基于卡尔曼滤波修正算法的电池SOC估算

Estimation of battery SOC based on Kalman filter correction algorithm

电池荷电状态(SOC)的估算是电池管理系统的核心内容,SOC估算准确与否,将直接影响到电池管理系统的决策和控制。在结合开路电压法、安时法的基础上,充分利用扩展卡尔曼滤波法的修正功能,综合考虑电池充放电倍率、温度和充放电循环次数等因素对SOC估算的影响,提出了卡尔曼滤波修正算法,并将其应用在插电式混合动力汽车电池管理系统中。研究结果表明,卡尔曼滤波修正算法有效地解决了传统安时法无法估计SOC初值和误差累积,以及开路电压法需要电池静置无法做到在线估算SOC等问题,获得了更高的估算精度,为电池管理系统提供一种实用的SOC估算方案。

The estimation of state-of-charge was the key part of battery management system （BMS）. The control of BMS was influenced by the accurate estimation. Based on the combination of the open-circuit-voltage method and Ah counting method, the correction factor of the extended Kalman filter was used. Charging and discharging rate and battery temperature and battery aging problem were considered. Then the Kalman filter correction algorithm was proposed. Algorithm was applied in the BMS of plug-in hybrid electric vehicle. The result indicates that the Kalman filter correction algorithm can make a further correction of the SOC which is estimated by open-circuit-voltage method and Ah counting method, then the accuracy of SOC estimation in order to provide a practical solution for the battery management system is improved.