用CNN替代复杂电化学模型，实现电池寿命状态精准预测。
Learning the P2D Model for Lithium-Ion Batteries with SOH Detection
- 用CNN学习P2D模型的锂浓度分布，提升计算效率
- 在随机工况下模拟数据训练，保持高精度匹配
- 可动态适配电池健康状态变化，适合实际系统部署
锂离子电池广泛应用于各类场景。电池管理系统需通过数学模型模拟电池性能，以优化使用、充电策略，并预测其在预定工况下的失效时间。传统方法采用电化学模型或数据驱动模型。高电流运行下的电化学模型存在数学和计算复杂性问题。本文展示，经典的伪二维（Pseudo Two Dimensional, P2D）电化学模型可被一个高效的卷积神经网络（CNN）代理模型替代，该模型基于多种随机驾驶循环的精确仿真数据进行拟合。结果表明，CNN能准确捕捉锂离子浓度分布特征；同时，通过调整网络参数，可有效对应电池健康状态（State of Health, SOH）的变化，为实际电池管理提供高效、可适应的建模方案。
原文摘要 · Abstract (English)
Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These models can be electrochemical or data-driven. Electrochemical models for batteries running at high currents are mathematically and computationally complex. In this work, we show that a well-regarded electrochemical model, the Pseudo Two Dimensional (P2D) model, can be replaced by a computationally efficient Convolutional Neural Network (CNN) surrogate model fit to accurately simulated data from a class of random driving cycles. We demonstrate that a CNN is an ideal choice for accurately capturing Lithium ion concentration profiles. Additionally, we show how the neural network model can be adjusted to correspond to battery changes in State of Health (SOH).
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