High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering

Abstract

SummaryHigh‐precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural network (particle swarm optimization ‐ backpropagation neural network ‐ double extended Kalman filter [PSO‐BPNN‐DEKF]) are combined to estimate and correct the SOC of lithium‐ion batteries, in which the initial weight and threshold of the BPNN are optimized by particle swarm optimization algorithm. Based on the second‐order RC equivalent circuit model, parameter identification is carried out using the adaptive forgetting factor least squares (AFFRLS) method. Online parameter updates and SOC estimation are realized by DEKF algorithm. Then, the trained PSO‐BPNN is used to predict the SOC estimation error in real time, and the SOC estimation value is corrected by adding prediction errors. The SOC estimates before and after correction under Beijing Dynamic Stress Test (BBDST), dynamic stress test (DST), and hybrid pulse power characterization (HPPC) were compared. Under BBDST, DST, and HPPC tests, the maximum errors of the corrected SOC estimates are 0.0107, 0.0090, and 0.0147, respectively. The root mean square error (RMSE) of the corrected SOC estimates decreased by 94.02%, 83.18%, and 88.03%, respectively, compared with the extended Kalman filtering (EKF). The mean absolute error (MAE) of the corrected SOC estimates remained around 0.1% for all the BBDST dynamic operating conditions at different temperatures. The experimental results demonstrate the accuracy, effectiveness, and temperature adaptability of the proposed algorithm for SOC estimation under complex conditions of lithium‐ion batteries.