Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending operational lifespan. Here, we introduce PINEAPPLE (Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter inference in Lithium-ion battery Electrodes), a novel framework that integrates physics-informed neural networks (PINNs) with an evolutionary search algorithm to enable rapid, scalable, and interpretable parameter inference with potential for application to next-generation batteries. The meta-learned PINN utilizes fundamental physics principles to achieve accurate zero-shot prediction of electrode behavior with test errors below 0.1$\%$ while maintaining an order-of-magnitude speed-up over conventional solvers. PINEAPPLE demonstrates robust parameter inference solely from voltage-time discharge curves across multiple batteries from the open-source CALCE repository, recovering the evolution of key internal state parameters such as Li-ion diffusion coefficients across usage cycles. Notably, the inferred cycle-dependent evolution of these parameters exhibit consistent trends across different batteries without any customized degradation physics-embedded heuristic, highlighting the effective regularizing effect and robustness that can be conferred through incorporation of fundamental physics in PINEAPPLE. By enabling computationally efficient, real-time parameter estimation, PINEAPPLE offers a promising route towards the non-destructive, physics-based characterization of inter-cell and intra-cell variability of battery modules and battery packs, thereby unlocking new opportunities for downstream on-the-fly needs in next-generation battery management systems such as individual cell-scale state-of-health diagnostics.

翻译：准确、实时且无损地估计锂离子电池内部状态对于预测性能退化、优化使用策略和延长运行寿命至关重要。本文提出PINEAPPLE（面向锂离子电池电极预后参数推断的物理信息神经进化算法），该新型框架将物理信息神经网络（PINNs）与进化搜索算法相结合，实现了快速、可扩展且可解释的参数推断，具备应用于下一代电池的潜力。元学习PINN利用基础物理原理实现了电极行为的精确零样本预测，测试误差低于0.1$\%$，同时较传统求解器获得一个数量级的加速。PINEAPPLE仅基于开源CALCE数据库中多组电池的电压-时间放电曲线，即可实现鲁棒参数推断，成功复原了关键内部状态参数（如锂离子扩散系数）在使用周期内的演变规律。值得注意的是，这些参数的周期依赖性演变在不同电池间呈现出一致趋势，且无需嵌入任何定制化的退化物理启发式规则，凸显了PINEAPPLE通过融合基础物理原理所赋予的有效正则化效应与鲁棒性。通过实现计算高效、实时的参数估计，PINEAPPLE为电池模组与电池包的单体间及单体内部变异性的无损物理表征提供了可行路径，从而为下一代电池管理系统中的下游实时需求（如单体电池级别的健康状态诊断）开辟了新机遇。