Learning battery model parameter dynamics from data with recursive Gaussian process regression

TL;DR

This paper introduces a hybrid Bayesian method using recursive Gaussian process regression to accurately estimate battery health parameters dynamically, improving robustness and forecasting in real-world conditions.

Contribution

It presents a novel recursive Gaussian process regression approach for modeling battery parameter dynamics, combining data-driven and model-driven techniques for improved health estimation.

Findings

Accurate estimation of battery capacity and internal resistance.

Robustness to gaps and varying operating conditions.

Effective on both simulated and real data.

Abstract

Estimating state of health is a critical function of a battery management system but remains challenging due to the variability of operating conditions and usage requirements of real applications. As a result, 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 lack of generality beyond their training dataset. In this paper, we propose a hybrid approach combining data- and model-driven techniques for battery health estimation. Specifically, we demonstrate a Bayesian data-driven method, Gaussian process regression, to estimate model parameters as functions of states, operating conditions, and lifetime. Computational efficiency is ensured through a recursive approach yielding a unified 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 data, including accurate estimates and forecasts of battery capacity and internal resistance. This opens up new opportunities to understand battery ageing in real applications.