Large-scale field data-based battery aging prediction driven by statistical features and machine learning

Cell Reports Physical Science, vol. 4, pp. 101720

Abstract

Accurately predicting battery aging is critical for mitigating performance degradation during battery usage. While the automotive industry recognizes the importance of utilizing field data for battery performance evaluation and optimization, its practical implementation faces challenges in data collection and the lack of field data-based prognosis methods. To address this, we collect field data from 60 electric vehicles operated for over 4 years and develop a robust data-driven approach for lithium-ion battery aging prediction based on statistical features. The proposed pre-processing methods integrate data cleaning, transformation, and reconstruction. In addition, we introduce multi-level screening techniques to extract statistical features from historical usage behavior. Utilizing machine learning, we accurately predict aging trajectories and worst-lifetime batteries while quantifying prediction uncertainty. This research emphasizes a field data-based framework for battery health management, which not only provides a vital basis for onboard health monitoring and prognosis but also paves the way for battery second-life evaluation scenarios.

Authors 6

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

   Beijing Institute of Technology · RWTH Aachen University

   Affiliation as printed

   Beijing Co-innovation Center for Electric Vehicles, Beijing 100081, China

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany

   Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany

   National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

2. Zhenpo Wang corresponding

   Beijing Institute of Technology

   Affiliation as printed

   Beijing Co-innovation Center for Electric Vehicles, Beijing 100081, China

   National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

3. Peng Liu

   Beijing Institute of Technology

   Affiliation as printed

   Beijing Co-innovation Center for Electric Vehicles, Beijing 100081, China

   National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

4. Lei Zhang

   Beijing Institute of Technology

   Affiliation as printed

   Beijing Co-innovation Center for Electric Vehicles, Beijing 100081, China

   National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

5. Dirk Uwe Sauer Aachen Center for Ageing Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA)

   Forschungszentrum Jülich · Helmholtz-Institute Münster · 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, Campus-Boulevard 89, 52074 Aachen, Germany

   Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany

   Helmholtz Institute Münster (HI MS), IEK 12, Forschungszentrum Jülich, 52425 Jülich, Germany

   Jülich Aachen Research Alliance, JARA-Energy, Templergraben 55, 52056 Aachen, Germany

6. 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, Campus-Boulevard 89, 52074 Aachen, Germany

   Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany

   Jülich Aachen Research Alliance, JARA-Energy, Templergraben 55, 52056 Aachen, Germany