Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte
2026-06-26 • Machine Learning
Machine Learning
AI summaryⓘ
The authors explain that physics-informed neural networks (PINNs) can solve complex battery models more efficiently than traditional numerical methods. They focus on a common battery model called the single particle model with electrolyte (SPMe), which describes how lithium-ion batteries work. Training these models for different batteries can be slow, so the authors introduce a way to speed this up by using transfer learning—first training on general battery behavior, then fine-tuning for specific batteries. Their approach, tested with a battery modeling tool called PyBaMM, shows accurate voltage predictions while saving training time.
Physics-informed neural networksPartial differential equationsSingle particle model with electrolyteTransfer learningLithium-ion batteriesElectrochemical modelingBattery voltage predictionPyBaMMNumerical methodsReduced-order models
Authors
Gift Modekwe, Qiugang Lu
Abstract
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models. They typically en-force conservation laws within the loss function to ensure physically consistent solutions. Tradi-tional numerical methods such as finite difference, finite volume, and finite element techniques, re-ly on discretization and can be computationally expensive for nonlinear systems. To address this challenge, PINNs offer improved scalability, particularly for reduced-order models like the single particle model with electrolyte (SPMe). The SPMe describes lithium-ion battery dynamics through coupled diffusion, transport, reaction kinetics, and voltage equations. Despite these advantages, training SPMe-based PINNs from scratch for different battery chemistries or operating conditions is demanding and often leads to slow convergence. To overcome this limitation, this work introduces a transfer learning framework for SPMe-PINNs. The model is first pretrained to learn general elec-trochemical dynamics and then adapted to a target battery by transferring weights, freezing se-lected layers, and fine tuning the remaining parameters, including estimating key electrochemical variables. Validation using PyBaMM demonstrates accurate voltage prediction, indicating that the proposed approach preserves electrochemical consistency while reducing training time and ena-bling efficient generalization across batteries.