Title: Ant colony assisted extended Kalman filter for estimation of state of charge of lithium-ion batteries in electric vehicles

Authors: Kannan Madhavan Namboothiri; K. Sundareswaran; P. Srinivasa Rao Nayak; Sishaj Pulikottil Simon; Mithun Thottankara

Addresses: Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

Abstract: State of charge (SoC) is an important index of batteries and its knowledge is mandatory for an effective battery management system (BMS). There are several methods adopted for SoC estimation ranging from model-based methods to model-free methods. Among these, the model-based method with an extended Kalman filter (EKF) has gained more attention by its high accuracy. The objective of this work is to optimally tune the error covariances of EKF and then estimate the SoC. A simplified version of ant colony optimisation (ACO) is employed for optimally tuning the error covariances of EKF. The proposed ACO-based approach is lucidly explained and SoC values for three different types of dynamic drive cycles of a lithium-ion battery are estimated. The experimental results revealed a reduction in root mean square error (RMSE) from 3.06% with the conventional approach to 1.59% with the proposed method. Further experiments with varying quantities of drive cycle data revealed that the drive cycle dataset does not need to be used in its entirety and that in most cases, one-third of the data is sufficient to optimally tune the EKF parameters.

Keywords: lithium-ion battery; state of charge; SoC; battery management system; BMS; extended Kalman filter; EKF; error covariance; ant colony optimisation; ACO.

DOI: 10.1504/IJEHV.2024.138987

International Journal of Electric and Hybrid Vehicles, 2024 Vol.16 No.2, pp.184 - 201

Received: 01 Apr 2023
Accepted: 06 Nov 2023
Published online: 06 Jun 2024 *

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