Fast data augmentation for battery degradation prediction

Energy and AI, vol. 21, pp. 100542

Abstract

Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data. However, generating such data, whether in the laboratory or the field, is time- and resource-intensive. Here, we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization, aiming to augment the datasets used by data-driven models for degradation prediction. We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications. These datasets encompass various chemistries and realistic conditions, including cell-to-cell variations, measurement noise, varying charge-discharge conditions, and capacity recovery. Our results show that it is possible to reduce cell-testing efforts by at least 50% by substituting synthetic data into an existing dataset. This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.

Authors 7

1. Weihan Li corresponding Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

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

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany

2. Harshvardhan Samsukha Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

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

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany

3. Bruis van Vlijmen
4. Lisen Yan Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

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

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany

5. Samuel Greenbank

   University of Oxford

   Affiliation as printed

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

6. Simona Onori

   SLAC National Accelerator Laboratory · Stanford University

   Affiliation as printed

   Department of Energy Science and Engineering, Stanford University, Stanford, California 94305, USA

   SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA

7. Venkat Viswanathan

   University of Michigan

   Affiliation as printed

   Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA