基于弛豫电压的锂离子电池内短路分级检测方法  

Internal Short Circuit Classification and Detection Methods Based on Relaxation Voltage for Lithium-ion Batteries

作　　者：丘恒越 田凌 旷永红[3] QIU Hengyue;TIAN Ling;KUANG Yonghong(School of Automation,Guangdong University of Technology,Guangzhou,Guangdong 510006,China;Southern Power Grid Energy Storage Co.,Ltd.,Guangzhou,Guangdong 510630,China;College of Electrical and Information Engineering,Hunan Institute of Engineering,Xiangtan,Hunan 411104,China)

机构地区：[1]广东工业大学自动化学院,广东广州510006 [2]南方电网储能股份有限公司,广东广州510630 [3]湖南工程学院电气与信息工程学院,湖南湘潭411104

出　　处：《广东电力》2024年第7期32-41,共10页Guangdong Electric Power

基　　金：湖南省教育厅重点项目(23A0525)

摘　　要：为了提高锂离子电池在运行过程中的内短路故障检测效率,提出基于弛豫电压的锂离子电池内短路分级及检测方法。首先根据内短路电池暂停放电后的弛豫电压曲线特征,基于支持向量机模型对电池内短路等级进行分类。然后根据分类结果,提出相应的电池内短路故障检测方法。对于中期内短路电池,立即令其退出电池系统;对于早期内短路电池,利用卡尔曼滤波(Kalman filtering,KF)算法实时计算电池荷电状态(state of charge,SOC)偏差;对于无短路电池,保持原检测措施。最后对所提分类及检测方法进行实验验证。实验结果表明该分类方法的正确率受弛豫电压序列的采样总时间长度和采样间隔时间影响,增加恒流恒压充电阶段获取的特征数据能进一步提高内短路分类结果的正确率,实时检测电池SOC偏差的方法能及时发现异常的早期内短路电池。To improve the efficiency of detecting internal short circuit defects of the lithium-ion battery during operation,this paper proposes a method of lithium-ion battery internal short circuit classification and detection based on relaxation voltage.Firstly,according to the characteristics of relaxation voltage curves acquired after paused discharging of the the internal short circuit battery,the battery internal short circuit levels are classified based on a support vector machine(SVM)model,and the corresponding internal short circuit detection method is proposed according to the classification results.For the battery in the middle short circuit level,it is required to exit the battery system immediately.If the battery is in the early short circuit level,its state of charge(SOC)deviation is calculated in real time based on the Kalman filter(KF)algorithm.If the battery is normal,the original detection measures are maintained.Finally,the paper verifies the proposed classification and detection methods through simulation experiments.The experimental results show that the classification accuracy is influenced by the relaxation voltage duration and sampling interval.The classification accuracy can be enhanced by adding the characteristic data obtained during the constant current and constant voltage charging.The real-time SOC deviation detection method enables the prompt identification of abnormal early internal short circuit batteries.

关 键 词：锂离子电池 内短路 弛豫电压 荷电状态 卡尔曼滤波 

分 类 号：TM911[电气工程—电力电子与电力传动]