这篇论文讲怎么用迁移学习加速电池PINN模型训练，能跨电池用，省时间还准，做电池仿真的可以看看。
本研究提出一种迁移学习框架，用于基于单粒子模型含电解质（SPMe）的物理信息神经网络（PINN）训练。模型首先在通用电化学动力学数据上预训练，然后通过权重迁移、冻结部分层并微调剩余参数适配目标电池。使用PyBaMM验证表明，该方法能准确预测电压，保持电化学一致性。相比从头训练，迁移学习显著减少训练时间，实现跨电池高效泛化。
Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte
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.