Rapid health estimation of in-service battery packs based on limited labels and domain adaptation  

作　　者：Zhongwei Deng Le Xu Hongao Liu Xiaosong Hu Bing Wang Jingjing Zhou 

机构地区：[1]School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China [2]School of Sustainability,Stanford University,Stanford,CA 94305,USA [3]College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China [4]China Automotive Engineering Research Institute Co.Ltd.,Chongqing 401122,China

出　　处：《Journal of Energy Chemistry》2024年第2期345-354,I0009,共11页能源化学（英文版）

基　　金：supported in part by the National Natural Science Foundation of China,China(Grant No.52102420);the National Key Research and Development Program of China,China(Grant No.2022YFE0102700);the China Postdoctoral Science Foundation,China(Grant No.2023T160085)。

摘　　要：For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.

关 键 词：Lithium-ion battery Electric vehicles Health estimation Feature extraction Convolutional neural network Domain adapatation 

分 类 号：TM912[电气工程—电力电子与电力传动] U469.72[机械工程—车辆工程]