Journal of Electrical Engineering and Automation
IRO Journals
Volume 6 — Issue 2 — June 2024

EV Battery Management using Adaptive Kalman Filter and ECC

https://doi.org/10.36548/jeea.2024.2.001
Danny Moses H L
Dept of EEE, CIT, Coimbatore, Tamil nadu, India
Soundarya L
Dept of EEE, CIT, Coimbatore, Tamil nadu, India
Sridhar R
Dept of EEE, CIT, Coimbatore, Tamil nadu, India
Yogesh Kumar S
Dept of EEE, CIT, Coimbatore, Tamil nadu, India
M. Packia Sudha
Dept of EEE, CIT, Coimbatore, Tamil nadu, India
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How to Cite

L, Danny Moses H, Soundarya L, Sridhar R, Yogesh Kumar S, and M. Packia Sudha. 2024. “EV Battery Management Using Adaptive Kalman Filter and ECC”. Journal of Electrical Engineering and Automation 6 (2): 82-97. https://doi.org/10.36548/jeea.2024.2.001.
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Keywords

— SoC
— EV
— Lithium-Ion Batteries
— Adaptive Kalman Filter
— Enhanced Coulomb Counting
Published: 09-05-2024

Abstract

This initiative addresses the critical concerns of enhancing battery management and easing the calculation of battery capacity in electric vehicles. By using Coulomb counting method, the State of Charge (SoC) of Electric Vehicle (EV) batteries is estimated by analysing real-time battery data through simulations and interpolation techniques. The primary objective is to offer precise SoC estimation to mitigate the range anxiety. Furthermore, this research proposes methods of estimating the SoC of Lithium-Ion batteries and an enhanced coulomb counting model with Adaptive Kalman Filter to estimate the state of charge with higher accuracy. This comprehensive approach seeks to address critical challenges in EV battery management contributing significantly to the electric vehicle technology for the society.

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