20, July 2025

AI-Based Kalman Filter (KF) and Extended Kalman Filter (EKF) for SOC Estimation in Electric Vehicles: A Comprehensive Review

VOLUME-11; ISSUE-7; JULY-2025

Author(s): 1. Suwarna Shete, 2. Kamal Arora, 3. R.K.Kumawat

Authors Affiliations:

1.Career Point University, Kota, India,  2. Career Point University, Kota, India, 3. Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, India

 

DOIs:10.2015/IJIRMF/202507016     |     Paper ID: IJIRMF202507016


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Developing a battery management system for an electric vehicle (EV) remains a challenging task. Due to their low weight and high charge density, lithium-ion (Li-ion) batteries have emerged as the preferred battery for electric vehicle manufacturers. An intelligent battery management system (BMS) is crucial for EVs. Accurately estimating the state of charge (SOC) of a Li-ion battery is difficult due to its extremely unstable nature. The SOC estimation techniques for a Li-ion battery are thoroughly reviewed in this paper. The strengths, weaknesses, critical explanations, and estimation errors of this paper are presented. 

BMS, Li-ion, EV, SOC.

Suwarna Shete,  Kamal Arora,  R.K.Kumawat (2025); AI-Based Kalman Filter (KF) and Extended Kalman Filter (EKF) for SOC Estimation in Electric Vehicles: A Comprehensive Review , International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-7, Pp.99-103         Available on –   https://www.ijirmf.com/

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