首页|Unlocking the potential of unlabeled data:Self-supervised machine learning for battery aging diagnosis with real-world field data

Unlocking the potential of unlabeled data:Self-supervised machine learning for battery aging diagnosis with real-world field data

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Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in elec-tric vehicles.Despite significant advancements achieved by data-driven methods,diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data.This paper pre-sents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations.We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehi-cles.Our analysis comprehensively addresses cell inconsistencies,physical interpretations,and charging uncertainties in real-world applications.This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves.By leveraging inex-pensive unlabeled data in a self-supervised approach,our method demonstrates improvements in aver-age root mean square errors of 74.54％and 60.50％in the best and worst cases,respectively,compared to the supervised benchmark.This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in real-world scenarios.

Lithium-ion batteryAging diagnosisSelf-supervisedMachine learningUnlabeled data

Qiao Wang、Min Ye、Sehriban Celik、Zhongwei Deng、Bin Li、Dirk Uwe Sauer、Weihan Li

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National Engineering Research Center for Highway Maintenance Equipment,Chang'an University,Xi'an 710064,Shaanxi,China

Key Laboratory of Road Construction Technology and Equipment of MOE,Chang'an University,Xi'an 710064,Shaanxi,China

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

Center for Ageing,Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems(CARL),RW

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

Jülich Aachen Research Alliance,JARA-Energy,52074 Aachen,Germany

Helholtz Institute Münster(HI MS),Forschungszentrum Jülich,52425 Jülich,Germany

School of Automobile,Chang'an University,Xi'an 710064,Shaanxi,China

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

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2024

10.1016/j.jechem.2024.08.037

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)：2024.99(12)