Yang Shichun, Zhou Sida, Hua Yang, Zhou Xinan, Liu Xinhua, Pan Yuwei, Ling Heping, Wu Billy
School of Transportation Science and Engineering, Beihang University, Beijing, China.
BYD Auto Industry Co., Ltd, Shenzhen, 518118, China.
Sci Rep. 2021 Mar 11;11(1):5805. doi: 10.1038/s41598-021-84729-1.
An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.
在电池管理系统（BMS）中，准确的荷电状态（SOC）估计对于确保汽车电池的安全有效运行至关重要。然而，BMS一直存在SOC估计不准确的问题。在此，我们基于改进的扩展卡尔曼滤波器（IEKF）提出了一种兼具高精度和鲁棒性的SOC估计方法。建立了等效电路模型，并使用模拟退火粒子群优化（SA-PSO）算法进行离线参数辨识。此外，基于传统的EKF方法，在噪声自适应、渐消滤波器和线性-非线性滤波方面进行了改进，并相应地进行了严格的数学证明。为了处理模型失配问题，通过双卡尔曼滤波器实现了在线参数辨识。最后，进行了各种实验以验证所提出的IEKF。实验结果表明，在动态应力测试条件下，IEKF算法可将误差降低至2.94%，并且通过噪声干扰验证了鲁棒性分析，从而证明了其在扩展到实际运行条件下应用的电池组状态估计方面的实用性。
