将物理方程融入神经网络，提升锂电池电量预测精度与泛化能力
Coupling Neural Networks and Physics Equations For Li-Ion Battery State-of-Charge Prediction
- 设计双分支神经网络，分别预测当前与未来电量状态
- 引入电池动力学方程约束训练，使模型在不同预测时长下更准确
- 在两个公开数据集上表现优于纯数据驱动模型，且结构更轻量
估算电池在使用过程中的电量状态（SoC）演变对实现有效的电源管理策略并延长系统寿命至关重要。现有方法多为基于物理的数字孪生或数据驱动的神经网络（NN）。本文提出两项新贡献：首先，设计一种由两个级联分支组成的新型神经网络架构，一个基于传感器读数预测当前SoC，另一个根据负载行为预测未来SoC；其次，将电池动力学方程融入神经网络训练过程，融合物理规律与数据驱动方法，提升模型在不同预测时长下的泛化能力。我们在两个公开数据集上验证了该方法，结果表明，所提出的物理信息神经网络（PINNs）不仅优于纯数据驱动模型，且在保持更小模型规模的前提下实现了更高的预测精度，达到当前最优水平。
原文摘要 · Abstract (English)
Estimating the evolution of the battery's State of Charge (SoC) in response to its usage is critical for implementing effective power management policies and for ultimately improving the system's lifetime. Most existing estimation methods are either physics-based digital twins of the battery or data-driven models such as Neural Networks (NNs). In this work, we propose two new contributions in this domain. First, we introduce a novel NN architecture formed by two cascaded branches: one to predict the current SoC based on sensor readings, and one to estimate the SoC at a future time as a function of the load behavior. Second, we integrate battery dynamics equations into the training of our NN, merging the physics-based and data-driven approaches, to improve the models' generalization over variable prediction horizons. We validate our approach on two publicly accessible datasets, showing that our Physics-Informed Neural Networks (PINNs) outperform purely data-driven ones while also obtaining superior prediction accuracy with a smaller architecture with respect to the state-of-the-art.
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