resources however the loss

associated with the EV charging accessories are not taken into consideration. But in case of

mathematical analysis, the technological, financial and environmental parameters are taken into

account for finding proper results. The charger efficiency is assumed as per the technical

specifications given by the EV charger.

On the other hand, the financial parameters such as COE, NPC, operating cost, payback period,

profitability index are analyzed in HOMER provides the result only for generating power from

the resources. However, the mathematical analysis considers the charging equipment cost and

infrastructures for charging 5 EVs simultaneously. Thus the financial parameter varies from the

0 5000 10000 15000 20000 25000 30000

Grid based EVCS

(kg/yr)

Proposed EVCS

(kg/yr)

CO2 emission in kg

59

HOMER results.

Different researches performed on emissions associated with the EV charging signifies that, EV

charging process contribute in a small percent of GHG emission [66-67]. In addition, the battery

storage system helps to reduce CO2 emission showed in a research [68]. Materials used in the

battery also responsible for GHG emission. Moreover, EV battery receives power through

charger from utility grid during low or zero power generation from the proposed project. It

would be a source of GHG emission. Research performed on this issue demonstrate that,

electricity mix (fossil plus renewables) used for EV charging emits more GHG than renewable

resources based generation [69].

Table 3.13: Comparison of results from HOMER and Mathematical analysis

Parameters HOMER Analysis Mathematical Analysis

Technological

parameter

kWh generation 40,170 39,366.60

Financial

Parameter

COE $0.1302 $0.1469

NPC $56,202 $56,860

Operating cost $2,720 $1,350

Payback period

(year)

PV – 10.10 Bio1 – 3.00

Bio2 – 3.10 Bio3– 3.72

PV – 10.14 Bio1 – 3.05

Bio2 – 3.14 Bio3 – 3.75

Profitability

Index

2.02 1.98

Environmental

Parameter

CO2

emission/year

8,837.40 kg 10,750.85 kg

Moreover, battery production processes are responsible for GHG emission. Lead acid batteries

used in the EV produce SO2 whereas lithium ion batteries produce CO2 during charging process.

Table 3.13 represents the comparison of results obtained from HOMER and Mathematical

60

analysis. Actually the drawbacks of the HOMER system are found which needs to correct for

more viable and practical application.

3.10 Socio-Economic Aspects of the Proposed EVCS

Electric Vehicles are now very promising due to their several benefits mentioned earlier. In a

country like Bangladesh, there are different environmental and socio-economic factors behind

the rising popularity of electric vehicles. Electric vehicles such as Easy Bikes, Auto-rickshaws

and Electric rickshaw vans have high potential of reducing emissions, improving air quality in

both urban and rural areas and increasing the income level of the lower class people. An Easy

Bike driver in Bangladesh can easily earn approximately $18–$25 where the energy consumption

cost of this car is only $1–$1.25 daily, so the living standard of an unemployed person would be

upgraded due to electric car adaptation.

The mass introduction of Easy Bikes could lead to a revolution in rural transport. It would

increase the income rate of the rickshaw pullers and curtail their transportation time and physical

labor down to half. However, the demand for charging stations is increasing rapidly. Charging

cost of an Easy Bike is around $45 per month. It will be lower than the present cost if the EVs

are charged by the proposed charging station. Table 3.14 shows the summary of the charging

cost in a grid-based system and the proposed EVCS-based system. The monthly savings and

monthly income by using this EVCS for an EV driver are given in Table 3.14. In the proposed

charging station, there will be low O&M cost and resources are available, so utilizing these

resources can provide an excellent opportunity to reduce pressure on the grid as well as the

environment and improving life standards. Thus, the system loss for recharging electric vehicle

will be reduced and government power sector will benefit a lot.

Table 3.14: Charging cost and monthly income summary of an EV

Electric

Vehicle

Charging Cost/Month

(Grid based EVCS)

Charging Cost/Month

(Proposed EVCS)

Monthly

Savings

Easy Bike $44 $26.25 $17.75

Auto-Rickshaw $28 $15.00 $12.50

61

3.11 MATLAB SIMULINK Modeling for Solar and Biogas Based Generation

Electricity generation for the purpose of EV charging is obtained by the hybridization of solar

and biogas resources. In this section, the MATLAB SIMULINK model of the stand alone solar

PV and Biogas system is given. The effective output from the both system can be a cost-

effective, energy efficient and environment friendly method to reduce the power crisis for EV

charging.

Fig. 3.16: Solar PV based system model.

Figure 3.9 shows the PV based electricity generation scheme where three PV module is added up

for electricity generation. The irradiance is considered as 1000 W/m2 and temperature varies

from 250C to 45

0C for this simulation. Three PV module is added up for electricity generation. In

62

this modeling, the PV module named as 1 soltech 1STH 350 Wh is chosen. The combined output

of the model shown in Fig. 3.16 which exhibits current and power variation with respect to

system voltage.

Solar Cell I-V Characteristics Curves are basically a graphical representation of the operation of

a solar cell or module summarizing the relationship between the current and voltage at the

existing conditions of irradiance and temperature shown in Fig. 3.17. I-V curves provide the

information required to configure a solar system so that it can operate as close to its optimal peak

power point (MPP) as possible. The intensity of the solar radiation (insolation) that hits the cell

controls the current (I), while the increases in the temperature of the solar cell reduce the voltage

(V). Another curve P-V shows the Power versus Voltage for a PV module in Fig. 3.17.

Fig. 3.17: I-V & P-V Characteristics curve of PV based Model.

Biogas generation comprises from the Cow dung, poultry waste and MSW which will be

collected from the surroundings. The simulink model for digester size and corresponding biogas

generation is shown in below. The value of cow dung, poultry waste and MSW varies from

month to month. Thus a variation in input has been chosen as per the biomass collection of each

63

month. Figure 3. 9.2 shows the biogas generation scheme using cow_dung, poultry_waste and

MSW as the inputs. Depending on the biomass/biogas resources collection, the output biogas

generation is carried out and finally this biogas is used to generate electricity.

The biogas production from the wastes as cow dung, poulrty droppings and MSW are used as the

inputs in MATLAB SIMULINK. The simulation results shows the variation of biogas generation

with respect to input wastes. Also, the digester size is found from the simulation. The digester

size and corresponding biogas generation plotted against month is displayed in Fig. 3. 18 where

the maximum digester size is found 39.86 m3 and minimum is 35.25 m

3.

Fig. 3.18: Biogas Generation output in m3.

3.12 Summary

This chapter firstly identified the solar and biogas/biomass potential in Bangladesh. It concludes

that, use of solar resources can meet up the existing demand as well as future demand. The

government also recognize the solar potential and already use it in the different charging station

for EV battery charging. However, in case of rainy/foggy day and night time, the stand alone PV

system can not able to generate electricity. Thus, in this research I have incorporate biogas

resources which will increase effective operational hours. The first portion of this chapter also

64

determines that, cow dung, poultry waste and MSW can be a good source for electricity

generation and it is possible to generate 4913.24 kW, 1344.50 kW and 2942.81 kW electricty per

day respectively.

The mathematical modeling of the hybrid renewable power generation based EVCS is described

in this chapter which includes different parameters like technological, financial and

environmental parameter. Then, different components specification for designing a hybrid

renewable based EVCS i.e. solar photovoltaic (PV) module, digester, biogas generator,

converter, battery, hybrid charge controller and electric vehicle charging apparatus are analyzed

according to the market availabilities and price.

HOMER Pro software is used for designing the proposed EVCS. According to the technological

view of point, it is found that 40,170 kWh electric energy can be produced by this proposed

method. Moreover, every day 15–20 EVs can be recharged from this charging station according

to the SOC of the vehicle battery. In case of financial feasibility, getting the profitability index

greater than unity and payback period less than the life time indicate the proposed EVCS is

feasible. The COE is found as $0.1302. The proposed EVCS yearly emits CO2 which is about

8,837.40 kg whereas for the same demand grid based charging station produces 25708.80 kg of

CO2. The difference in the HOMER result and the mathematical analysis responds to the several

factors associated with the EV charging which are expressed in a table.

In the context of Bangladesh, proposed EVCS can save monthly $12.50-$17.75 of the EV

owners who recharges their batteries into this EVCS instead of grid connected EVCS.

Finally MATLAB SIMULINK based PV and Biogas generation scheme shows that, the

proposed EVCS is feasible and can serve the EV owners efficiently.

65

Chapter 4

Fuzzy Optimization of Proposed EVCS

4.1 Introduction

Optimization algorithm is usually designed for maximizing or minimizing one or more objective

function. In the previous chapter, the use of solar and biogas resources for power generation to

recharge the EV batteries is found feasible according to the technological, financial and

environmental aspects. Thus, in the present chapter fuzzy logic based algorithm is developed for

minimization of charging cost while maximizing the use of renewable resources. MATLAB

based fuzzy logic controller is divided into two major categories: Mamdani and Sugeno. In this

chapter, firstly the input & output variable and the corresponding membership functions are

defined. Then the algorithm is designed based on if-then rules in fuzzy inference system. The

results obtained from Mamdani and Sugeno type fuzzy logic controller are compared in the final

section of this chapter.

4.2 Block Diagram of the Proposed EVCS

The block diagram of the proposed EVCS is shown in Fig. 4.1 where solar energy and

biomass/biogas resources (waste) are the input of the proposed EVCS. Solar power is found in

the form of DC and it needs to convert in AC using converter. Biogas forms from the digester

and the resultant is converted into electricity by using biogas generator. The combined output

from the solar and biogas system gives the output power generation. The output power

generation of the proposed EVCS depends upon the input resources as-

),( wsfPout , (4.1)

Where s is the solar irradiation and w is the waste input. Solar energy is found only on 9:00 AM

to 3:00 PM. Also, the solar resources is absent in the rainy and foggy days. In that case biogas

energy offers an alternate way to produce electricity. In addition, the biogas system provides

slurry which is used as fertilizer and fish feed. The power demanded by the EVs when greater

66

than output power generation, then the difference power comes from the utility grid. However, in

case of excess generation the extra power supply will be given to the residential connection. The

overall power demanded by EVCS is then fulfill the energy demand. In this case a fuzzy logic

controller will be employed to optimize the charging cost for different EVs at different time

period & duration. Output generated power availability, power demand of EVs, period &

duration of charging is taken as input variable and charging rate is the output variable.

Fig. 4.1: Block diagram of the proposed EVCS.

67

4.3 Fuzzy Optimization Model

In this optimization model, MATLAB based fuzzy logic scheme is used. Both Mamdani &

Sugeno type fuzzy inference model is used in this proposed EVCS for obtaining optimized

charging rate at various input conditions. Centroid based defuzzification technique is employed

in this model. Output power availability, Power demanded by EVs, period and duration of

charging are the input parameters of fuzzy model and charging rate is the output parameter. The

fuzzy (Mamdani) optimization model shows input & output variables in Fig. 4.2.

Fig. 4.2: Fuzzy (Mamdani) optimization model

4.4 Input and Output Variables

Power availability of the EVCS depends on the input renewable resources i.e. solar and biogas. If

the solar and biogas resources are sufficient then the output power generation will be maximum

and thus the power availability will be high. On the other hand, when solar energy is absent then

the generated power is low. Thus, the input parameter power availability is categorized by three

categories as the membership function (Less, Normal and High).

68

Fig. 4.3 (a): Input variable “Power_ Availability” with membership functions.

Power demanded by an EV depends on the battery capacity and SOC. In this model, the

membership functions of “Power_Demand” are as “Very_Low”, “Low”, “Medium”, “Large”

and “Very_Large”. As the number of EV comes in the charging station with different battery

capacity and SOC, thus the power demand varies. For 0 - 4 kW the membership function defined

as “Very_Low”, 4 - 8 kW is “Low”, 8 - 12 kW is “Medium”, 12 - 16 kW is “Large” and 16 - 20

kW is “Very_Large”.

Fig. 4.3 (b): Input variable “Power_ Demand” with membership functions.

In Bangladesh, peak hour is defined as 5:00 PM to 11: 00 PM and off-peak hour is the time

between 11:00 PM to 5:00 PM. For this reason, in this proposed model membership functions of

the “Period_ of_ Charging” are “peak_hour” and “off-peak_hour”.

69

Fig. 4.3 (c): Input variable “Period_of_Charging” with membership functions.

Electric vehicles in Bangladesh mainly recharge their batteries in night time. But few of them are

charged at day time also. Normally the easy bike and auto-rickshaw battery takes 8-10 hours for

full charging. But the EVs come at charging station with above the minimum SOC, requires less

time to recharge. Thus, the “Duration_ of_ charging” has membership functions as “Short”,

“Average”, “Big”. For 0-3 hours charging is considered as “short”, 3-6 hours is “Average” and

6-10 hours is “Big”.

Fig. 4.3 (d): Input variable “Duration_ of_Charging” with membership functions.

Charging rate is the output variable which is used in designing fuzzy system. It has membership

functions declared as “Very_Small”, “Small”, “Moderate”, “High” and “Extra_High”.

70

Fig.4.3 (e): Output variable “Charging_Rate” with membership functions.

4.5 Optimization Algorithm

The main objective of the optimization algorithm is to minimize charging rate while keeping the

utilization of renewable resources maximum. The objective function of the proposed model is as

follows:

}{ argingchCMinimize (4.2)

While renewable resources should be utilized as maximum. This objective function works under

few constraints as follows:

maxmin SOCSOCSOC i ; (4.3)

giD PtPP )( ; (4.4)

21 ttt ; (4.5)

The generated power, Pg depends only on the availability of renewable resources. This is also a

cause of varying electrcity price in the proposed EVCS. The power demand of the EV depends

on the SOC and battery capacity. In this model, the minimum SOC is taken as 20% where

maximum SOC is 80%. The generated available power should be less than or eqaul to the power

demand of the EVCS. In that case the renewable resources will be used as maximum. The

duration of charging is the time to recharge the batteries. Equation (4.6) shows the duration of

71

charging of EV. The period of charging should be in between t1 and t2. Minimum duration of

charging, t1 is assumed as 3 hours whereas maximum charging time, t2 is taken as 10 hours.

2

1

t

t

waitingarrivaldepartureD TTTT (4.6)

The period of charging is divided into two parts- peak_hour, off_peak hour. Equation (4.7)

displays the time/period of charging of EV.

(4.7)

The power demanded by an EV can be expressed as

(4.8)

The overall charging cost is the function of the four input parameters such as- power availability,

power demand, period and duration of charging. Thus, it can be represented by equ. (4.9).

2

1

)(),(

t

tt

DC dttrtiPF

(4.9)

where r(t) is the actual charging rate for an EV. Charging time varies from t1 to t2. If t is equal to

the TC, then r(t) will be maximum. Also, if the power generation is smaller within the proposed

EVCS, the extra demand will be fulfilled by the utility grid. In that case the price will be highest.

The definite integral of the r(t) is constant over the time between t1 and t2. So,

})(

{}{1

minmax

N

i D

CDC

T

SOCSOCBMinimizePMinimizeFMinimize

(4.10)

.

N

i D

CD

T

SOCSOCBP

1

minmax )(

pmpmT

pmpmTT

peak

peakoff

C 115;

511;

72

Figure 4.4 shows the optimization algorithm using fuzzy logic controller for the proposed EVCS.

Fig. 4.4: Optimization Algorithm for EVCS.

4.6 Fuzzy Rule Viewer

Figure 4.5 shows the fuzzy rule viewer diagram where if-then rules are emloyed for obtaining the

optimized charging rate. 92 rules are used in this mamdani based fuzzy algorithm and

corresponding output i.e. charging rate can be seen here for each and individual rule.

73

Fig. 4.5: Fuzzy rule viewer

4.7 ANFIS (Adaptive Neuro Fuzzy Inference System) Model Structure

The Adaptive Neuro Fuzzy Inference System (ANFIS) model is obtained from sugeno fuzzy

logic controller where number of input is 4 and output is 1. The membership functions of each

input are defined by user. Sugeno Fuzzy logic controller when integrated with the neural

network, then it is termed as ANFIS model. In this model, 54 rules are used and each time

controller provides an output. The output variable is defined by rules used in Sugeno logic

74

controller.

Fig. 4.6: ANFIS Model structure.

4.8 Result & Discussion

Electric Vehicle Charging Station optimization for minimizing charging cost is the main purpose

of this research. In the present energy scenario of Bangladesh, charging electric vehicles leads to

a huge consumption with increasing system loss. EV owners are also in a big trouble for being

less number of charging stations throughout the country. On the other hand, charging cost is very

high when the electric vehicles are charged from the commercial line. Due to solve these

problems, fuzzy optimization technique is employed in this research. Fuzzy “if-then” rule-based

strategy is used in this optimization system. The membership functions are defined according to

the data for the current battery electric vehicles of Bangladesh. The surface view of the charging

75

rate, power availability, power demand, time of charging and duration of charging is shown in

Fig. 4.7 (a) & (b).

(a) Charging rate with respect to power availability and duration of charging.

(b) Charging rate with respect to power availability and time/period of charging.

76

(c) Charging rate with respect to power demand and duration of charging.

(d) Charging rate with respect to power demand and time/period of charging

Fig. 4.7: Surface view of charging rate, Power availability, power demand, time of

charging and duration of charging, (a), (b), (c) and (d).

77

The variation of charging rate with power availability, power demand, time/period of charging

and duration of charging are expressed in Fig. 4.8 (a), (b), (c) & (d).

When the available power generation at

maximum i,e, the membership function

is “High”(16-20 kW), then the charging

rate according to Mamdani FLC is 0.33.

Thus, using actual electricity tariff for

the EV consumer in Bangladesh, it

would be BDT. 3.05. However, at less

generation period i.e. at 0-5 kW, the

charging rate is found 0.522 which

means the tariff would be near about

BDT. 4.83. Thus, it is economical to

charge the EVs in the proposed charging

station during the highest generation.

(a) Variation of charging rate with power availability

The variation of charging rate with

power demand is shown in Fig. 4.8

(b). Here, it is seen that, during the

power demand is very low(0-5 kW),

the degree of charging rate is near

about 0.12. Thus, the charging cost

would be equal to BDT. 1.11 which

seems very small. However, during

high power demand (15-20 kW), the

degree of charging rate is near about

0.7 i.e. BDT. 6.47, which is expensive.

(b) Variation of charging rate with power demand.

78

During peak hour the electricity tariff is

higher due to having large demand. If the EV

takes place for charging in this period it

would charges higher rate. Figure 4.8 (c)

shows that, charging rate is higher in the time

between peak-hour and lower in off peak-

hour. At peak hour the charging rate is about

BDT.6.66.

(c) Variation of charging rate with time/period of charging.

Charging duration depends upon battery

capacity, SOC. If the duration of charging

becomes higher, the charging cost would be

expensive according to the fuzzy rules.

However, if this charging is performed on off

peak hour, the charging cost will be lower.

Figure 4.8 (d) shows the variation of charging

rate at peak and off peak hour with difference

in charging duration.

(d) Variation of charging rate with duration of charging.

Fig. 4.8: Variation of charging rate with (a) power availability (b) power demand (c) time of

charging (d) duration of charging from Mamdani Fuzzy logic controller.

79

(a) (b)

(c) (d)

Fig. 4.9: Variation of charging rate with (a) Power availability (b) Power demand (c) time of

charging (d) duration of charging from Sugeno Fuzzy logic controller.

Figure 4.9 shows the variation of charging rate with four input variables in a sugeno fuzzy logic

controller. As like Mamdani, Sugeno logic controller also optimizes charging cost with respect to

four input variables.

80

Comparison between the output charging rate obtained from Mamdani and Sugeno type fuzzy

logic controller is given in Table 4.1 for different input variables.

Table 4.1 (a): Charging rate variation with power availability in Mamdani & Sugeno

Power_ availability

Membership

function Range (kW)

Charging_ Rate (Degree of

membership function)

Mamdani Sugeno

Low 0-7 0.52 0.5

Medium 7-14 0.5-0.32 0.5-0.11

High 14-20 0.32 0.01

Table 4.1 (b): Charging rate variation with power demand in Mamdani & Sugeno

Power_ Demand

Membership

function Range (kW)

Charging_ Rate

Mamdani Sugeno

Very low 0-4 0.12 0.48

Low 4-8 0.12-0.15 0.48-0.5

Medium 8-12 0.5 0.5-0.7

High 12-16 0.5-0.7 0.99-1.0

Very High 16-20 0.7 1.0

Table 4.1 (c): Charging rate variation with time/period of charging in Mamdani & Sugeno

Time of charging

Membership

function Range (Hour)

Charging_ Rate

Mamdani Sugeno

Off Peak-hour 0-17 0.5-0.52 0.5

Peak hour 17-23 0.72 1.0

Table 4.1 (d): Charging rate variation with duration of charging in Mamdani & Sugeno

Duration of

charging

Membership

function Range (Hour)

Charging_ Rate

Mamdani Sugeno

Low 0-3 0.33-0.35 0.04-0.05

Medium 3-6 0.45-0.50 0.1-0.5

High 6-10 0.51 0.52

81

Electric vehicle charging affects the power network by consuming huge energy. If the EV

charging is performed on peak hour, it will surely provide a negative impact on the power

system. Otherwise, electric vehicle charging at off-peak hour can be beneficial for the power

system and also for the consumers. In Bangladesh, off-peak period are between 11 pm to 5 pm

and peak hours are between 5 pm to 11 pm. Due to having less power demand in the off peak

hour, the charging rate will be lesser than the peak hour charging rate. In the proposed fuzzy

based EVCS, the charging rate for peak hour is taken greater than the off peak hour.

The conventional EVCS takes on average $1.5 to $1.875 for charging an EV. According to the

electricity tariff determined by BERC (Bangladesh Energy Regulatory Commission), the battery

charging rate is $0.09625/ kWh (BDT.7.70/kWh) of electricity consumption. However,

optimization of proposed EVCS based on hybrid renewable resources offers different tariff for

difference in time, duration, power demand and availability of the power generated by the EVCS

itself. In this proposed method, the charging cost is optimized which is shown in Fig.4.8.

Conventional charging station has maximum charging rate per kWh $0.09625 (BDT. 7.70)

whereas the fuzzy logic based EVCS offers $0.0925 (BDT. 7.40) at peak hour conditions. During

off-peak hour proposed EVCS will take $0.073 - $ 0.07375 (BDT. 5.84-5.90) per kWh.

Fig. 4.10: Comparison of charging rate by fuzzy logic system with conventional electricity price.

0

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6.00PM

8.00PM

10.00PM

12.00PM

2.00AM

4.00AM

6.00AM

8.00AM

10.00AM

12.00AM

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Charging Cost (Existing Tariff in BDT.)

Charging Cost (Fuzzy optimized tariff in BDT.)

Hour

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.)

82

This type of optimization motivates the EV consumer to charge the EV batteries on off-peak

hours which will further reduces the demand at the peak hour period. In addition, at day time

solar energy is available and charging from solar energy resources will maximize the renewable

energy utilization and cut the excess battery requirement for the EVCS.

4.9 Summary

Optimization algorithm for the proposed EVCS is developed in this chapter for minimizing the

charging rate while maximizing the use of renewable resources under subjected to few

constraints. In this chapter, firstly fuzzy logic based algorithm is designed using four input

parameters such as- power availability, power demand, time/period of charging and duration of

charging. All these parameters are assumed in different categories called membership functions.

Also, in case peak/off peak hour, the charging rate is different in Bangladesh and the tendency of

EV charging in peak hour causes a great harm to the power sector. Thus, in this logic it is

recommended to place high charging rate for this period. Power availability is a function of

renewable resources because the output power availability depends upon the solar and biogas

resources. In the absence of solar resources, only biogas can help to provide electricity. Thus the

charging rate of will be high for less availability of power generation.

Mathematical modeling for optimization is analyzed in this chapter. This optimization algorithm

is used in both Mamdani and Sugeno fuzzy logic scheme and finally a comparison is established

which demonstrate that, the Mamdani is best suited for optimization of the proposed EVCS.

The conventional grid connected charging station has maximum charging rate per kWh $0.09625

(BDT 7.70) whereas the fuzzy logic based EVCS offers $0.925 (BDT. 7.40) at peak hour period.

Also, for off-peak hour conventional EVCS takes $0.09625 (BDT. 7.70) per kWh. However, the

proposed EVCS will take $0.073 - $ 0.07375 (BDT 5.84-5.90) per kWh at off-peak hours.

This type of optimization will inspire the EV owner to charge the batteries at off peak hour

which further cut the demand at peak hour period. In addition to this, during day time solar

energy is available. Thus, the charging at day time corresponds to the maximum utilization of

renewable resources with minimum battery backup for the proposed EVCS. Use of Mamdani

fuzzy logic controller scheme will be a potential optimizer for minimizing charging cost with

maximizing renewable energy utilization.

83

Chapter 5

Conclusions and Future works

5.1 Conclusion

Growing popularity of Electric Vehicles opens a new sector in the field of transportation. It is an

user & environment friendly and cost-effective mode of transportation. The whole research

aimed to design, develop and optimization of Electric Vehicle Charging Station using solar and

biogas/biomass resources.

This research identifies the main challenges of the EV adoption in Bangladesh by PORTER’s

five forces model, PESTEL and SWOT analysis such as- lack of charging infrastructure, high

charging cost & time, no governmental policy, less investment and technological barriers etc.

Finally, the negative impacts of the existing EVCS onto the utility grid, distribution station by

affects power quality which is analyzed by MATLAB SIMULINK. It is found that, EV chargers

are non-linear load and produces harmonics, sag/swelling and transformer power loss. Moreover

the increased demand causes a great harm to the power sector and accelerates the load shedding

process. This research shows that how the large number of electric vehicle charging station

affects the power quality of Bangladesh. In order to establish the efficient charging infrastructure

throughout the country, several policies are recommended in this research after analyzing

different barriers.

In chapter 3 determines the solar and biogas potential throughout the country and showed that

cow dung, poultry waste and MSW can be a good renewable resources for electricity generation

which can meet the existing and future demand. As the transportation fuel and electricity

generating fuel is limited in stock, thus it is necessary to incorporate renewable resources.

Moreover, the proposed hybrid generation based EVCS is found feasible in case of

technological, financial and environmental benefits. The proposed method produces less GHG

than other grid connected EVCS. It reduces up to 65.62% CO2 employing solar and biogas

resources. Another benefit is, the proposed EVCS produces slurry that can be used in agricultural

field and fish feed. The O & M cost of the proposed plant can be minimized by selling the slurry

as a fertilizer or fish feed. Besides a comparison is made between the results obtained from

HOMER analysis and mathematical analysis. The socio-economic aspects of the EV adoption

84

also are analyzed in this research. In addition, identification of great potentiality of renewable

resources and from these the possible electricity generation is obtained. However, to cut the huge

pressure on the national grid and make the EV sector a profitable one, effective utilization of

available renewable resources can be a great option.

Main part of this research which demonstrates that fuzzy optimization algorithm based on the

power availability, power demand, duration & period of charging determines the optimum

charging rate for the EV charging. By using the same algorithm for both the Mamdani and

Sugeno fuzzy logic controller, it is found that the optimization results found from the Mamdani

controller is more cost-effective and efficient. For sustainable development of the country’s

power sector and also the transportation sector this research will bring a new hope and contribute

to the national development.

5.2 Future Works

This research mainly identified the renewable resources and tested the feasibility of these

resources for electric vehicle charging. Only the solar and biogas resources are assumed to

exploit in this hybrid generation system. However, in future experimental investigation will be

carried out if sufficient funding is found. The environmental affects upon acceptance of Electric

Vehicle charging infrastructure all over the country will be analyzed which also be a good option

for researchers.

The optimization algorithm developed in this system is based on fuzzy logic. In future, the

proposed hybrid renewable based EVCS will be optimized in particle swarm optimization (PSO),

genetic algorithm (GA) and also by the combination of GA-PSO algorithm for better results.

Comparison among these algorithms for obtaining better result could be an interesting point to

the researchers. Also the implementation of the best optimizer among these methods can help us

to build a sustainable charging infrastructure worldwide.

Finally, a scheme will be designed as the bidirectional energy transfer facility like smart grid for

EVCS that is called V2G (Vehicle to Grid) technology. During blackout and peak hour period,

the EVs will transfer energy to the utility grid by using the scheme which will be the future

work.

85

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92

Publications Related to Thesis

Journal Papers:

1. AK Karmaker, MR Ahmed, MA Hossain, and MM Sikder. “Feasibility assessment &

design of hybrid renewable energy based electric vehicle charging station in

Bangladesh.” Sustainable Cities and Society 39 (2018): 189-202.

2. Ashish Kumar Karmaker, Md Mijanur Rahman, Md Alamgir Hossain, and Md Raju

Ahmed, “Exploitation of Biogas Resources for Electric Vehicle Charging in

Bangladesh”, is under review at International journal of Energy & Environment.

Conference Papers:

1. Ashish Kumar Karmaker and Md. Raju Ahmed, “Techno-economic & environmental

feasibility analysis of solar-biogas based electric vehicle charging station in Bangladesh”

accepted & Presented in IEEE conference of EECCMC at Vellore, Tamil Nadu, India

2018.

2. Ashish Kumar Karmaker, Sujit Roy, and Md. Raju Ahmed, “Analysis of the impact of

Electric Vehicle Charging on Power Quality issues.” accepted in IEEE Conference on

ECCE, 2019, CUET, Cox’s Bazar, Bangladesh.

3. Md. Raju Ahmed and Ashish Kumar Karmaker “Challenges for Electric Vehicle

Adoption in Bangladesh.” accepted in IEEE Conference on ECCE, 2019, CUET, Cox’s

Bazar, Bangladesh.

4. Ashish Kumar Karmaker, Md. Raju Ahmed, “Fuzzy logic based Electric Vehicle

Charging Station Optimization in Bangladesh”, Submitted in IEEE conference on IC4

ME2-2019, Rajshahi University, Bangladesh.