Real-Time Physics-Aware Battery Health Monitoring from Partial Charging Profiles via Physics-Informed Neural Networks
For battery management systems, this enables rapid, detailed health diagnostics from partial data, addressing the trade-off between speed and diagnostic depth.
The paper develops a physics-informed neural network that identifies internal battery parameters from partial charging profiles in about 30 seconds (47x speedup over finite volume method), improving state-of-health estimation accuracy by at least 60.61% and enabling extrapolation to unseen degradation levels.
Monitoring battery health is essential for ensuring safe and efficient operation. However, there is an inherent trade-off between assessment speed and diagnostic depth-specifically, between rapid overall health estimation and precise identification of internal degradation states. Capturing detailed internal battery information efficiently remains a major challenge, yet such insights are key to understanding the various degradation mechanisms. To address this, we develop a parameterized physics-informed neural network (P-PINNSPM) over the key aging-related parameter space for a single particle model. The model can accurately predict internal battery variables across the parameter space and identifies internal parameters in about 30 seconds-achieving a 47x speedup over the finite volume method-while maintaining high accuracy. These parameters improve the battery state-of-health (SOH) estimation accuracy by at least 60.61%, compared to models without parameter incorporation. Moreover, they enable extrapolation to unseen SOH levels and support robust estimation across diverse charging profiles and operating conditions. Our results demonstrate the strong potential of physics-informed machine learning to advance real-time, data-efficient, and physics-aware battery management systems.