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.

利用贝叶斯参数推断改进锂离子电池诊断，帮助构建电池老化模型；通过物理信息神经网络作为代理加快贝叶斯校准速度，实现快速电池健康状态诊断。