Electric Vehicle Battery Aging Prediction Methods Manoz Kumar M Tirupati, Tata Elxsi

Battery Aging Prediction In Electric Vehicle Application

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Battery Aging Prediction In Electric Vehicle Application

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

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Battery Aging Prediction In Electric Vehicle Application

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. Figure 1: Battery Electric Vehicle Architecture

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 [email protected] © Tata Elxsi 2019 3

Battery Aging Prediction In Electric Vehicle Application

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 Figure 2: Cylindrical Cell Construction  Lithium-Titanate Battery  Lithium-Iron Phosphate Battery  Lithium-Nickel Manganese Cobalt Oxide Battery and  Lithium-Manganese Oxide Battery

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Battery Aging Prediction In Electric Vehicle Application

DEFINITION Aging is the reliability and life span of a component or Cyclic Aging (Driving & Charging Mode) 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 Cyclic aging is associated with utilization of the efficiency. This degradation is reflected in the reduced battery during operation of the electric vehicle, with the battery being subject to recurring charging and performance and range of electric vehicles. discharging cycles. The severity of cyclic aging State-of-Health (SoH) is an indicator that characterizes depends on the load on the battery, operating the system parameter related to aging. An additional temperature, depth of discharge and current rates. parameter that defines the life of a battery is End-of- Calendric Aging (Parking Mode) 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. Batteries tend to degrade when it is stored in the idle A similar standard IEC 61982 on “performance and condition, independent of charge-discharge cycling. endurance tests of secondary batteries (except lithium) This irreversible process contributing to a loss in the capacity of the battery is termed Calendric Aging. for the propulsion of electric road vehicles” defines EoL as 80% of the nominal capacity. Figure 3: Cyclic and Calendric Aging

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Battery Aging Prediction In Electric Vehicle Application

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. [email protected] © Tata Elxsi 2019 6

Battery Aging Prediction In Electric Vehicle Application

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)

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Battery Aging Prediction In Electric Vehicle Application

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.

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Battery Aging Prediction In Electric Vehicle Application

Figure 7: Aging Mechanisms

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.

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Battery Aging Prediction In Electric Vehicle Application

Figure 8: Battery Life when Exposed to Different Operating Conditions

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.

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Battery Aging Prediction In Electric Vehicle Application

Time Scale Time

Length Scale Length

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

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.

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Battery Aging Prediction In Electric Vehicle Application

Figure 11: Empirical Model Flow Chart

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 Figure 12: Cell Current During Discharge 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 ) denote the Direction of Motion of Li+ Ions Li+ of Motion of Direction the denote ) also depend on the numerical method used è to obtain the solution. Electrochemical models are high fidelity models and difficult to implement in the control ( Arrows systems.

Figure 13: Cell Current During Charge

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Battery Aging Prediction In Electric Vehicle Application

The structure of this model is shown in Fig 14. It shows the interdependence of the thermal, electric, electro-chemistry and aging 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 14: Typical Process Flowchart for Electro-Chemical 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 15: Components of Equivalent Circuit Model with Aging [email protected] © Tata Elxsi 2019 13

Battery Aging Prediction In Electric Vehicle Application 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

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Battery Aging Prediction In Electric Vehicle Application

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.

퐶푑𝑖푠푐ℎ푎푟𝑔푒,푎푐푡푢푎푙 푆표퐻 = 퐶푑𝑖푠푐ℎ푎푟𝑔푒,𝑖푛𝑖푡𝑖푎푙 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. 휕푆표퐻 휕푆표퐻 푑푆표퐻 = 푑퐶푑𝑖푠푐ℎ푎푟𝑔푒,푎푐푡푢푎푙 + 푑푅𝑖푛푡푒푟푛푎푙 휕퐶푑𝑖푠푐ℎ푎푟𝑔푒,푎푐푡푢푎푙 휕푅𝑖푛푡푒푟푛푎푙

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 18: Aging Model integrated in Full Figure 17: Structure of Freedom Car Battery Model Vehicle Model [email protected] © Tata Elxsi 2019 15

Battery Aging Prediction In Electric Vehicle Application

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.

Figure 19: Factors affecting Battery Life

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.

Battery Aging Prediction In Electric Vehicle Application

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.

Figure 20: Modified IEC Micro-Cycle Figure 21: Test Parameters 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. [email protected] © Tata Elxsi 2019 17

Battery Aging Prediction In Electric Vehicle Application

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.

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

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.

Battery Aging Prediction In Electric Vehicle Application

Figure 23: Capacity Fade vs. Temperature

Figure 24: Results from Real Driving Condition Simulation (SoC, Maximum Capacity and Equivalent Age)

Battery Aging Prediction In Electric Vehicle Application

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.

Battery Aging Prediction In Electric Vehicle Application

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

Battery Aging Prediction In Electric Vehicle Application

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