State of health estimation for lithium-ion batteries in real-world electric vehicles  

作　　者：WU Ji FANG LeiChao DONG GuangZhong LIN MingQiang 

机构地区：[1]School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei 2300o9,China [2]Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province,Hefei 230009,China [3]School of Mechanical Engineering and Automation,Harbin Institute of Technology,Shenzhen 518055,China [4]Quanzhou Institute of Equipment Manufacturing,Fujian Institute of Research on the Structure of Matter,Chinese Academy of Sciences,Jinjiang362200,China

出　　处：《Science China(Technological Sciences)》2023年第1期47-56,共10页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos. 61903114 and 62203423);the Anhui Provincial Natural Science Foundation (Grant No. 2008085QF301);the Youth Science and Technology Talents Support Program (2020) by Anhui Association for Science and Technology (Grant No. RCTJ202008);the Fundamental Research Funds for the Central Universities (Grant No. JZ2021HGTB0076);the Education and Scientific Research Project for Young and Middleaged Teachers in Fujian Province (Grant No. JAT201276)。

摘　　要：The state of health(SOH) plays a significant role in the mileage and safety of an electric vehicle(EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for SOH estimation. However, most of these proposed methods cannot be applied to real-world EVs. Here, we present a method for SOH estimation based on realworld EV data. A battery-aging evaluation health index(HI) with a strong correlation to the SOH is retrieved from battery-aging data and then modified with thermal factors to depict the former SOH. Afterward, a local weighted linear-regression algorithm is used to qualitatively characterize the declining trend of the HI, which eliminates the local HI fluctuation caused by data noise.Subsequently, a series of features-of-interest(FOIs) is extracted according to the battery consistency, cell-voltage extrema, and cumulative mileage, and validated using the grey relational analysis. Finally, a battery-degradation model is built using the extreme gradient-boosting algorithm with the selected FOIs. The experimental results from real-world data indicate that the proposed method has high estimation accuracy and generalization, and the maximum error is around 2% for batteries in realworld EVs.

关 键 词：electric vehicle state of health extreme gradient boosting battery consistency 

分 类 号：TM912[电气工程—电力电子与电力传动]