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

TL;DR

This paper develops physics-informed neural network surrogates for Li-ion battery models, significantly reducing computational costs for parameter inference and enabling rapid diagnostics, with regularization techniques tailored for the complex pseudo-2D model.

Contribution

It introduces a regularized PINN surrogate for the pseudo-2D battery model, achieving over 2000x speed-up in parameter inference compared to traditional methods.

Findings

PINN surrogates enable 2250x faster parameter inference.

The models achieve low testing errors of 2mV and 10mV.

Regularization improves surrogate accuracy for complex models.

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 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.