基于频繁项统计的锂动力电池SOH估计  

SOH estimation of lithium-ion batteries based on frequent item statistics

作　　者：李俊丽 赵理[1,2] 客汉宸 LI Junli;ZHAO Li;KE Hanchen(School of Mechanical and Electrical Engineering,Beijing Information Science and Technology University,Beijing 100192,China;Collaborative Innovation Center of Electric in Beijing,Beijing 100192,China)

机构地区：[1]北京信息科技大学机电工程学院,北京100192 [2]北京电动车辆协同创新中心,北京100192

出　　处：《重庆理工大学学报（自然科学）》2023年第9期31-39,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(52077007);北京市教育委员会科技计划项目(KM201811232003);北京信息科技大学教改项目(2021JGYB02)。

摘　　要：快速而精确的估计电池健康状态(SOH)是保证动力电池系统安全的基础。纯电动汽车在运行过程中,传统的估计方法难以利用车载有限的计算资源,在线构建精确的SOH估计模型。针对该问题,提出一种利用短时监测数据在线构建电池健康指标(HI)的特征提取方法。该方法将不同电压区间内积累的电量看作频繁项,利用Lossy Counting算法构建概要数据结构对频繁项分布进行统计,基于频繁项分布规律的变化对电池健康状态进行表征。仿真和实验结果表明,该方法能利用车载计算资源,以较小的时间和空间复杂度提取电池健康状态指标。Rapid and accurate estimation of battery state of health(SOH)is the basis for ensuring the safety of power battery systems.During the operation of pure electric vehicles,traditional estimation methods are difficult to use the limited computing resources of the vehicle to construct accurate SOH estimation models online.To solve this problem,an online feature extraction method using short-term monitoring data to construct battery health indicators(HI)is proposed.In this method,the accumulated electricity in different voltage ranges is regarded as frequency items,and the Lossy Counting algorithm is used to construct a summary data structure to perform statistic on the distribution of frequency items,and the battery health status is characterized based on the change of the distribution law of the frequency items.Simulation and experimental results show that the proposed method can use on-board computing resources to extract battery health status indicators with small time and space complexity.

关 键 词：锂离子电池 SOH估计 健康因子(HI) Lossy Counting算法 

分 类 号：TM911[电气工程—电力电子与电力传动]