Electrical Vehicle in India

8/20/2019 Electrical Vehicle in India

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Electrical consumption of two-, three- and four-wheel light-duty electric

vehicles in India

Samveg Saxena ⇑, Anand Gopal, Amol Phadke

Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, United States

h i g h l i g h t s

Model electrical consumption of 2-, 3- and 4-wheelers in India. Average city energy use is 33 Wh/km for scooters, 61 Wh/km for 3-wheelers.

Average city energy use is 84 Wh/km and 123 Wh/km for low and high power 4-wheelers.

The increased energy use from air conditioning is quantified.

Energy use from variations in vehicle mass and motor efficiency are quantified.

a r t i c l e i n f o

Article history:

Received 9 August 2013

Received in revised form 18 October 2013

Accepted 27 October 2013

Available online 20 November 2013

Keywords:

Electric vehiclesPowertrain

Transportation

Vehicle to grid

India

a b s t r a c t

The Government of India has recently announced the National Electric Mobility Mission Plan, which sets

ambitious targets for electric vehicle deployment in India. One important barrier to substantial market

penetration of EVs in India is the impact that large numbers of EVs will have on an already strained elec-

tricity grid. Properly predicting the impact of EVs on the Indian grid will allow better planning of new

generation and distribution infrastructure as the EV mission is rolled out. Properly predicting the grid

impacts from EVs requires information about the electrical energy consumption of different types of

EVs in Indian driving conditions. This study uses detailed vehicle powertrain models to estimate per kilo-

meter electrical consumption for electric scooters, 3-wheelers and different types of 4-wheelers in India.

The powertrain modeling methodology is validated against experimental measurements of electrical

consumption for a Nissan Leaf. The model is then used to predict electrical consumption for several types

of vehicles in different driving conditions. The results show that in city driving conditions, the average

electrical consumption is: 33 Wh/km for the scooter, 61 Wh/km for the 3-wheeler, 84 Wh/km for the

low power 4-wheeler, and 123 Wh/km for the high power 4-wheeler. For highway driving conditions,

the average electrical consumption is: 133 Wh/km for the low power 4-wheeler, and 165 Wh/km for

the high power 4-wheeler. The impact of variations in several parameters are modeled, including the

impact of different driving conditions, different levels of loading by air conditions and other ancillary

components, different total vehicle masses, and different levels of motor operating efficiency.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

India is one of the world’s most rapidly growing economies, and

is the third largest vehicle market in the world. Annual demand of

vehicles is rapidly growing in India, with 2020 annual projected

sales of 10 million passenger vehicles, 2.7 million commercial vehi-

cles, and 34 million two-wheelers. India currently imports about

85%of itsoil and is projected to reach 92% by 2020, creatinga signif-

icant challenge for the balance of payments and the energy security

of the country [1]. Based on the pressing challenges with growth in

vehicle sales and energy security facing the country, the Central

Government of India has released the National Electric Mobility

Mission Plan (NEMMP) [1] which establishes a pathway for thewidespread deployment of hybrids, plug-in hybrids, and electric

vehicles in India. The NEMMP calls for the deployment of 5–7 mil-

lion EVs (hybrids and full EVs) on the roadby 2020, and the Govern-

ment of India has committed Rs 22,500 cr (approximately $4.1

Billion USD) to this initiative. Similar goals for widespread electric

vehicle adoption have been set by other governments around the

world [2–4].

The rapid deployment of plug-in hybrid and fully electric vehi-

cles (collectively called plug-in vehicles, PEVs, in this paper) called

for in the NEMMP places significant demands on an already

strained electricity grid in India [5]. However, since range anxiety

is a significant consumer perception barrier to EV deployment

0306-2619/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.apenergy.2013.10.043

⇑ Corresponding author.

E-mail address: [email protected] (S. Saxena).

Applied Energy 115 (2014) 582–590

Contents lists available at ScienceDirect

Applied Energy

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[6], the absence of reliable charging points (which require a stable

electricity grid) in India will make it difficult to achieve the tar-

geted levels of EV market penetration. Additionally, if the electric-

ity grid is unable to accommodate PEV charging, it is possible that

diesel generators will be used to provide the unmet electricity de-

mand. Although this local distributed generation solution may

accommodate PEV charging demand in the interim, it is not an

effective way to decouple the Indian transportation sector fromoil and can still lead to urban air quality problems.

The Government of India has recently joined the Electric Vehicle

Initiative (EVI) of the Clean Energy Ministerial, which seeks to facil-

itate the deployment of 20 million EVs by 2020. Under this initia-

tive, Lawrence Berkeley National Laboratory is supporting the

NEMMP in assessing the real-world costs, benefits and environ-

mental impacts of EV uptake in India; this publication is the first

in a series of studies in this effort.

To properly plan for the rapid deployment of PEVs in India,

there is a need for finely resolved temporal and spatial predictions

of PEV charging load on the electricity grid. The ability to properly

forecast PEV charging load is essential for utility grid operators to

ensure that adequate generation capacity is available at the correct

times, and ensure that distribution infrastructure can accommo-

date substantial PEV charging. Several studies [7–13] have devel-

oped methods to estimate PEV charging load for the US

electricity grid. The most rigorous of these studies [14–16] follow

a three-step methodology (listed below) to predict temporally re-

solved PEV charging load profiles. A modeling tool, called V2G-

Sim, has been developed at Lawrence Berkeley National Laboratory

to streamline the simulation of vehicle-grid interactions and this

tool is available for use in potential research collaborations [17].

1. Estimating the time when vehicles are plugged in: Survey data is

used to provide information on how drivers use their vehicles,

including number of vehicle trips per day, time of departure

of each trip, trip travel length, arrival time of each trip, type

of vehicle, etc. In the United States, a common data source for

this information is the National Household Travel Survey(NHTS) [18], however other data sources have also been used.

2. Estimating the amount of energy required to charge the vehicle

battery: Typically, a simplified vehicle model is used to esti-

mate: (a) how much of the vehicle battery is depleted during

each trip, and (b) how much energy is required during charging.

A standard approach in prior studies [14–16] is to assume a

constant value for electrical consumption (kWh/km or kWh/

mile) depending on the type of vehicle (i.e. car, van, SUV, truck,

etc.) that is being modeled. More accurate estimates of battery

depletion while driving can be obtained with detailed vehicle

physics models (such as models used in other papers), however

this approach may be prohibitively computationally expensive

when attempting to model hundreds, thousands, or millions

of PEVs on an electricity grid.3. Estimating charging rates while a vehicle is plugged in : Using esti-

mates of when different vehicles will plug in for charging from

step 1, how much charging is required from step 2, and infor-

mation about the charging rate (i.e. level 1, level 2, or DC fast

charger), number of PEVs and any smart charging strategies,

aggregate charging load profiles are estimated for a large num-

ber of vehicles within a given region (i.e. utility service territory,

state, or country).

Successful implementation of the NEMMP within the prescribed

timeline requires immediate planning and infrastructure deploy-

ment to ensure that the Indian electricity grid can cope with the

added charging load from large numbers of PEVs. Thus, the 3-step

analysis methodology described above must be applied to the In-dian context, however much of the required data for India is not

available in published studies. For instance, for step 1 better data

is required to characterize vehicle usage patterns in India. For step

2, average electrical consumption (Wh/km) numbers are required

for vehicles specifically in the Indian context (i.e. for vehicle sizes

representative of typical Indian vehicles driving in Indian traffic

conditions). The use of prior published electrical consumption val-

ues does not adequately account for typical Indian vehicles or for

the influence of driving and usage factors (i.e. from dense traffic,or the use of power-consuming devices like an air conditioner).

Electrical consumption data for scooters, 3-wheelers, and small

4-wheelers has previously been unavailable in the literature, par-

ticularly for the Indian context where driving conditions will be

different than in developed countries and air conditioning load will

be a significant factor. For the Indian context in particular, it may

be inappropriate to use prior published Wh/km values because

two-wheelers and ultra-compact four-wheelers that are typical

in India are significantly smaller and lighter than the US market,

and typical driving conditions are different in India with more fre-

quent stopping, lower average speeds and potentially more sudden

acceleration and deceleration [19].

In support of the NEMMP and as a step towards predicting the

charging load of PEVs on the Indian electricity grid, the results pre-

sented in this study will enable better estimates of PEV charging

load on the Indian electricity grid. Specifically, the results of this

study provide Wh/km values that are representative of typical

vehicles in India, driving in conditions representative of Indian

roads. The results of this study can then be used in Step 2 of the

3-step methodology above to estimate temporally resolved PEV

charging loads on the Indian electricity grid.

2. Specific objectives

In support of the India National Electric Mobility Mission Plan,

this study provides critical data to enable detailed predictions of

PEV temporal charging load profiles for the Indian electricity grid.

Detailed vehicle powertrain modeling is used for:

1. Providing estimates of average electrical consumption

(Wh/km) for vehicles that are representative of typical

Indian two-, three- and four-wheel vehicles over drive

cycles that are representative of Indian driving conditions.

2. Providing correlations for the Wh/km results that account

for variations in vehicle use, such as variability in vehicle

mass, the use of air conditioners, and variations in power-

train component efficiency.

3. Vehicle models

3.1. Vehicle powertrain models

A detailed vehicle powertrain model is used to estimate electri-

cal consumption for four types of vehicles, with specifications for

each vehicle listed in Table 1. The powertrain models are created

in the industry standard Autonomie powertrain modeling platform.

3.2. Drive cycles

Given that energy consumption of a vehicle depends signifi-

cantly on driving patterns [19–25], several different drive cycles

are chosen. Five drive cycles are chosen based on Indian driving

conditions, including a New Delhi cycle [26], Pune cycle [27], the

modified Indian drive cycle (MIDC) [28], and an Indian urban and

Indian highway cycle. Additionally three US certification cycles

are also included for comparison purposes, the EPA UDDS, HWFETand US06 cycles [29]. Figs. 1–4 compare the characteristics of each

S. Saxena et al. / Applied Energy 115 (2014) 582–590 583


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drive cycle in terms of velocity, stopping/idling, acceleration and

deceleration characteristics. The values in these figures are nor-

malized by the average values across all driving cycles to allow

easier comparisons.

Fig. 1 compares the velocity characteristics of the US and Indian

drive cycles. The plot shows that driving conditions on the Indian

cycles involve lower maximum speed, lower mean speed, and low-

er mean driving speed1. Even the speeds on the Indian highway cy-

cle are considerably lower than the speeds on the US highway cycles.

Fig. 2 compares the stopping and idling characteristics of the US

and Indian drive cycles. As expected, the results show that stop fre-

quency and fraction of total time stopped are much higher on the

city cycles as compared with the highway cycles. Of particular

importance, Fig. 2 shows that stop frequency is much higher in

the Indian city cycles than the US city cycle. The total fraction of

time stopped is highest in the Pune cycle, followed by the US city

cycle.

Fig. 3 compares the acceleration characteristics of the US and

Indian drive cycles. The highest acceleration values are seen in

the high speed US highway cycle (US06). Comparing the US and In-dian city cycles, it is seen that greater maximum acceleration and

Table 1

Vehicle specifications used in powertrain models.

Scooter 3-Wheeler Low power 4-wheeler High power 4-wheeler

Base vehicle mass (kg) 150 500 898 1493

Motor max power output (kW) 1.5 5.46 19 80

Final drive ratio 6.3805 6.3805 6.8737 7.9377

Usable battery capacity (kWh) 2.16 4.25 6.54 16.7

Tire size 1000 300 1000 4.500 P155/70R13 P205/55 R16

Drag coefficient 0.60 0.35 0.335 0.28Frontal area (m2) 1.25 2.40 2.0 2.50

Baseline electrical accessory & AC load (W) 50 100 200 200

Estimated range in City (km) 64–71 60–80 70–95 123–138

Estimated range on Highway (km) N/A N/A 34–76 73–136

Top speed (km/h) 50 73 117 120

Fig. 1. Velocity characteristics of US and Indian drive cycles.

Fig. 2. Stopping/idling characteristics of US and Indian drive cycles.

Fig. 3. Acceleration characteristics of US and Indian drive cycles.

Fig. 4. Deceleration characteristics of US and Indian drive cycles.

1 Mean speed is defined as the average of all velocities over the drive cycle. Meandriving speed is defined as the average of all non-zero velocities.

584 S. Saxena et al. / Applied Energy 115 (2014) 582–590


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maximum acceleration from stop values are encountered in the In-

dian city cycles, however the average acceleration is higher in the

US city cycle.

Fig. 4 compares the deceleration characteristics of the US and

Indian drive cycles. The results show that maximum deceleration

and maximum deceleration to stop are higher in Indian city condi-

tions than US city conditions, however higher levels of average

deceleration are seen in the US city cycle.Summarizing the results in this section, Figs. 1–4 compared the

drive cycle characteristics for the US and Indian drive cycles. It was

generally observed that the Indian drive cycles involve lower driv-

ing speeds, greater frequency of stopping, and higher levels of

maximum acceleration and deceleration. These results suggest that

driving in India may involve more severe stop-and-go conditions,

and previous studies [19,25] have found that these types of driving

conditions create unique opportunities for achieving greater levels

of fuel savings with vehicle electrification.

3.3. Parametric variations

In addition to the vehicle speed profiles while driving, other

parameters will significantly influence vehicle energy consump-tion as well. The vehicle modeling that is discussed in this paper

captures the impact on vehicle energy usage from several parame-

ters that will change with different vehicle designs, usage patterns,

and driving conditions. For warm climates like India, ancillary

components such as vehicle air conditioning load will have a

significant impact on energy consumption [30–31]. Loading the

vehicle with more passengers or cargo will also impact energy

consumption. Additionally, variations in powertrain component

efficiency will also impact energy consumption. Table 2 lists the

range of parameter variations that were explored using the vehicle

powertrain models for their impact on vehicle energy

consumption.

3.4. Model validation

To ensure that the electrical consumption estimates presented

in the results section of this paper are reasonable, the same mod-

eling methodology is followed to create a powertrain model for a

Nissan Leaf electric vehicle, for which there are well documented

values of electrical consumption under various driving conditions.

A vehicle powertrain model was constructed with specifications

resembling a Nissan Leaf, and electrical consumption model esti-

mates were compared against published measurement data [32]

for the EPA UDDS, Highway, and US06 drive cycles over a range

of total vehicle mass. Fig. 5 shows a comparison of the modeled

and measured electrical consumption values for a Nissan Leaf.

The modeled and experimentally measured electrical consump-

tion values plotted in Fig. 5 show that the vehicle powertrain mod-el reasonably predicts both the trends and absolute values of

electrical consumption for a range of different vehicle masses for

all three drive cycles. The largest difference in absolute values

between the model and the experimental measurements is

11.50%, which occurs for the lowest vehicle mass on the highway

cycle. It is typically the case that increased vehicle mass leads to

increased energy consumption, however the experimental

measurements on the highway cycle do not display this expected

trend. This may be due to experimental error because obviously

the vehicle mass will have a significant influence on the electricity

consumption of a vehicle. This expected trend is indeed seen for

the UDDS and US06 experimental measurements, thus the data

points at the lowest mass values for the highway cycle seem higher

than expected. As a result of the overall agreement of trends and

absolute values shown in Fig. 5, the modeling methodology is con-

sidered accurate enough for the purposes of this study.

4. Results

4.1. Baseline electrical consumption estimates

Table 1 lists the vehicle specifications that were used in the

powertrain models for an electric scooter, electric 3-wheeler, low

power EV 4-wheeler and high power EV 4-wheeler. These power-

train models provide the electrical consumption per kilometer esti-

mates over several different drive cycles in Fig. 6 and Table 3. There

are several numerical values in Fig. 6 which are crossed out (partic-

ularly for the electric scooter and 3-wheeler). These crossed out

values denote that the vehicle was unable to perform on the drive

cycle, either because the drive cycle requests speeds which are

higher than the maximum speed capability of the vehicle, or

because acceleration profiles are demanded which exceed the

capabilities of the powertrain components. Thus, these crossed

Table 2

Range of parameter variations explored for their impact on vehicle energy consumption.

Scooter 3-Wheeler Low power 4-wheeler High power 4-wheeler

Ancillary loading (i.e. A/C) (kW) 0.0–0.30 0.0–0.50 0.20–3.0 0.20–4.0

Vehicle mass (kg) 150–300 500–800 898–1200 1493–1800

Motor efficiency (%) 55–90 55–90 55–90 55–90

Fig. 5. Model validation: comparison of modeled and measured electrical con-

sumption for a Nissan Leaf.

Table 3

Electrical consumption range of each vehicle.

Electrical consumption (Wh/km)

Avg city Avg hwy Range

Scooter 33 38 31–40

3-Wheeler 61 85 53–97

Low power EV 84 133 70–192

High power EV 123 164 101–224


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out values should not be given much weight but instead simply

considered for reference.

The results in Fig. 6 show that electrical consumption per kilo-

meter is highest for the 4-wheelers and lowest for the electrical

scooter, which comes as no surprise given the differences in vehi-

cle mass. For the vehicles which are capable of sustaining highway

speeds (i.e. only the 4-wheelers), electrical consumption is signifi-

cantly higher for high speed highway driving.

4.2. Impact of parameter variations on vehicle electricity consumption

The electrical consumption was calculated for several vehicles

on several US and Indian drive cycles in Section 4.1, with specifica-

tions defined in Table 1. Vehicles on the road, however, will rarely

have exactly the same specifications as those defined in Table 1,

thus this section explores how different parameters will impact

electrical consumption of each vehicle.

4.2.1. Ancillary component and air conditioning loads

For hot climates like India, energy use by air conditioners will

have a significant impact on the electricity consumption of a vehi-

cle. Additionally, other ancillary components (like vehicle control

electronics, radio, and lights) will consume energy. Fig. 7 presents

the impact on vehicle electricity consumption from different levels

of loading by ancillary components. As two- and three-wheelers

typically do not have an enclosed cabin they will not have air con-

ditioners, and thus their maximum loading from ancillary compo-

nents will be lower. Thus, in Fig. 7 the modeled range of energy

consumption from ancillary components for the two- and three-

wheel vehicles is much lower than for the four-wheelers.

Fig. 7 shows that for each vehicle on all the different drive cy-

cles, vehicle electricity consumption (Wh/km) increases linearly

with increasing loading from ancillary components. It is particu-

larly important to note that the slope of this linear increase is dif-

ferent across the different drive cycles. The equation of fit for the

relationship between ancillary component loading and vehicle

electricity consumption follows the form of Eq. (1), where x is

Fig. 6. Electrical energy consumption rate for different types of EVs on different

drive cycles.

Fig. 7. Variation of vehicle electrical consumption with different ancillary compo-

nent loading.

Table 4

Coefficients for equation of fit for impact of ancillary component loading (kW) on vehicle electricity consumption (Wh/km).

UDDS HWFET US06 India urban India highway Delhi Pune MIDC

2 Wheeler m 42.35 48.13 61.62 57.59 40.59

b 32.83 28.85 30.40 28.84 33.97

R2 1.00 1.00 1.00 1.00 1.00

3 Wheeler m 36.47 45.85 22.75 59.28 55.40 34.42

b 65.70 50.62 67.15 46.94 50.46 69.80

R2 1.00 1.00 1.00 1.00 1.00 1.00

4 Wheeler low power m 35.30 15.43 15.73 47.11 23.72 60.64 56.86 34.49

b 87.86 117.48 189.49 67.81 80.78 57.03 69.33 89.80

R2 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

4 Wheeler high power m 34.22 14.21 14.70 46.14 22.88 59.57 55.73 33.38

b 128.27 142.42 220.64 112.28 118.40 88.84 113.99 124.40

R2 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Fig. 8. Variation of vehicle electrical consumption with different total vehicle

masses.



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the electrical loading in kW, m is the slope, and b is the y-intercept

(in this case, the electrical consumption if there was no loading

from ancillary components). Values for m and b for each vehicle

on each drive cycle are listed in Table 4.

y ¼ mx þ b ð1Þ

The fitting equation parameters in Table 4 shows that the slope

of each fitting equation for a given drive cycle is not sensitive to

vehicle type. The slopes are generally higher for lower speed driv-

ing conditions. These results suggest that ancillary component

loading has a greater impact on vehicle electricity consumption

at lower speed driving conditions (i.e. city driving), and is not sen-

sitive to vehicle type.

4.2.2. Variations in vehicle, passenger and cargo mass

Individual vehicles are bound to be loaded with different mass

due to variations in the number of passengers or cargo being car-ried. Fig. 8 shows the impact of variations in total vehicle mass

for each vehicle on each drive cycle. The 3- and 4-wheelers will

have greater carrying capacity and thus the range of vehicle masses

modeled is larger for these vehicles.

Fig. 8 shows that vehicle electricity consumption is also linearly

dependent on vehicle mass, with increased electricity consumption

for greater total vehicle mass. The equation of fit relating changes

in vehicle electrical consumption with changes in vehicle mass fol-

lows the form of Eq. (1) as well, with x being vehicle mass in kg.

Table 5 lists the coefficients for the equations of fit for variations

in vehicle mass. The fitting coefficients in Table 5 suggest that

the impact of vehicle mass on vehicle electricity consumption is

Table 5

Coefficients for equation of fit for impact of vehicle mass (kg) on vehicle electricity consumption (Wh/km).


2 Wheeler m 0.03 0.02 0.02 0.03 0.02

b 29.94 28.12 30.88 27.64 32.73

R2 1.00 0.95 0.95 0.98 0.96

3 Wheeler m 0.07 0.07 0.05 0.04 0.07 0.04

b 34.43 22.27 45.54 32.00 23.64 51.03

R2 1.00 1.00 1.00 1.00 1.00 1.00


b 30.87 77.49 127.74 18.62 31.96 32.06 22.60 48.43

R2 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00


b 44.05 83.86 114.44 31.90 42.89 39.43 37.38 60.27

R2 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Fig. 9. Variation of vehicle electrical consumption with different average motor

operating efficiency.

Table 6

Coefficients for equation of fit for impact of motor efficiency (%) on vehicle electricity consumption (Wh/km).


2 Wheeler m 54.11 50.93 49.53 51.55 54.34

b 75.97 69.54 70.89 69.97 77.54

R2 0.99 0.99 0.99 0.99 0.99

3 Wheeler m 125.9 106.2 118.2 74.5 99.9 118.0

b 165.3 135.6 161.4 110.2 131.3 164.6

R2 0.99 0.99 0.99 0.99 0.99 0.99


b 248.5 271.7 441.8 328.7 322.9 219.4 321.1 318.9

R2 0.99 0.99 1.00 0.99 0.99 0.99 0.99 0.99


b 361.7 326.1 568.6 328.7 322.9 219.4 321.1 318.9

R2 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99

Fig. 10. Powertrain architecture for electric vehicle models.


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fairly consistent across the different drive cycles and across the dif-

ferent vehicles (especially the 3-wheeler and both 4-wheelers).

4.2.3. Variations in average motor efficiency

The values chosen for the motor efficiency maps used for the

baseline vehicle simulations (in Section 4.1) were established to

fit the Nissan Leaf model validation results in Section 3.4. Electric

vehicles released in the Indian market, however, may use differenttypes of motors with different efficiency operating profiles, thus

this section explores the impact of changes is motor operating effi-

ciency. Fig. 9 shows the variation of vehicle electricity consump-

tion with average motor operating efficiency for the different

vehicles driving on the different drive cycles.

As expected, the results in Fig. 9 show that vehicle electricity

consumption decreases as a more efficient motor is used. A partic-

ularly interesting result, however, is that changes in motor effi-

ciency have very little impact on electricity consumption for the

smaller vehicles, especially the two-wheeler. This result is of sig-

nificant importance as it suggests that the use of less expensive

motors, which may be less efficient, can be used to lower the cost

of electric scooters while having minimal impact on vehicle elec-

tricity consumption (and thus vehicle range). For the larger vehi-

cles and for higher speed driving conditions (i.e. on highways),

however, motor efficiency impacts electrical consumption signifi-

cantly and thus better motors must be used. The results for the lar-

ger vehicles in Fig. 9 show that the relationship between vehicle

electricity consumption and motor efficiency is not perfectly linear

(i.e. a slight curvature can be seen on the plots), however the R2 fit-

ting parameters in Table 6 show that a linear equation of the form

of Eq. (1), with x being the average motor efficiency (%), produces a

good fit.

5. Conclusions

Given the ambitious targets for electric vehicle deployment in

India under the National Electric Mobility Mission Plan whichwas announced by the Government of India, there are significant

concerns with the impact that EV charging will have on an already

strained Indian electricity grid. This study is part of a larger effort

towards estimating the impact on the Indian electricity grid from

substantial deployment of EVs on the Indian grid to subsequently

plan the deployment of new generation and distribution

infrastructure.

This study used detailed vehicle powertrain models to estimate

the per kilometer electrical consumption of several types of EVs,

including a scooter, a 3-wheeler, a low power 4-wheeler, and a

high power 4-wheeler. Electrical consumption data for scooters,

3-wheelers, and small 4-wheelers has previously been unavailable

in the literature, particularly for the Indian context where driving

conditions will be different than in developed countries and airconditioning load will be a significant factor. The powertrain model

methodology was validated against experimental measurements

for a Nissan Leaf. The main conclusions from this study are as

follows:

1. Average electrical consumption: Vehicle size has the greatest

impact on per km electrical consumption, followed by the

driving characteristics (i.e. city vs. highway driving). In city

driving conditions average electrical consumption results were:

33 Wh/km for the scooter, 61 Wh/km for the 3-wheeler, 84 Wh/

km for the low power 4-wheeler, and 123 Wh/km for the high

power 4-wheeler. For highway driving conditions average elec-

trical consumption results were: 133 Wh/km for the low power

4-wheeler, and 165 Wh/km for the high power 4-wheeler. The

scooter and 3-wheeler were incapable of sustaining highway

speeds. Readers are referred to Section 4.1 for a detailed break-

down of electrical consumption for different driving conditions.

2. Impact of air conditioners and ancillary component loads on elec-

trical consumption: Ancillary components have a significant

impact on electrical consumption, with per km electrical con-

sumption increasing linearly with greater ancillary component

loads. The slope of increasing electrical consumption is largerfor lower speed driving conditions (i.e. in cities), but is not sen-

sitive to vehicle type.

3. Impact of variations in vehicle mass on electrical consumption: Per

km electrical consumption also increases linearly with increas-

ing vehicle mass (i.e. for more passengers or cargo). The slope of

increase is fairly consistent across different driving conditions

and vehicle types.

4. Impact on variations in motor efficiency on electrical consumption:

Per km electrical consumption decreases linearly with greater

motor operating efficiency, however the slope of this decrease

is highly sensitive to vehicle size. An important finding is that

for smaller vehicles, like scooters, increasing motor efficiency

has little impact on electrical consumption. As a result, the

use of inexpensive and less efficient motors to minimize the

cost of electrical scooters will only have minimal impact on

electrical consumption and thus on EV range. For larger vehi-

cles, however, motor efficiency has a significant impact, with

more efficient motors allowing significantly reduced electrical

consumption. For larger vehicles there is also an impact of driv-

ing characteristics, with higher speed driving conditions show-

ing greater variation of electrical consumption with changes in

motor operating efficiency.

Acknowledgements

This work was supported by the Assistant Secretary of Policy

and International Affairs, Office of Policy and International Affairs,

of the US Department of Energy and the Regulatory Assistance

Project through the US Department of Energy under Contract No.

DE-AC02-05CH11231.

Appendix A.

This Appendix presents a brief description of the powertrain

and component models that are used to model the four types of

electric vehicles considered in this study. For a detailed description

of each model, readers are referred to the documentation associ-

ated with the commercially available powertrain modeling soft-

ware Autonomie, which was used in this study.

A.1. Overall powertrain architecture

The electric vehicle models in Autonomie include the compo-

nent models shown in Fig. 10, as well as an overarching propulsion

and brake control model.

The propulsion control model translates driver acceleration

commands, which are governed by the specified drive cycle, into

motor torque demands while simultaneously considering vehicle

and motor speed, battery state of charge, maximum torque output

before wheel slip at a given speed, and loading from ancillary

components.

The braking control model performs a similar function of trans-

lating driver braking commands, which are governed by the spec-

ified drive cycle, into braking torque demands while considering

several factors and constraints. One further function of the braking



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model is to specify the braking torque provided by the traction

motor and the mechanical brakes. In general, braking torque is

provided entirely by the traction motor until the motor or

battery power limits are encountered. Beyond these limits,

mechanical braking is used to absorb the remaining required brak-

ing torque.

A.2. Battery model

The battery model calculates the state of an individual cell and

assumes that all cells operate identically. Cell state of charge (SOC)

is calculated according to the coulomb counting approach in Eq.

(1):

SOC ¼

R I 3600

dt þ Ahinit

Ahmaxð1Þ

In Eq. (1), I is the charging or discharging current requested from

the battery, Ahinit is the amount of energy stored in the battery

when the model is initialized, and Ahmax is the maximum energy

storage capacity of the battery as a function of cell operating tem-

perature, as shown in Eq. (2):

Ahmax ¼ f ðT cellÞ ð2Þ

The values for Ahmax are specified in an initialization file using mea-

surement data for the maximum capacity of a cell that is discharged

at a C /5 rate.

The open circuit voltage and the internal resistances of the cell

on charging or discharging are determined as a function of SOC and

cell temperature, as shown in Eq. (3) through Eq. (5) respectively:

V OC ¼ f ðSOC;T cellÞ ð3Þ

Rint;chg ¼ f ðSOC;T cellÞ ð4Þ

Rint;dis ¼ f ðSOC;T cellÞ ð5Þ

Open circuit voltage and internal resistance data on charging anddischarging is specified in an initialization file based on experimen-

tally measured data. For lithium ion batteries, open circuit cell volt-

age typically spans a range from 3.5 to 4.2 V.

The cell output voltage at any given operating condition (i.e. at

the battery terminals) is calculated using Eq. (6) for charging and

Eq. (7) for discharging:

V out ;chg ¼ V OC gcoulI out Rint;chg ð6Þ

V out ;dis ¼ V OC I out Rint;dis ð7Þ

gcoul is the coulombic efficiency, which for these models is simplyset to 1.0.

A simple thermal model is included as part of the battery model

to estimate the cell operating temperature. The rate of heat gener-ation in a cell is calculated according to Eq. (8) while charging and

Eq. (9) while discharging:

_Q gen;chg ¼ I 2out Rint;chg V out I out ð1 gcoulÞ ð8Þ

_Q gen;dis ¼ I 2out Rint;dis ð9Þ

Heat dissipation is calculated by assuming a fan flows cooling air

across the cells within a pack. Eq. (10) is activated when the cell

temperature rises above a specified threshold to cause the battery

management system to turn the cooling fan on.

_Q cooling ¼ T module air T moduleThermal resistance

ð10Þ

In situations where the cooling fan remains off, Eq. (10) is simply setto zero. The module air temperature is calculated using Eq. (11):

T module air ¼ T air 1=2_Q cooling

_mcooling airC p;moduleð11Þ

Finally, the module temperature is calculated through the bal-

ance of heat generation and heat dissipation rates in Eq. (12),

and it is assumed that each cell within the module will have the

same temperature.

T cell ¼ T module ¼

R _Q gen _Q cooling

dt

mmoduleC p;moduleð12Þ

A.3. Motor model

The motor model provides the torque demanded by the propul-

sion controller, while taking into account the effects of losses and

rotor inertia. Motor temperature is used to determine the time that

the motor can spend above the maximum continuous rated torque

levels.

The maximum continuous torque is specified in an initialization

file for a full range of motor speeds according to experimental data.

The absolute maximum torque output is specified according to

a predefined value for continuous to peak torque ratio. The

motor efficiency map is specified for a full range of torque and

speed points in the initialization file using experimentally

measured data. The motor model inputs are the command to the

motor (i.e. required propulsion torque), the input voltage, and

the motor speed.

The maximum propulsion and regenerative torque capabilities

of the motor are determined as a function of motor speed. The

maximum torque map (as a function of speed) is specified in the

vehicle initialization file. The specified torque map enables maxi-

mum torque at low speeds (up to roughly 2000 RPM) and subse-

quently decaying maximum torque up to the high speed limits of

the motor.

Section 4.2.3 of this paper examines the impacts of different

levels of motor efficiency for the vehicles that are modeled. Motorefficiency is scaled by multiplying the efficiency map specified in

the initialization file by a scaling factor. The scaling factor is de-

fined as the ratio of desired maximum motor efficiency over the

maximum efficiency specified in the map defined in the initializa-

tion file.

A.4. Torque coupling, final drive and wheel model

The final drive and torque coupling models are functionally

similar, and serve to apply a fixed gear reduction ratio to both tor-

que and speed by taking into account the losses. The torque cou-

pling and final drive are assumed to be 97% efficient across the

entire torque/speed range.

The wheel model serves to transform rotational energy into lin-ear. Losses from mechanical braking and tire friction are calculated

within this model. Linear force exerted or absorbed by the tires is

calculated using Eq. (13):

F ¼ T =r wheels ð13Þ

In Eq. (13), T is the total input or output torque to the tires, and

r wheels is the wheel radius. Torque input or output is calculated

using Eq. (14):

T ¼ T in T braking T res ð14Þ

In Eq. (14), T in is the torque input from the vehicle powertrain,

T braking is the braking torque exerted by the mechanical brakes,

and T res is the resistive torque from tire rolling resistance which iscalculated using a third-order polynomial function of speed. The


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coefficients for the polynomial are specified in an initialization file

based on experimentally measured data.

A.5. Chassis model

By balancing the total powertrain output against the total

opposing forces, the linear acceleration and vehicle speed is finally

calculated at the chassis model. Powertrain output (or regenerative

input) is calculated at earlier sub-models based on the specified

drive cycle and the specified component parameters. Opposing

forces include factors such as hill climbing, aerodynamic losses,

and tire rolling resistance.

Aerodynamic losses are calculated within the chassis model

using Eq. (15):

F loss;aero ¼ 1=2qC d AV 2 ð15Þ

In Eq. (15), q is the density of air, Cd is the vehicle drag coefficient, A

is the frontal area of the vehicle, and V is the vehicle speed.

Opposing force from hill climbing is calculated using Eq. (16):

F loss;hill ¼ mg sinðhÞ ð16Þ

In Eq. (16), m is the vehicle mass, g is the acceleration from gravity,

and h is the hill grade.

The acceleration of the vehicle is subsequently calculated using

Eq. (17):

a ¼ F in F loss

mstatic þ mdynamic ð17Þ

In Eq. (17), F in is the input from the vehicle powertrain, F loss is the

sum of all opposing forces, mstatic is the static mass of the vehicle

and mdynamic is the dynamic mass of the vehicle from rotating com-

ponents. Vehicle speed is calculated by integrating Eq. (17) over

time.

A.6. Ancillary components models

Power losses from ancillary components (such as air condition-

ing and electronic in-vehicle equipment) are calculated as a spec-

ified continuous power draw. The power that is flowed to

ancillary components is assumed to travel through a power con-

verter which maintains its output voltage at the required voltage

input for ancillary components (i.e. 12 V). The power converter is

assumed to have 95% conversion efficiency.

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