SOC Estimation for Automotive Power Battery Based on
FA-DSAEKF Algorithm

Vehicle Engine ›› 2026, Vol. 0 ›› Issue (1) : 71-80.

SOC Estimation for Automotive Power Battery Based on FA-DSAEKF Algorithm

KANG Hengxin¹,WANG Jiguang²,XU Jianzhong³,TAN Zefei¹,LI Jiaqiang¹,YI Qiankun¹

Author information +

History +

Abstract

To address the issues of slow convergence, low accuracy and poor robustness in the estimation of state of charge (SOC) for automotive power batteries using the extended Kalman filter (EKF), a dual-symmetric adaptive extended Kalman filter method optimized by firefly algorithm (FA-DSAEKF) was proposed. Based on the EKF algorithm, the initial parameters were intelligently optimized, the symmetry and stability of the algorithm were enhanced, and the noise covariance matrix was adaptively adjusted using dual parameters, significantly improving the SOC estimation performance. The experimental results show that under different operating conditions, temperatures and initial states, the algorithm can converge quickly and stably with maximum absolute error, root mean square error, and mean absolute error all below 0.28%, and convergence time within 200 seconds. Compared to the traditional EKF algorithm, the estimation error is reduced by about 80%, and compared to the DSAEKF algorithm, the convergence speed is increased by over 83%, demonstrating excellent accuracy, adaptability, and robustness.

Key words

automotive power battery / state of charge / extended Kalman filter / equivalent circuit model / firefly algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

KANG Hengxin, WANG Jiguang, XU Jianzhong, TAN Zefei, LI Jiaqiang, YI Qiankun. SOC Estimation for Automotive Power Battery Based on FA-DSAEKF Algorithm[J]. Vehicle Engine. 2026, 0(1): 71-80