State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks  

作　　者：Yvxin He Zhongwei Deng Jue Chen Weihan Li Jingjing Zhou Fei Xiang Xiaosong Hu 

机构地区：[1]School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China [2]College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China [3]Chair for Electrochemical Energy Conversion and Storage Systems,Institute for Power Electronics and Electrical Drives(ISEA),RWTH Aachen University,Aachen 52074,Germany [4]Center for Ageing,Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems(CARL),RWTH Aachen University,Aachen 52074,Germany [5]China Automotive Engineering Research Institute Co.Ltd.,Chongqing 401122,China

出　　处：《Journal of Energy Chemistry》2024年第11期1-11,共11页能源化学（英文版）

基　　金：National Key Research and Development Program of China (Grant No. 2022YFE0102700);National Natural Science Foundation of China (Grant No. 52102420);research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV);China Postdoctoral Science Foundation (Grant No. 2023T160085);Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。

摘　　要：A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

关 键 词：Lithium-ion battery State of health estimation Feature extraction Graph convolutional network Long short-term memory network 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM912[自动化与计算机技术—控制科学与工程]