State-of-Charge Estimation of Lithium-Ion Batteries Based on EKF Integrated With PSO-LSTM for Electric Vehicles
Hequan Xu, Qiang Xu, Fanchang Duanmu, Jingyi Shen, Ling Jin, Bin Gou, Fei Wu, Wei Zhang
Abstract
The battery management system (BMS) is integral to the electric vehicle (EV) energy system, primarily responsible for managing the battery state and accurately estimating its state-of-charge (SOC). The precision of SOC estimation is critical for the accurate projection of the EV’s driving range and the optimal control of battery charging. To address the limited accuracy and inadequate adaptability of the existing SOC estimation algorithms, this article proposes a pioneering approach that combines the extended Kalman filter (EKF) algorithm with particle swarm optimization (PSO) and long short-term memory (LSTM) models to precisely estimate the SOC of power batteries. Validation demonstrates that the joint estimation algorithm maintains a root-mean-square error (RMSE) within 0.258% and a maximum error below 1.559% across various standard operating conditions and on-vehicle road testing (OVRT), signifying its excellent accuracy and robustness.