One-shot battery degradation trajectory prediction with deep learning

Journal of Power Sources, vol. 506, pp. 230024

Abstract

The degradation of batteries is complex and dependent on several internal mechanisms. Variations arising from manufacturing uncertainties and real-world operating conditions make battery lifetime prediction challenging. Here, we introduce a deep learning-based battery health prognostics approach to predict the future degradation trajectory in one shot without iteration or feature extraction. We also predict the end-of-life point and the knee-point. The model correctly learns about intrinsic variability caused by manufacturing differences, and is able to make accurate cell-specific predictions from just 100 cycles of data, and the performance improves over time as more data become available. Validation in an embedded device is demonstrated with the best-case median prediction error over the lifetime being 1.1% with normal data and 1.3% with noisy data. Compared to state-of-the-art approaches, the one-shot approach shows an increase in accuracy as well as in computing speed by up to 15 times. This work further highlights the effectiveness of data-driven approaches in the domain of health prognostics.

Authors 6

1. Weihan Li corresponding Aachen 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

   Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jägerstraße 17-19, 52066, Aachen, Germany

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

2. Neil Sengupta Aachen Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

   Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jägerstraße 17-19, 52066, Aachen, Germany

3. Philipp Dechent Aachen 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

   Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jägerstraße 17-19, 52066, Aachen, Germany

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

4. David A. Howey

   University of Oxford · The Faraday Institution

   Affiliation as printed

   Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK

   The Faraday Institution, Harwell Campus, Didcot, OX11 0RA, UK

5. Anuradha M. Annaswamy

   Massachusetts Institute of Technology

   Affiliation as printed

   Department of Mechanical Engineering, Massachusetts Institute of Technology, 33 Massachusetts Ave, MA, 02139, Cambridge, USA

6. Dirk Uwe Sauer Aachen 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

   Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jägerstraße 17-19, 52066, 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