Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of 2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. In the low-data availability scenario, the testing error was estimated to 2mV for the SPM surrogate and 10mV for the P2D surrogate which could be mitigated with additional data.

翻译：贝叶斯参数推断有助于改进锂离子电池诊断，并可辅助建立电池老化模型。然而，该方法计算成本高昂，难以在多个循环、多种运行条件或多组重复电池单元上轻易重复应用。为降低贝叶斯校准的计算成本，可将基于物理模型的数值求解器替换为更快速的代理模型。本文开发了一种物理信息神经网络（PINN）作为伪二维（P2D）电池模型校准的代理。与第一部分中开发的PINN单粒子模型（SPM）相比，P2D代理模型需要额外的训练正则化。本文对PINN SPM和P2D代理模型均进行了参数推断测试，并将其结果与从控制方程直接数值解中获得的数据进行了比较。参数推断研究突显了利用这些PINN校准阴极锂扩散和阳极交换电流密度缩放参数的能力。与使用标准积分方法相比，P2D模型实现了2250倍的计算加速，使得PINN代理模型能够快速进行健康状态诊断。在低数据可用性场景下，SPM代理模型的测试误差估计为2毫伏，P2D代理模型为10毫伏，这一问题可通过增加数据量来缓解。