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

Journal of Energy Chemistry, vol. 99, pp. 681–691

Abstract

Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric 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 presents 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 vehicles. 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 inexpensive unlabeled data in a self-supervised approach, our method demonstrates improvements in average 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.

Authors 7

1. Qiao Wang Aachen Center for Ageing Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA)

   Chang'an University · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

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

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

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

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

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

2. Min Ye

   Chang'an University

   Affiliation as printed

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

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

3. Sehriban Celik Aachen Center for Ageing Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA)

   Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

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

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

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

4. Zhongwei Deng Aachen Center for Ageing Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA)

   University of Electronic Science and Technology of China · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

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

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

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

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

5. Bin Li corresponding Aachen Center for Ageing Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA)

   Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

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

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

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

6. Dirk Uwe Sauer
7. Weihan Li corresponding Aachen Center for Ageing Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA)

   Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

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

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

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