用分数阶微分方程提升电池电量估计精度
Fractional Differential Equation Physics-Informed Neural Network and Its Application in Battery State Estimation
- 将分数阶微分引入神经网络，融合物理规律建模
- 在-10℃至20℃下测试，显著优于传统方法
- 适合需要高精度电池管理的工程应用
精确估计锂离子电池的荷电状态（SOC）对保障系统安全、可靠与性能优化至关重要。传统数据驱动神经网络模型难以充分刻画电化学过程中的复杂非线性及记忆依赖动态特性，导致在动态工况下预测精度和物理可解释性受限。为此，本文提出一种新型神经架构——分数阶微分方程物理信息神经网络（FDIFF-PINN），将分数阶微积分与深度学习相结合。主要贡献包括：(1) 基于分数阶等效电路模型，构建离散化的分数阶偏微分方程；(2) 在多温度条件（-10℃至20℃）下，针对Panasonic 18650PF电池的动态充放电数据集开展对比实验。
原文摘要 · Abstract (English)
Accurate estimation of the State of Charge (SOC) is critical for ensuring the safety, reliability, and performance optimization of lithium-ion battery systems. Conventional data-driven neural network models often struggle to fully characterize the inherent complex nonlinearities and memory-dependent dynamics of electrochemical processes, significantly limiting their predictive accuracy and physical interpretability under dynamic operating conditions. To address this challenge, this study proposes a novel neural architecture termed the Fractional Differential Equation Physics-Informed Neural Network (FDIFF-PINN), which integrates fractional calculus with deep learning. The main contributions of this paper include: (1) Based on a fractional-order equivalent circuit model, a discretized fractional-order partial differential equation is constructed. (2) Comparative experiments were conducted using a dynamic charge/discharge dataset of Panasonic 18650PF batteries under multi-temperature conditions (from -10$^{\circ}$C to 20$^{\circ}$C).
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