基于OCV曲面在线重构的电池容量衰退轨迹提取  

Method for online extraction of battery capacity trajectory based on OCV surface reconstruction

作　　者：裴磊 陈斌[1] 王天鸶 栗欢欢[1] PEI Lei;CHEN Bin;WANG Tiansi;LI Huanhuan(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang Jiangsu 212013,China;School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang Jiangsu 212013,China)

机构地区：[1]江苏大学汽车工程研究院,江苏镇江212013 [2]江苏大学汽车与交通工程学院,江苏镇江212013

出　　处：《电源技术》2024年第12期2452-2461,共10页Chinese Journal of Power Sources

基　　金：国家自然科学基金(52107225);江苏省自然科学基金(BK20210765);中国博士后科学基金(2020M681501);镇江市科技计划项目(CQ2022004)。

摘　　要：电池容量衰减轨迹的提取,对于电动汽车的安全与性能管理具有十分重要的意义。但现有方法仍需依赖大量的前期实验,建立起对应的理论或数据驱动模型,这严重限制了方法的工况普适性与参数迁移性。为此,提出一种基于循环次数-荷电状态-开路电压三维曲面(简称OCV曲面)大数据在线重构的容量轨迹在线提取方法。新方法以由多个离散OCV点组成的OCV曲线片段为研究对象,利用相同老化片段间的相容性与不同老化片段间的排异性,实现对各片段在老化与电量两个维度上的位置重排,以及对完整OCV曲面的在线重构。最终基于曲面的二维切片,完成电池容量轨迹的在线、免训获取。测试结果表明:该方法提取容量衰退轨迹结果的最大绝对百分比误差为3.51%,平均绝对百分比误差小于0.35%,均方根百分比误差小于0.70%,证明该方法在容量衰退轨迹提取方面具有良好的准确性。Extracting the capacity degradation trajectories of batteries is of importance for the safety and performance management of electric vehicles.However,existing methods still rely heavily on numerous preliminary experiments to establish corresponding theoretical or data-driven models,severely limiting the universality and parameter transferability of the methods.Therefore,this paper proposed a method for online extraction of capacity trajectories based on the online reconstruction of a three-dimensional surface of cycle count-state of charge-open circuit voltage(referred to as OCV surface)using big data.The method focused on discrete OCV fragments composed of multiple OCV points,utilizing the compatibility between same aging fragments and the incompatibility between different aging segments to rearrange the positions of each fragment in terms of aging and capacity dimensions,as well as reconstructing the complete OCV surface online.Finally,based on the two-dimensional slices of the surface,the online acquisition of battery capacity trajectories was achieved without the need for training.The test results demonstrate that the proposed method exhibits an excellent accuracy and reliability in extracting capacity trajectories,achieving a maximum absolute percentage error of 3.51%,a mean absolute percentage error(MAPE)below 0.35%,and a root mean square percentage error(RMSPE)under 0.70%.

关 键 词：开路电压 容量衰减 曲面重构 老化电池 离散片段 

分 类 号：TM912.9[电气工程—电力电子与电力传动]