PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model

arXiv:2312.17336v2 Announce Type: replace
Abstract: 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 …

abstract aging application arxiv battery bayesian cells cs.lg diagnostics however inference multiple part physics.app-ph pinn regularization replicate type