Forecasting battery capacity and power degradation with multi-task learning

Energy storage materials, vol. 53, pp. 453–466

Abstract

Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is challenging due to intrinsic manufacturing variances and coupled nonlinear ageing mechanisms. In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately at early-life stage. The validation shows an average percentage error of 2.37% and 1.24% for the prediction of capacity fade and resistance rise, respectively. The model's ability to accurately predict the degradation, facing capacity and resistance estimation errors, further demonstrates the model's robustness and generalizability. Compared with single-task learning models for forecasting capacity and power degradation, the model shows a significant prediction accuracy improvement and computational cost reduction. This work presents the highlights of multi-task learning in the degradation prognostics for lithium-ion batteries.

Authors 5

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, Aachen 52066, Germany

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

2. Haotian Zhang 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, Aachen 52066, Germany

3. Bruis van Vlijmen

   SLAC National Accelerator Laboratory · Stanford University

   Affiliation as printed

   Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, United States

   SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States

4. 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, Aachen 52066, Germany

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

5. 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, Aachen 52066, Germany

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

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