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提升锂离子电池诊断精度，并可为电池老化建模提供支持，但其计算复杂度较高，难以在多个循环、多种工况或多组平行电池样本中重复应用。为降低贝叶斯标定的计算成本，可采用快速替代模型替代基于物理过程的数值求解器。本文开发了一种基于物理信息神经网络（PINN）作为伪二维（P2D）电池模型标定的替代方案。与第一部分中开发的单粒子模型（SPM）PINN相比，P2D替代模型需要引入额外的训练正则化。本研究分别采用PINN-SPM和PINN-P2D替代模型进行参数推断，并将结果与控制方程的数值直接解进行对比。参数推断研究突出了利用此类PINN标定阴极锂扩散系数和阳极交换电流密度缩放参数的能力。相较于标准积分方法，P2D模型可实现2250倍的加速比，使PINN替代模型得以实现快速的健康状态诊断。在低数据可用性场景下，SPM替代模型的测试误差估计为2mV，P2D替代模型为10mV，该误差可通过增加数据量予以缓解。