中图分类号：TM912
文献标志码：A
DOI: 10.16157/j.issn.0258-7998.223341
中文引用格式： 王祥，苏建徽，赖纪东，等. 基于AEKF的锂离子电池SOC估算[J]. 电子技术应用，2023，49(4)：57-62.
英文引用格式： Wang Xiang，Su Jianhui，Lai Jidong，et al. SOC estimation of lithium-ion battery based on AEKF[J]. Application of Electronic Technique，2023，49(4)：57-62.

SOC estimation of lithium-ion battery based on AEKF

Abstract： Aiming at the problem that the noise information is fixed when the extended Kalman filter(EKF) algorithm estimates the state of charge(SOC) of lithium-ion battery, resulting in low estimation accuracy, an adaptive extended Kalman filter(AEKF) algorithm with automatic matching of noise information covariance is proposed. Firstly, the parameters are identified based on the dual polarization (DP)equivalent circuit model of the battery, and an accurate equivalent model is established. Then, the variation of noise covariance matrix of EKF filtering algorithm and AEKF filtering algorithm and the estimation effect of battery SOC are compared under dynamic stress test(DST) conditions. The results show that AEKF filtering algorithm has higher estimation accuracy. Finally, several groups of different SOC initial deviations are set to verify the strong robustness of AEKF filtering algorithm in estimating battery SOC.

Key words : lithium-ion battery；state of charge estimation；dual polarization equivalent circuit model；adaptive extended Kalman filter