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.

本文提出一种基于数据驱动和模型驱动方法相结合的混合方法来进行电池健康估计，并证明其计算效率高、数据适用性强以及对不同操作条件具有鲁棒性。实验结果表明该方法能够准确预测电池容量和内阻，为了解电池在实际应用中的老化问题提供了新机遇。