摘要： 针对拓展卡尔曼滤波（Extended Kalman Filter， EKF）算法进行锂离子电池荷电状态（State of Charge， SOC）估算时噪声信息固定从而导致估算精度低的问题，提出噪声信息协方差能够自动匹配的自适应拓展卡尔曼滤波（Adaptive Extended Kalman Filter， AEKF）算法。首先基于电池的双极化（Dual Polarization， DP）等效电路模型进行参数辨识，建立精确的等效模型；然后在动态应力测试（Dynamic Stress Test， DST）工况下对比了EKF滤波算法与AEKF滤波算法噪声协方差矩阵变化情况以及对电池SOC的估算效果，结果表明AEKF滤波算法具有更高的估算精度；最后设置了几组不同的SOC初始偏差，验证了AEKF滤波算法在估算电池SOC时具有鲁棒性强的优点。

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