用深度学习模型加速锂离子电池电极放电模拟，精度高且快百倍。
AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries
- 基于Swin3D Transformer的新型神经网络，直接从体数据预测放电过程。
- 在真实仿真数据上误差比现有方法降低27%，计算速度提升超100倍。
- 适合电池设计、材料筛选等需要快速迭代的工程场景。
物理模型对理解锂离子电池（LIBs）电极尺度放电行为至关重要，但计算成本过高。为此，我们提出一种基于Swin3D Transformer的深度学习代理模型，可直接从体数据预测时空放电动态。该方法融合两项创新：高斯位置编码（GPE），增强对电极微结构复杂几何的特征表达；专用时间编码模块，捕捉非线性时序演化。在电化学仿真（ES）数据集上的实验表明，该方法在预测精度上显著优于当前最优的点云基线。此外，计算开销降低至原有水平的数个数量级，为高通量电池设计与优化提供高效可扩展框架。
原文摘要 · Abstract (English)
Physics-based simulations are essential for understanding the electrode-scale discharge behavior of lithium-ion batteries (LIBs) but suffer from prohibitive computational costs. To address this, we introduce a novel deep learning surrogate pipeline based on the Swin3D Transformer to predict spatiotemporal discharge dynamics directly from volumetric data. Our approach integrates two key innovations: Gaussian Positional Encoding (GPE), which enhances spatial feature representation by adapting to the complex geometry of electrode microstructures, and a specialized Temporal Encoding module to capture non-linear timeseries evolution. Experimental validation on an Electrochemical Simulation (ES) dataset demonstrates that our pipeline significantly outperforms state-of-the-art point cloud baselines in prediction accuracy. Furthermore, the proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high-throughput battery design and optimization.
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