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.

翻译：估计健康状态是电池管理系统的一项关键功能，但由于实际应用中运行条件和用户需求的多样性，这一任务仍具有挑战性。因此，基于等效电路模型拟合的技术可能在极端性能条件、长期老化过程中出现不准确性，或导致参数估计的不稳定性。而纯粹的数据驱动技术则缺乏超越其训练数据集的泛化能力。本文提出了一种结合数据驱动与模型驱动技术的混合方法用于电池健康估计。具体而言，我们采用一种贝叶斯数据驱动方法——高斯过程回归——来估计模型参数作为状态、运行条件和寿命的函数。通过递归方法提高了计算效率，从而形成统一的联合状态-参数估计器，该估计器能从数据中学习参数动态，并对数据缺失和变化的运行条件具有鲁棒性。结果表明，该方法在模拟数据和实测数据上均表现出有效性，包括对电池容量和内阻的准确估计与预测。这为理解实际应用中电池老化机制开辟了新的可能性。