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

• URL: http://arxiv.org/abs/2607.20577v2
• Date: Fri, 24 Jul 2026 07:45:07 GMT
• Title: AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries
• Abstract summary: We introduce a deep learning surrogate pipeline based on the Swin3D Transformer to predict discharge dynamics directly from data.<n>The proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high- throughput battery design and optimization.
• Score: 47.27974914502511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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