Learning Battery Model Parameter Dynamics From Data With Recursive Gaussian Process Regression

Journal of Dynamic Systems Measurement and Control, vol. 147

Abstract

Abstract Estimating the state of health is a critical function of a battery management system, but remains challenging due to variability of operating conditions and usage requirements in real applications. As a result, existing techniques based on fitting equivalent circuit models may exhibit inaccuracy at extremes of performance and over long-term ageing, or instability of parameter estimates. Pure data-driven techniques, on the other hand, suffer from a lack of generality beyond their training dataset. Here, we propose a novel hybrid approach combining data- and model-driven techniques for battery health estimation, estimating both capacity loss and resistance increase. Specifically, we use a Bayesian method, Gaussian process regression, to estimate model parameters as functions of states, operating conditions, and lifetime. Computational efficiency is ensured by a recursive implementation, yielding a joint state-parameter estimator that learns parameter dynamics from data and is robust to gaps and varying operating conditions. Results show the efficacy of the method, on both simulated and measured drive cycle data, including accurate estimates and forecasts of battery capacity and internal resistance. This opens up new opportunities to understand battery ageing from field data.

Authors 4

1. Antti Aitio

   University of Oxford

   Affiliation as printed

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

   Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, U.K

2. Dominik Jöst Aachen Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA) Center for Ageing Chair for Electrochemical Energy Institute for Power Electronics & Electrical & Center for Ageing, Reliability and Lifetime

   Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Chair for Electrochemical Energy Conversion & Storage Systems, Institute for Power Electronics & Electrical Drives (ISEA), Center for Ageing, Reliability and Lifetime Prediction for Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Jülich Aachen Research Alliance, JARA-Energy , Campus-Boulevard 89, Aachen 52074, Germany

   RWTH Aachen University

   Chair for Electrochemical Energy, Conversion & Storage Systems, Institute for Power Electronics & Electrical, Drives (ISEA), & Center for Ageing, Reliability and Lifetime, Prediction for Electrochemical and Power, Electronic Systems (CARL), RWTH Aachen University, & Jülich Aachen Research Alliance, JARA-Energy, Campus-Boulevard 89, Aachen 52074, Germany

3. Dirk Uwe Sauer Aachen Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives (ISEA) Center for Ageing Chair for Electrochemical Energy Institute for Power Electronics & Electrical & Center for Ageing, Reliability and Lifetime

   Forschungszentrum Jülich · Helmholtz-Institute Münster · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Chair for Electrochemical Energy Conversion & Storage Systems, Institute for Power Electronics & Electrical Drives (ISEA), Center for Ageing, Reliability and Lifetime Prediction for Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Jülich Aachen Research Alliance, JARA-Energy , Campus-Boulevard 89, Aachen 52074, Germany ; , IEK 12, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, Jülich 52428, Germany

   Helmholtz Institute Münster (HI MS) , Campus-Boulevard 89, Aachen 52074, Germany ; , IEK 12, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, Jülich 52428, Germany

   RWTH Aachen University

   Chair for Electrochemical Energy, Conversion & Storage Systems, Institute for Power Electronics & Electrical, Drives (ISEA), & Center for Ageing, Reliability and Lifetime, Prediction for Electrochemical and Power, Electronic Systems (CARL), RWTH Aachen University, & Jülich Aachen Research Alliance, JARA-Energy, Campus-Boulevard 89, Aachen 52074, Germany, & Helmholtz Institute Münster (HI MS), IEK 12, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, 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 ; , Becquerel Avenue, Harwell Campus, Didcot OX11 0RA, UK

   The Faraday Institution, Quad One , Parks Road, Oxford OX1 3PJ, UK ; , Becquerel Avenue, Harwell Campus, Didcot OX11 0RA, UK

   Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, U.K., & the Faraday Institution, Quad One, Becquerel Avenue, Harwell, Campus, Didcot, OX11 0RA, U.K