基于频率解耦和双卡尔曼滤波的锂离子电池参数辨识  

Parameter Identification of Lithium-ion Batteries Based on Frequency Decoupling and Double Kalman Filtering

作　　者：龚瑞云 王学梅[1] 康龙云[1] Gong Ruiyun;Wang Xuemei;Kang Longyun(School of Electric Power,South China University of Technology,Guangzhou Guangdong 510641,China)

机构地区：[1]华南理工大学电力学院,广东广州510641

出　　处：《电气自动化》2024年第6期73-75,78,共4页Electrical Automation

摘　　要：锂离子电池内部的复杂动力学过程发生在毫秒到百秒的时间尺度上。忽略电池的动力学过程和缺乏解耦机制的辨识算法可能引起参数间的交叉干扰,使辨识结果缺少物理意义。为此,提出了一种基于频率解耦和双卡尔曼滤波的参数辨识算法。将电池模型分为快速动态部分和慢速动态部分,使用高通滤波器将快速动态响应频率解耦,通过双卡尔曼滤波算法在不同时间尺度上辨识慢速动态参数。结果表明,算法的电压动态跟踪能力强,均方根误差最低为0.83 mV。参数辨识结果具有合理的电化学动力学意义,且不同工况下的结果一致性高,证明了算法的可行性和有效性。The complex dynamic process inside lithium-ion batteries occurs on the time scale of milliseconds to hundreds of seconds.Neglecting the dynamic process of batteries and lacking decoupling mechanisms in identification algorithms may cause cross interference among parameters,resulting in identification results lacking physical significance.A parameter identification algorithm based on frequency decoupling and dual Kalman filtering was proposed for the purpose.The battery model was divided into the fast dynamic part and the slow dynamic part,the fast dynamic response frequency was decoupled by use of a high pass filter,and the slow dynamic parameters at different time scales were identified through dual Kalman filtering algorithm.The results show that the algorithm has strong voltage dynamic tracking ability,with the lowest root mean square error of 0.83 mV.The parameter identification results have reasonable electrochemical kinetic significance,and the high consistency of the results under different operating conditions proves the feasibility and effectiveness of this algorithm.

关 键 词：锂离子电池 等效电路模型 参数辨识 频率解耦 双卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]