动力锂电池SOC估计仿真研究  

Simulation Study on Dynamic Lithium Battery SOC Estimation

作　　者：贾海峰 李聪 JIA Hai-feng;LI Cong(College of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620

出　　处：《计算机仿真》2021年第5期55-59,228,共6页Computer Simulation

基　　金：国家自然基金资助项目(51505275)。

摘　　要：针对传统的无迹卡尔曼滤波算法(UKF)估计动力锂电池荷电状态(SOC)时,由于滤波迭代过程中系统噪声不确定,可能导致估计结果精度欠佳的问题,提出一种改进的自适应无迹卡尔曼滤波算法(AUKF)动态地估计锂离子电池的SOC。算法以UKF算法为基础,引入改进的Sage-Husa自适应滤波算法,利用观测数据进行滤波递推的同时,实时更新系统噪声的统计特性。以等效电路模型为基础,采用递推最小二乘法辨识模型参数,应用AUKF算法对电池SOC进行估算,并从实际工况进行仿真验证分析。仿真结果表明,上述算法有效的提高了估计精度,误差稳定性较高。Aiming at the problem that the traditional Unscented Kalman Filter(UKF) can estimate the SOC of lithium-ion battery dynamically due to the uncertainty of system noise in the iterative process of filtering, an improved Adaptive Unscented Kalman Filter(AUKF) was proposed to estimate the SOC of lithium-ion battery dynamically. Based on the UKF algorithm, an improved Sage-Husa adaptive filtering algorithm was introduced to update the statistical characteristics of system noise in real-time while filtering recursion was carried out with the observed data. Based on the equivalent circuit model, the recursive least square method was adopted to identify model parameters, the AUKF algorithm was applied to estimate battery SOC, and simulation verification analysis was carried out from actual working conditions. Simulation results show that the above algorithm can effectively improve the estimation accuracy and the error stability is high.

关 键 词：动力锂电池 等效电路模型 荷电状态 自适应无迹卡尔曼滤波 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]