Battery Aging Prediction In Electric Vehicle Application · Hybrid Electric Vehicles (HEV), Plug-in...

Battery Aging Prediction In Electric Vehicle Application

[email protected] © Tata Elxsi 2019 1

Electric Vehicle Battery Aging Prediction Methods

Manoz Kumar M Tirupati, Tata Elxsi



TABLE OF CONTENTS

INTRODUCTION ............................................................................................................................................. 3

DEFINITION ................................................................................................................................................... 5

BATTERY AGING PHENOMENA ..................................................................................................................... 6

ANODE ACTIVE MATERIAL ........................................................................................................................ 6

CATHODE ACTIVE MATERIAL .................................................................................................................... 6

ELECTROLYTE ............................................................................................................................................ 8

SEPARATOR ............................................................................................................................................... 8

CURRENT COLLECTOR ............................................................................................................................... 8

NEED FOR PREDICTION ................................................................................................................................. 9

BATTERY PERFORMANCE PREDICTION MODELS ........................................................................................ 11

EMPIRICAL MODEL .................................................................................................................................. 11

ELECTROCHEMICAL MODEL .................................................................................................................... 12

EQUIVALENT CIRCUIT MODEL ................................................................................................................. 13

PHYSICS-BASED MODEL .......................................................................................................................... 14

OUR APPROACH-THE EMPIRICAL METHOD ................................................................................................ 15

ASSUMPTIONS/LIMITATIONS .................................................................................................................. 16

INPUTS .................................................................................................................................................... 17

RESULTS & DISCUSSION .......................................................................................................................... 17

CONCLUSION ............................................................................................................................................... 20

FUTURE SCOPE ............................................................................................................................................ 20

ABOUT TATA ELXSI ...................................................................................................................................... 21

REFERENCES ................................................................................................................................................ 22



INTRODUCTION Energy storage systems, usually batteries are

essential for electric drive vehicles such as

Hybrid Electric Vehicles (HEV), Plug-in Hybrid

Electric Vehicles (PHEV) and Electric Vehicles

(EV). Different types of batteries are used in

electric vehicles such as lead-acid, nickel-

metal hydride (NiMH), zebra and lithium-ion

batteries. At present, lithium-ion batteries

(LIB) are most commonly used for a broad

range of electronic products and in the

automotive sector for energy storage.

Lithium-ion (Li-ion) batteries are an excellent option for primary energy storage devices as it is capable of

delivering a high power rate in a relatively small and lightweight package with low self-discharge rate and

no memory effect. The primary functional components of a lithium-ion battery are the positive and

negative electrodes and electrolyte (See Fig 2). Generally, the negative electrode of a conventional

lithium-ion cell is made of carbon. The positive electrode is a metal oxide and the electrolyte is a lithium

salt in an organic solvent.

Lithium-ion batteries are now considered to be the standard for modern battery electric vehicles. There

are many types of Lithium-ion batteries, each having different characteristics. Vehicle manufacturers are

Figure 1: Battery Electric Vehicle Architecture



however focused on variants that have a high energy and

power density with excellent durability. Lithium-ion

batteries offer many benefits compared to other mature

battery technologies. For example, it has excellent specific

energy (140 Wh/kg) and energy density, making it ideal for

battery electric vehicles. Lithium-ion batteries are also

excellent in retaining energy with a low self-discharge rate

(about 5% per month) which is an order of magnitude

lower than NiMH batteries. Lithium-ion batteries are now

considered to be the standard for modern battery electric

vehicles.

The commonly available types of Lithium-ion batteries in

the market are:

Lithium-Cobalt Oxide Battery

Lithium-Titanate Battery

Lithium-Iron Phosphate Battery

Lithium-Nickel Manganese Cobalt Oxide Battery and

Lithium-Manganese Oxide Battery

Figure 2: Cylindrical Cell Construction



DEFINITION Aging is the reliability and life span of a component or

a system. Lithium-Ion Batteries also deteriorate over

time. This gradual deterioration in its performance is

due to irreversible physical and chemical changes that

take place during its usage. These changes occur due to

variations in the operating temperature, current

demand and frequency and depth of charge and

discharge cycles. The aging process can occur while the

vehicle is running or charging (cyclic aging) or when idle

(calendric aging) as explained in Fig 3.

Battery aging results in a change in the operational

characteristics including a reduction in the capacity,

decrease in energy output, reduced performance and

efficiency. This degradation is reflected in the reduced

performance and range of electric vehicles.

State-of-Health (SoH) is an indicator that characterizes

the system parameter related to aging. An additional

parameter that defines the life of a battery is End-of-

Life (EoL). The EoL of a battery is reached when the

energy content or power delivery is not enough to

support the application.

The battery standards ISO 12405-1, ISO 12405-2 on

“test specifications for lithium-ion traction battery

packs and systems of electrically-propelled road

vehicles” and IEC 62660-1 on “performance testing of

secondary lithium-ion cells for the propulsion of

electric road vehicles” does not specify any EoL criteria.

A similar standard IEC 61982 on “performance and

endurance tests of secondary batteries (except lithium)

for the propulsion of electric road vehicles” defines EoL

as 80% of the nominal capacity.

Cyclic Aging (Driving & Charging Mode)

Cyclic aging is associated with utilization of the battery during operation of the electric vehicle, with the battery being subject to recurring charging and discharging cycles. The severity of cyclic aging depends on the load on the battery, operating temperature, depth of discharge and current rates.

Calendric Aging (Parking Mode)

Batteries tend to degrade when it is stored in the idle condition, independent of charge-discharge cycling. This irreversible process contributing to a loss in the capacity of the battery is termed Calendric Aging.

Figure 3: Cyclic and Calendric Aging



BATTERY AGING PHENOMENA The battery aging phenomenon occurs due to various factors that influence its structural and chemical

composition. The phenomena can be factored into the aging processes of the Anode, Cathode, Electrolyte,

Separator and Current Collectors. It is also understood that the major contribution is from the anode and

cathode.

Anode Active Material The negative electrode of the Li-Ion Batteries is commonly made of Graphite. The aging effects at the

graphite anode are attributed to the following -

Solid Electrolyte Interphase (SEI) Layer

Decomposition reactions tend to occur along the lithium intercalation when the cells are operated beyond

the thermodynamic stability of organic electrolytes. These products form films on the surface of the anode

active material (see Fig 4), termed SEI Layer. The SEI layer formed has low conductivity and its formation

consumes cyclable lithium leading to an irreversible capacity fade.

Over a period of time, the SEI layer penetrates into the pores of the electrode and the separator and

reduce the active surface area.

Lithium Plating

Lithium plating occurs when batteries are being charged. It occurs due to the reduction of lithium ions

dissolved in the electrolyte to metallic lithium at the surface of the anode active material. Some of this

plated lithium dissipates after charging and gets intercalated in the anode material, a portion reacts with

the electrolyte consuming cyclable lithium and resulting in a capacity fade.

Mechanical Stress

The intercalation and de-intercalation of lithium ions into graphite leads to volume changes in the active

material. This can lead to cracks in the SEI layer, weaker particle-to-particle contact and structural damage

to the graphite anode resulting in the increase in internal resistance and capacity fade.

Cathode Active Material The positive electrode of the Li-Ion Batteries is commonly made of lithium metal oxides like LiCoO2 or

LiMn2O4. The aging effects at the cathode are attributed to the following:

Structural Changes and Mechanical Degradation

Structural changes and phase transitions occur with electrochemical delithiation and lithiation of cathode

active material causing mechanical stresses. These mechanical degradations are typically accompanied by

an impedance rise.

Transition Metal Dissolution

The transition metals of the cathode active material tend to suffer from dissolution owing to high cathode

potentials and high temperatures. These dissolved metal ions migrate to the anode and intensify the SEI

growth essentially causing a reversible self-discharge.



Solid Permeable Interface (SPI) Formation

The electrolyte decomposition and formation of the surface film also occur at the cathode and are

referred to as a solid permeable interface. This electrolyte reduction at the cathode causes reintercalation

of lithium ions into the active material and causes self-discharge.

Figure 5: Cathode Aging Processes in Li-Ion Battery

Figure 6: Aging Contribution (Cyclic and Calendar Aging)



Electrolyte The electrolyte serves as a medium in transporting the positive lithium ions between the cathode and

anode on charge and in reverse on discharge. The most common electrolytes used in commercial Li-Ion

batteries are composed of one or more organic solvents and a salt. The preferred solvent for the

electrolyte in lithium-ion batteries is a combination of ethylene carbonate (EC) and dimethyl carbonate

(DMC) and the most common salt used is LiPF6.

The cycle life of rechargeable Li-Ion batteries depends on the long-term reversibility of cell chemistries,

which is influenced by the electrochemical stability of the electrolyte. The electrolyte is involved in

decomposition reactions leading to surface film formation at both electrodes and affect the ohmic

resistance of the lithium-ion cell. The properties of the SEI layers depend on the electrolyte composition,

additives, and impurities. The electrolyte reduction at the anode consumes cyclable lithium leading to a

capacity fade. The electrolyte oxidation at the cathode causes a reintercalation of lithium ions into the

cathode representing a self-discharge. Both types of electrolyte decomposition can be accompanied by a

release of gaseous reaction products and increase the internal cell pressure.

Separator The separator of a lithium-ion cell is a porous polymer foil filled with electrolyte present between the

anode and cathode. It acts as a catalyst that promotes the movement of lithium ions from cathode to

anode on charge and in reverse on discharge and also serves as an insulator preventing short circuits.

Although the porous separator of a lithium-ion cell is electrochemically inactive, it can affect the

performance of the lithium-ion cell considerably. The main aging mechanisms are

Clogging of pores in the separator due to the deposits from electrolyte decomposition which increases

ionic impedance.

Change in porosity and tortuosity of the separator due to mechanical stress.

Current Collector The current collectors are mainly subject to two degradation mechanisms.

The current collectors can be subject to electrochemical corrosion. It is particularly prevalent at the

aluminum current collector of the positive electrode when acidic species are present. This can lead to

increased contact resistance between the collector foil and the cathode active material. At the negative

electrode, the copper collector can dissolve under over-discharge conditions.

The other major degradation factor is mechanical stress which can deform the current collector foil. This

condition occurs during high current cycling when the intercalation and deintercalation of the lithium ions

can cause volume changes leading to local deformation. An effect of this volume change is the weakening

of the contact between the electrodes and can render certain regions ineffective and lead to a decrease

in capacity.



NEED FOR PREDICTION Extensive use of Lithium-Ion Batteries as energy storage devices in the Electric or Hybrid Electric Vehicles

subject to adverse operating conditions and high dynamic loads. These conditions hamper the long-term

usage of the battery and restrict the life of the battery.

The batteries also contribute to a major portion of the cost of the car, compelling the manufacturers to

ensure the battery life is maximized to reduce the operational cost. As a result, identifying aging and

degradation mechanisms in the battery and developing prognostic models to predict the health of the

battery is important.

The major factors necessitating the need for a robust and accurate aging model are listed below

• To develop a system for State-of-Health prediction and monitoring of Lithium-ion Batteries,

in order to attempt an extension of their life and avoid unexpected costly failures.

• These studies can help provide inputs regarding the sensitivity of various operating factors to

vehicle manufactures and help them develop better batteries.

• The aging model helps the Battery Management Systems (BMS) to operate more efficiently

and control the battery charging and discharging to enhance the life of the battery.

• It will also help the Electric Vehicle manufacturer provide ideal operating conditions for the

battery. A precise definition of the aging model may help to find the most efficient conditions

for long-term Lithium-ion Battery operation.

• Automotive OEM’s can decide the appropriate battery for their vehicle applications without

the need for extensive testing, thereby reducing the development cost and improving the

turnaround time.

Figure 7: Aging Mechanisms



The following parameters are indicators of/relate to the aging parameters and of interest during study of

battery aging.

End-of-Life (EOL) A battery used in automotive applications is said to have reached its EoL when the

capacity reduces to 80% of its original capacity as per IEC 61982.

State-of-Health (SoH) The SoH is defined as the ratio of the current capacity of the battery to its initial

capacity. The ratio of a rise in the internal resistance can also be accounted in the above definition.

Remaining Useful Life (RUL) The RUL is defined as the length of time from the present time to the end of

useful life.

Figure 8: Battery Life when Exposed to Different

Operating Conditions



BATTERY PERFORMANCE PREDICTION MODELS The prediction of battery performance is a multi-physics problem involving electrochemistry, electrical

and thermal models and spanning different time scales (transient response to long term aging simulations)

and length scales (electrode-level electrochemical to vehicle level system simulations). The aging

prediction model is developed to work with specific battery models.

Empirical Model

Empirical models are developed without the knowledge of the aging process at the material level. These

models implement a temperature and SoC dependent aging prediction model for Li-Ion batteries.

Empirical relations are formulated based on the behavior of the battery during calendric and cyclic aging

and tuned based on results from the bench test.

The algorithm thus developed can be used to predict the aging under various conditions, providing

valuable inputs to improve battery life.

The empirical model relies on operating temperature, load (charging/discharging) and depth of discharge

limits as inputs and predicts the remaining life. This model can be implemented conveniently in BMS due

to ease of use.

Time Scale

Length

Scale

Figure 10: Range of Time and Length Scale Models for Battery Simulations



Electrochemical Model This model is based on the electrochemistry of the cell along (Fig 12, 13) with the thermal and electrical

models. The deterioration in the cell composition due to aging is also obtained while solving this model

based on electrochemical principles. It can take in to account the aging due to various deterioration

mechanisms in the battery.

This battery model is generally formulated

to compute the voltage across the

terminals of the battery as output with the

current drawn from the battery as input.

The problem is simplified into a lumped

parameter ODE (Ordinary Differential

Equation) form to make it computationally

efficient while considering the main

electrochemical processes. This model is

also suited for different battery chemistries

by making minor modifications in the

parameters of the system.

The electrochemistry model generally

involves solutions to these ODEs using

various numerical techniques. The

robustness and accuracy of the solution will

also depend on the numerical method used

to obtain the solution. Electrochemical

models are high fidelity models and

difficult to implement in the control

systems.

Figure 11: Empirical Model Flow Chart

Figure 12: Cell Current During Discharge

Figure 13: Cell Current During Charge

Arr

ow

s (è

) d

eno

te t

he

Dir

ecti

on

of

Mo

tio

n o

f Li

+ Io

ns



The structure of this model is shown in Fig 14. It shows the interdependence of the thermal, electric,

electro-chemistry and aging model.

Equivalent Circuit Model One of the most common battery models in use is the equivalent circuit model (ECM). ECMs use networks

of electrical components such as voltage sources, capacitors and resistors to simulate the electrical

behaviour of lithium-ion batteries during operation. The ECM model should be able to simulate the actual

battery voltage under any current excitation.

However, some characteristics of the lithium-ion batteries cannot be well represented by circuit elements,

such as the hysteresis effect or the Warburg

effect (Fig 15). This demands modification in

the equivalent circuit to address these issues.

The addition of pure mathematical models

with hysteresis is one such approach used to

address the issue.

Two technical routes are usually used to

estimate SOC using ECM. The first method is

a simple way to estimate SOC directly

through ECM parameter identification. The

second method uses a predetermined SOC to

realize Open Circuit Voltage (OCV) and then

estimates the lithium-ion battery voltage in

operating conditions through ECM. Hence,

the SOC-OCV relationship is very important

Figure 14: Typical Process Flowchart for Electro-Chemical Model

I, Current

SoH, State of Health

SoC, State of Charge

V, Voltage

T, Temperature

Φ, Electric Potential

C, Concentration

R, Internal Resistance

Q, Capacity Fade

Figure 15: Components of Equivalent Circuit

Model with Aging



not only in OCV method estimation but also in model-based method estimation

Physics-Based Model The physics-based models are mathematical formulations that describe the behaviour of a pristine cell.

To account for aging factors, some of the model parameters like SEI film resistance or thickness, volume

fraction of active material, etc. are updated using some isolated empirical relations or curve-fitting

procedures.

The models describe mass and charge transfer in detail using partial differential equations based on the

Porous Electrode Theory and Spatially Uniform Models (Fig 16).

The differential equations solved in these are of the following nature:

• Li-Ion Diffusion in Solid Phase

• Li-Ion Diffusion in Liquid Phase

• Solid and Liquid Potential

• Intercalation Current Density and

• Over-potential

These models are computationally complex as they require the solution to a system of partial differential

equations. As a result, it also becomes difficult to implement these models for control oriented

applications.

Figure 16: Unit Cell and Active Material Representation for

Physics Based Models



OUR APPROACH-THE EMPIRICAL METHOD Tata Elxsi’s approach for predicting battery life is through an empirical model. This model was chosen to

reduce the development time and make it production-ready for implementation in Battery Management

Systems. The model is expected to predict the capacity decrease of the battery under various operating

conditions.

The parameter State-of-Health (SoH) is an indicator of the aging of the battery and is related to the

capacity decrease of the battery from its initial capacity.

𝑆𝑜𝐻 =𝐶𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒,𝑎𝑐𝑡𝑢𝑎𝑙

𝐶𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒,𝑖𝑛𝑖𝑡𝑖𝑎𝑙

However, this definition does not consider the increase in the internal resistance of the battery due to

aging. In this study, the SoH parameter is redefined to include the effect of an increase in the internal

resistance of the battery in the aging indicator. The modified definition of SoH is shown below.

𝑑𝑆𝑜𝐻 =𝜕𝑆𝑜𝐻

𝜕𝐶𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒,𝑎𝑐𝑡𝑢𝑎𝑙𝑑𝐶𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒,𝑎𝑐𝑡𝑢𝑎𝑙 +

𝜕𝑆𝑜𝐻

𝜕𝑅𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙𝑑𝑅𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙

The capacity fade due to aging of batteries is a complex process involving the change in the composition

of the electrodes and metal deposition. An empirical approach to estimate the age forms a balanced

theoretical and pragmatic approach to evaluate this problem and putting it into practical use. It should

also be observed that this model predicts the capacity fade due to cyclic aging and not calendric aging.

The empirical model relies on the evaluation of the aging parameters by investigating different current

rates, working temperatures, and depths of discharge from the test.

This aging model is integrated with a battery model developed in Matlab/Simulink along with components

from the Simscape and Power-Train Blockset library (see Fig 18). The aging model thus developed is

capable of integrating into the system model to predict the battery capacity fade and resistance increase

during BEV operation.

Figure 17: Structure of Freedom Car Battery Model

Figure 18: Aging Model integrated in Full

Vehicle Model



Key advantages of the Empirical method

• Takes into account all the significant operating conditions of the battery to estimate life

• The relatively good accuracy and mathematical simplicity of the model make it suitable for

implementing in control system/ battery management system.

Assumptions/Limitations The common Freedom Car battery model is adapted for defining the system model of a battery (Fig 17).

This model has the advantage of accounting for -

• Hysteresis during charging and discharging

• RC polarization and

• Evolution of the resistance during the life cycle

As mentioned earlier, it should also be noted that the integrated aging model requires data obtained from

the test bench. It will be unable to predict life before an actual battery is made and tested.

The main limitations of this model are -

a. The model is unaware of the electrochemical nature of the battery and hence requires tuning for

use in predicting aging in other battery chemistries

b. The model neglects aging phenomena due to calendric aging

c. The model relies on aging data obtained from battery tests under various ambient conditions.

This requires the availability of a battery test bench and climate-controlled chamber to simulate

the various load and temperature profiles

d. The empirical model is not capable of proving inputs to help design the battery but rather

concentrates on the influence of load conditions, operating conditions on the aging. It can thus

provide suggestions to improve battery life by improving the operating conditions of the battery.

Figure 19: Factors affecting Battery Life



Inputs Empirical models require calibration/tuning using data obtained in various tests conducted under

standard conditions. Our model requires the tests to be conducted in test benches in a climate-controlled

chamber. Standardized dynamic load profile and non-accelerated test conditions are used to test the

aging of the batteries. These conditions simulate the operating conditions of the battery in actual BEV as

compared to constant load profiles used in other models. The tests are based on the charge/discharge

cycles defined in the IEC 62660-1 standard but adapted to represent the BEV operating conditions (Fig

20).

The following modifications are made to represent more realistic operating conditions

• Test is performed at a temperature lower than 45°C as most batteries have a lower permitted

operating temperature.

• The IEC cycle discharges the battery up to 80% Depth of Discharge (DoD). This DoD extended

to 100% as such situations can arise during BEV operation.

• The micro-cycles are calibrated in current rates (C-Rate) as opposed to power rates defined

in the IEC cycle.

The various operating conditions under which the test is conducted shown in Fig 21.

Results & Discussion The battery model developed was used to identify aging parameters in a battery used in SUV applications

from an OEM. The IEC micro-cycle test data for aging was obtained from the manufacturer based on the

test requirements provided by Tata Elxsi. This data was used for calibrating our empirical model and used

to predict the aging behaviour in real driving conditions.

Initially, simulations were performed with constant loads for charging and discharging for validation of

the empirical model. These tests were carried out in test benches at controlled temperatures. The results

of the simulations matched the test performance within acceptable tolerance levels.

The next set of simulations was performed in the vehicle with fresh batteries which were run on the

chassis dynamometer. The vehicle was run in JC08 and WLTP Class-3 drive cycles and the battery life was

measured at constant intervals. The aging model was under predicting the capacity fade in the battery

during these tests. It was later identified that the temperature fluctuations were responsible for these

variations.

Figure 20: Modified IEC Micro-Cycle Figure 21: Test Parameters



The test setup was modified to ensure the battery temperatures are maintained within a user-specified

limit. The predicted results were closer to the measured values during this trial, confirming that the

variations were in fact caused by the high-temperature variation. The model was predicting the trends

accurately with the capacity reduction values within a reasonable tolerance, providing us confidence in

this model. This model can be used to improve the battery selection, operating conditions, and control

system to prolong the life of the battery.

Based on this model, it was decided to try and evaluate the life of the model under conditions replicating

the actual usage of the car in the city.

An Electric Vehicle usage pattern was synthesized based on literature, which tries to replicate the use of

the EV by a city-based user who commuted to and from work on the weekends and drives out of the city

during the weekends. The actual drive pattern was obtained from standard drive cycles (WLTP) and

modified to replicate this usage pattern.

The effect of temperature is captured in the simulation and results are shown in Fig 23. The range for real

driving conditions for over a week is predicted using this empirical approach. It can be seen that the

battery capacity fades faster at higher temperatures.

Figure 22: Representative Real Driving Pattern (City Based User)



Figure 23: Capacity Fade vs. Temperature

Figure 24: Results from Real Driving Condition Simulation (SoC, Maximum Capacity

and Equivalent Age)



CONCLUSION The empirical approach adopted for estimating the State of Health (SoH) of the battery is primarily due to

its suitability for deployment in the battery management system. SoH determination using these models

are computationally very efficient and easily implemented on embedded hardware. The battery

management system can rely on the estimated SoH to regulate the usage of the battery and improve

battery life. The reliability of this approach depends on the availability and accuracy of test data

performed under different operating conditions.

Since these models are ignorant about the internal construction and composition of the battery pack, they

cannot be used to obtain data to aid in the design of individual cells or selecting the cell composition.

Besides, the effort to generate the required data through testing is a time consuming and expensive

activity and requires the availability of specialized testbeds.

To summarize, the selection of the battery aging model is based on the end requirements of the user. The

high fidelity of the electrochemical models is helpful to battery manufacturers to optimize the cell level

composition and chemistry to improve cell performance. Low fidelity models are preferred by OEMs to

help optimize the usage environment of the existing batteries by supervising the usage levels and

providing appropriate cooling during operation.

FUTURE SCOPE The aging processes of lithium-ion batteries are complex and strongly dependent on operating

conditions. In addition, it is still difficult to quantify the different mechanisms involved in battery aging

as these mechanisms are correlated and cross-dependent. Therefore, obtaining a complete battery

diagnosis based on every possible aging factor and compatible with vehicle use is still a major remaining

challenge.

The focus needs to be set on finding the ideal balance between developing aging estimation

methods combined with real-time compatibility in order to be more accurate. To address this, Tata

Elxsi’s research team is working towards developing a comprehensive “Electrochemical Model” to

predict battery performance and aging effects. This high-fidelity model will involve solving a system of

differential equations for electrochemical, electrical and thermal behaviour. The dearth of tools capable

of solving such problems is a challenge and will demand the development of appropriate numerical

algorithms to solve such a problem. Such models will help automotive OEMs and battery manufacturers

in the design, development, and optimization of the battery, right from the concept level to the final

product.



ABOUT TATA ELXSI Tata Elxsi is a global design and technology services Company. Tata Elxsi works with leading Automotive

OEMs and Tier1 Suppliers and provides engineering and design services for Vehicle Electrification,

Connected Cars, Autonomous Driving.

Tata Elxsi offers customized R&D services spanning across the product’s lifecycle to automobile

manufacturers and component suppliers. Our industry experience in working with leading OEMs, Tier1

suppliers, tool and chip vendors, makes us the preferred partner for system and sub-system design for

the entire product lifecycle.

For more information on our solution and services, please visit www.tataelxsi.com

http://www.tataelxsi.com/



REFERENCES [1] H. Berg, “Batteries for Electric Vehicles”, Cambridge University Press, 2015.

[2] B. Balagopal and M.-Y. Chow, “The State of the Art Approaches to Estimate the State of Health (SOH)

and State of Function (SOF) of Lithium-Ion Batteries”, 13th IEEE INDIN, Cambridge, UK, 2015.

[3] S. Santhanagopalan, R. White, J. “Power Sources” , 2006

[4] Alexander Bartlett, James Marcicki, Simona Onori, Giorgio Rizzoni, Xiao Guang Yang, and Ted Miller,

“Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode

Lithium-Ion”, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 24, NO. 2, MARCH

2016

[5] Ryan Ahmed, Mohammed El Sayed, Ienkaran Arasaratnam, Jimi Tjong, and Saeid Habibi, “Reduced-

Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-

Ion Batteries”, IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 2,

NO. 3, SEPTEMBER 2014

[6] J. Evelina Wikner, “Lithium-ion Battery Aging:Battery Lifetime Testing and Physics-based Modeling for

Electric Vehicle Applications”, CHALMERS UNIVERSITY OF TECHNOLOGY Göteborg, Sweden 2017

[7] Alexander P. Schmidta, Matthias Bitzerb, Árpád W. Imrea, Lino Guzzella, “Model-based distinction and

quantification of capacity loss and rate capability”, Journal of Power Sources 195 (2010) 7634–7638

[8] Kandler A. Smith, “ELECTROCHEMICAL MODELING, ESTIMATION AND CONTROL OF LITHIUM-ION

BATTERIES”, Department of Mechanical and Nuclear Engineering, The Pennsylvania State University.

[9] Changfu Zou, Chris Manzie, and Dragan Nešic, “A Framework for Simplification of PDE-Based Lithium-

Ion Battery Models”, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

[10] A Czerepicki and M Koniak, “A method of computer modeling the lithium-ion batteries aging process

based on the experimental characteristics”, Warsaw University of Technology, Poland

[11] J. Christensen, J. Newman, J. Electrochem. Soc. 153 (6) (2006) A1019–A1030.

[12] X. Zhang, W. Shyy, A.M. Sastry, J. Electrochem. Soc. 154 (10) (2007) A910–A916.

[13] G. Ning, B. Haran, B.N. Popov, J. Power Sources 117 (2003) 160–169.

Battery Aging Prediction In Electric Vehicle Application · Hybrid Electric Vehicles (HEV), Plug-in...

Documents

Transcript of Battery Aging Prediction In Electric Vehicle Application · Hybrid Electric Vehicles (HEV), Plug-in...