AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries
Summary
Introduces a deep learning surrogate pipeline based on Swin3D Transformer to predict spatiotemporal discharge dynamics in lithium-ion batteries, significantly reducing computational cost while improving accuracy over point cloud baselines.
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# AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries Source: [https://arxiv.org/abs/2607.20577](https://arxiv.org/abs/2607.20577) [View PDF](https://arxiv.org/pdf/2607.20577) > Abstract: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\. ## Submission history From: Mengda Xing \[[view email](https://arxiv.org/show-email/c530c2b5/2607.20577)\] \[via CCSD proxy\] **\[v1\]**Wed, 22 Jul 2026 09:10:48 UTC \(2,500 KB\)
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