PINEAPPLE：用于锂离子电池电极寿命预测参数推断的物理信息神经进化算法
PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes
AI总结：
PINEAPPLE通过整合物理信息神经网络与进化算法，实现了对锂离子电池电极内部参数的高效、准确推断，为电池寿命预测和管理提供新的解决方案。
摘要：
准确、实时且非破坏性地估计锂离子电池内部状态对于预测退化、优化使用策略和延长使用寿命至关重要。本文介绍了PINEAPPLE（用于锂离子电池电极寿命预测参数推断的物理信息神经进化算法），一种将物理信息神经网络（PINNs）与进化搜索算法相结合的新型框架，旨在实现快速、可扩展且可解释的参数推断，具有应用于下一代电池的潜力。元学习的PINN利用基本物理原理，实现了准确的零样本预测，测试误差低于0.1%，同时比传统求解器快一个数量级。PINEAPPLE在多个来自开源CALCE库的电池上，仅从电压-时间放电曲线中实现了稳健的参数推断，恢复了关键内部状态参数如锂离子扩散系数在使用循环中的演变。值得注意的是，推断出的循环依赖性演变趋势在不同电池中保持一致，无需任何定制的退化物理嵌入启发式方法，突显了通过在PINEAPPLE中整合基本物理原理所带来的有效正则化效果和鲁棒性。通过实现计算高效、实时的参数估计，PINEAPPLE为非破坏性、基于物理的电池模块和电池组内单元和单元间变异性表征提供了有前景的途径，从而解锁下一代电池管理系统中实时需求的新机会，如单个单元级健康状态诊断。
英文摘要：
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.
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