  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.
