A Physics-Guided Transformer for Robust State of Charge Estimation in Aging Lithium-Ion Batteries

Accurate state of charge (SOC) estimation is a critical challenge for battery management systems (BMSs), hindered by the nonlinear electrochemistry of lithium-ion batteries, their sensitivity to temperature, and pervasive measurement noise. Crucially, battery aging significantly degrades estimation accuracy, posing a major hurdle for long-term system dependability. We propose the Physics-Informed Transformer (PI-Transformer), a novel framework that integrates high-fidelity electrochemical constraints from the PyBaMM (Version: 25.10.2) model directly into a Transformer architecture. This approach ensures physical consistency while leveraging the Transformer’s self-attention mechanism to model long-term temporal dependencies. The framework is specifically designed to be robust against the effects of battery aging, incorporating an attention-based noise modeling module to enhance resilience against sensor uncertainty and capacity fade. Evaluated on two public datasets under diverse conditions, including variable temperatures, fast-charging protocols, and multiple stages of battery degradation, the PI-Transformer consistently achieves state-of-the-art performance. It demonstrates exceptional robustness and maintains high accuracy under challenging low-temperature and severely aged battery scenarios, highlighting its strong potential for deployment in real-world ESS applications where aging is a primary concern.