用物理神经网络+迁移学习，现场实时估算电池电化学参数
On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach
- 先用物理方程训练基础模型，再用实测数据微调关键参数
- 在树莓派上实现，估计活性物质体积分数误差仅3.89%
- 无需现场数据预训练，适合电池管理系统快速部署
本文提出一种基于物理信息神经网络（PINN）与迁移学习（TL）的新型在线参数估计算法。第一阶段仅基于单粒子模型（SPM）物理方程训练PINN；第二阶段冻结大部分参数，仅对关键电化学参数进行微调，利用真实电压数据优化。该方法大幅降低计算成本，适用于电池管理系统（BMS）实时部署。由于第一阶段无需现场数据，部署门槛低。实验表明，该方法可有效估计扩散系数和活性材料体积分数，尤其在不同老化状态下表现良好。在树莓派设备上使用标准充电曲线验证，对容量为额定值82.09%的NMC电池，活性材料体积分数估计相对误差为3.89%。
原文摘要 · Abstract (English)
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.
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