Multiphysics-Informed Adaptive Personalized Federated Learning for Battery State-of-Health Estimation

Accurate state-of-health estimation is critical for the safe and reliable operation of Lithium-ion batteries, but developing robust data-driven models is often hindered by data privacy concerns and the distributed nature of the data. Federated learning (FL) offers a privacy-preserving solution, yet its performance is severely challenged by the statistical heterogeneity and data imbalance inherent in real-world distributed battery data. Furthermore, the opaque nature of conventional FL models impedes the cross-institutional trust required for collaborative training. To address these limitations, this article proposes a novel multiphysics-informed adaptive personalized federated learning (MP-APFL) framework. At the client level, we first develop a multiphysics-informed, multistage aging partial differential equation (PDE) that combines analytical models for key degradation mechanisms, such as solid electrolyte interphase formation, thermal effects, and lithium plating-induced “knee points,” with a data-driven term to account for complex unmodeled dynamics. Subsequently, this governing PDE is embedded as a soft constraint within a multiphysics-informed neural network via an adaptively weighted composite loss function that synergistically enforces physical consistency, maintains parameter realism, and ensures trend plausibility by penalizing unphysical jumps while permitting valid recovery, guiding each local model to learn physically plausible and interpretable aging dynamics. At the system level, we design MP-APFL to employ a privacy-preserving hybrid architecture with a shared encoder and personalized multiphysics components. This is orchestrated by an adaptive strategy featuring Information value-driven client prioritization for efficient client selection and physics-guided and stability-aware encoder aggregation for robust model fusion. Comprehensive experiments on laboratory-generated and public datasets demonstrate that MP-APFL significantly outperforms prominent FL benchmarks, particularly in highly heterogeneous and imbalanced settings.