This paper presents a novel physical parameter estimation framework for on-site model characterization, using a two-phase modelling strategy with Physics-Informed Neural Networks (PINNs) and transfer learning (TL). In the first phase, a PINN is trained using only the physical principles of the single particle model (SPM) equations. In the second phase, the majority of the PINN parameters are frozen, while critical electrochemical parameters are set as trainable and adjusted using real-world voltage profile data. The proposed approach significantly reduces computational costs, making it suitable for real-time implementation on Battery Management Systems (BMS). Additionally, as the initial phase does not require field data, the model is easy to deploy with minimal setup requirements. With the proposed methodology, we have been able to effectively estimate relevant electrochemical parameters with operating data. This has been proved estimating diffusivities and active material volume fractions with charge data in different degradation conditions. The methodology is experimentally validated in a Raspberry Pi device using data from a standard charge profile with a 3.89\% relative accuracy estimating the active material volume fractions of a NMC cell with 82.09\% of its nominal capacity.

翻译：本文提出了一种新颖的物理参数估计框架，用于现场模型表征。该框架采用包含物理信息神经网络（PINNs）与迁移学习（TL）的两阶段建模策略。在第一阶段，仅利用单粒子模型（SPM）方程的物理原理对PINN进行训练。在第二阶段，冻结PINN的大部分参数，同时将关键电化学参数设置为可训练状态，并利用实际电压曲线数据进行调整。所提出的方法显著降低了计算成本，使其适用于电池管理系统（BMS）的实时部署。此外，由于初始阶段无需现场数据，该模型易于部署且设置要求极低。通过所提出的方法，我们能够利用运行数据有效估计相关电化学参数。这已通过在不同退化条件下利用充电数据估计扩散系数和活性材料体积分数得到验证。该方法在树莓派设备上进行了实验验证，使用标准充电曲线数据对NMC电池的活性材料体积分数进行估计，相对精度达3.89%，此时电池容量为其标称容量的82.09%。