A Data-Sparse Physics-Informed Gaussian-Process Model for Rechargeable Lithium-Metal Battery Cells in High C-Rate Applications

Abstract

Rechargeable lithium-metal batteries (LMBs) offer the potential for substantially higher energy density than lithium-ion batteries, but their deployment in high C-rate, weight-critical applications requires accurate and efficient models suitable for embedded battery-management systems. This work presents a data-sparse physics-informed Gaussian-process (GP) model that combines an enhanced single-particle model (SPMe) with learned residual dynamics to address accuracy degradation at high C-rate. We introduce a single-pole filter to capture longitudinal electrode non-uniformity neglected by conventional SPM formulations, and leverage GP regression to fuse this added dynamic with the SPMe. To reduce memory requirements, we employ a farthest-point sampling strategy to select a compact yet informative training dataset. By construction, our model guarantees stability and reverts to the vanilla SPMe for input stimuli far enough removed from the training data. Validation against a full-order electrochemical model demonstrates significant error reduction at high C-rates, including an 86% reduction in voltage prediction error for a cell with a 200 μm-thick electrode at 1.8C, and the GP model requires only 1.6% of the storage of our previously developed physics-informed neural-network (PINN) model.