基于自适应卡尔曼滤波的锂电池SOC估计

考虑到传统的卡尔曼滤波策略在未知干扰噪声环境下不能对锂离子电池的荷电状态(SOC)进行准确的估计,简要论述了锂离子电池的等效电路模型,提出了自适应卡尔曼滤波方法,利用Matlab/Simulink建立了基于自适应和常规的卡尔曼滤波法的锂离子电池SOC估计的仿真模型,分析研究了在未知干扰噪声下两种滤波法的SOC估计值变化曲线以及误差关系。仿真结果表明,采用自适应卡尔曼滤波方法估计的SOC误差较传统的要小,从而有效降低了未知干扰噪声对电池管理系统所受到的影响,且具有较好的鲁棒性,为今后深入研究动力电池SOC估计方法提供了一定的参考。

Estimation of state of charge of Li-ion battery based on adaptive Kalman filtering

Taking into account the traditional Kalman filtering strategy that can not estimate state of charge (SOC) of lithium-ion battery accurately in the unknown environment interference noise,the equivalent circuit model of lithium-ion battery was discussed,and an adaptive Kalman filtering method was put forward.The lithium-ion battery SOC estimation simulation model was built by using Matlab/Simulink based on adaptive Kalman filtering and conventional method.The SOC estimation value curve and error relationship of both filtering method under unknown interference noise was analyzed.The simulation results show that the SOC estimation error by adaptive Kalman filter method is much smaller than conventional method,thus effectively reducing the affection of unknown interference noise on the battery management system,and the method has a better robust,which provides a reference for study of battery SOC estimation method in the future.