On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach

This article presents a novel framework for onsite estimation of lithium-ion battery electrochemical parameters by combining physics-informed neural networks (PINNs) with transfer learning (TL). Unlike conventional physics-based models that require costly offline parameterization or extensive experimental tests, the proposed two-phase strategy first trains a PINN using only the governing equations of the single particle model, and then fine-tunes selected electrochemical parameters with field voltage measurements while keeping most network weights frozen. This enables efficient adaptation to ageing and cell-to-cell variability with minimal data requirements. The approach achieves high accuracy in estimating key parameters such as diffusion coefficients and active material volume fractions under different degradation conditions. Under a simulation environment, the method reconstructs voltage responses with a root-mean-square error of 8.94 mV and estimates active material fractions with an average relative error below 2.2% across 400 study cases, significantly outperforming classical optimization-based estimators. Experimental validation on an aged NMC cell (82.09% nominal capacity) implemented on a Raspberry Pi 5, demonstrates practical feasibility, yielding a relative error of 3.89% in estimating volume fractions with a computational cost of 106 ms per iteration. These results confirm that the proposed TL-based PINN framework offers an accurate, computationally efficient, and easily deployable solution for real-time battery management systems.