AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries

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.

Machine Learning (cs.LG)

arXiv:2607.20577 [cs.LG]

(or arXiv:2607.20577v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.20577 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Lecture Notes in Computer Science, 2027, Lecture Notes in Artificial Intelligence, 16615, pp.120-131

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