To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. This study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models -- such as the single-particle model (SPM) and the pseudo-2D (P2D) model -- with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/pinnstripes). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

翻译：为规划并优化考虑锂离子电池老化动态的储能需求，需开发能够精准快速诊断电池内部状态的技术。本研究旨在通过用基于物理信息神经网络（PINN）代理替代基于物理学的锂离子电池模型（如单粒子模型SPM和准二维模型P2D），降低确定电池内部状态所需的计算资源。该代理模型使贝叶斯校准等高通量技术能够从电压响应中推断电池内部参数。本手稿是两篇系列论文中的第一篇，该系列介绍用于参数推断（即健康状态诊断）的锂离子电池模型PINN代理。在第一部分中，提出了一种构建SPM的PINN代理的方法。研究表明，采用多保真度层级训练（即用多个物理保真度损失训练多个神经网络）在仅基于控制方程残差训练时，能显著提升代理精度。相关实现代码已公开在配套代码库中（https://github.com/NREL/pinnstripes）。第二部分将扩展用于SPM的PINN代理开发技术，应用于P2D电池模型的PINN代理，并探索两种代理的贝叶斯校准能力。