首页|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

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

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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 estimat-ing 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 informa-tion 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 accu-racy 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.

Lithium-ion batteryState of health estimationFeature extractionGraph convolutional networkLong short-term memory network

Yvxin He、Zhongwei Deng、Jue Chen、Weihan Li、Jingjing Zhou、Fei Xiang、Xiaosong Hu

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School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China

College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China

Chair for Electrochemical Energy Conversion and Storage Systems,Institute for Power Electronics and Electrical Drives(ISEA),RWTH Aachen University,Aachen 52074,Germany

Center for Ageing,Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems(CARL),RWTH Aachen University,Aachen 52074,Germany

China Automotive Engineering Research Institute Co.Ltd.,Chongqing 401122,China

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2024

10.1016/j.jechem.2024.06.024

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.98(11)