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Home / Archives / Volume-6 / Issue-2 / Article-1

Volume - 6 | Issue - 2 | june 2024

EV Battery Management using Adaptive Kalman Filter and ECC
Danny Moses H L  , Soundarya L, Sridhar R, Yogesh Kumar S, M. Packia Sudha
Pages: 82-97
Cite this article
L, Danny Moses H, Soundarya L, Sridhar R, Yogesh Kumar S, and M. Packia Sudha. "EV Battery Management using Adaptive Kalman Filter and ECC." Journal of Electrical Engineering and Automation 6, no. 2 (2024): 82-97
DOI
10.36548/jeea.2024.2.001
Published
09 May, 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.

Keywords

SoC EV Lithium-Ion Batteries Adaptive Kalman Filter Enhanced Coulomb Counting

e-ISSN: 2582-3051
4 issues per year
DOI: https://doi.org/10.36548/jeea
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