基于电池暂态响应分析的锂电池SOC估计  

Lithium battery SOC estimation based on battery transient response analysis

作　　者：李明维 张传远 宿剑飞 周杨林 王红军 LI Mingwei;ZHANG Chuanyuan;SU Jianfei;ZHOU Yanglin;WANG Hongjun(Beijing Guodiantong Network Technology Co.,Ltd.,Beijing 100192,China;Department of Electrical Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]北京国电通网络技术有限公司,北京100192 [2]清华大学电机工程与应用电子技术系,北京100084

出　　处：《电源技术》2021年第11期1431-1434,共4页Chinese Journal of Power Sources

基　　金：国家重点研发计划资助(2018YFC1902202)。

摘　　要：锂离子电池因快速充电和长循环寿命特性,在电动汽车、电网侧储能系统中应用广泛。精准的电池荷电状态(SOC)估计有助于保障系统可靠性,延长电池系统寿命,然而在考虑电池充电和放电以及复杂工况条件造成的电池内部复杂的化学和物理变化的条件下,完成精准电池SOC估计十分困难。通过脉冲激励电池的充放电暂态响应特性分析,辨识等效电路模型参数,建立电池不同工况条件下的动态电池模型,并采用扩展卡尔曼滤波方法对纠正参数辨识和OCV-SOC映射中的误差。以Arbin测试平台估算结果作为对标,验证了所提方法的有效性。Due to the fast charging and long cycle life,lithium ion battery cells are widely used in electric vehicles and grid-side energy storage systems.Accurate battery state of charge(SOC)estimation is vital to improve the system reliability and extend system lifetime.However,it’s not easy to complete this work under various working conditions with the consideration of complicated chemical and physical process caused by different charging and discharging rate.The transient response process of battery under power pulse excitation is identified and the equivalent circuit model(ECM)parameters are established.In addition,the extended Kalman filter are applied to correct parameters estimation and mapping relation of SOC-OCV.The SOC evaluation result of Arbin platform was used as benchmark to verify the proposed method.

关 键 词：锂离子电池模组 等效电路模型 电池SOC估计 参数识别 脉冲特性测试 

分 类 号：TM912[电气工程—电力电子与电力传动]