Jul 2026· International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems· pp. 120-131· 0 citations· 13 references
Computer Science
TL;DR
A novel deep learning surrogate pipeline based on the Swin3D Transformer is introduced to predict spatiotemporal discharge dynamics directly from volumetric data, providing a scalable and efficient framework for high-throughput battery design and optimization.
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.
Due to their high specific energy, lithium-metal batteries (LMBs) are widely regarded as the promising next-generation energy storage devices. Nevertheless, their practical applications are plagued by the challenges of irregular deposition and dissolution, coupled with the high chemical reactivity of lithium electrodes...
Accurate state of charge (SOC) estimation for electric vehicle (EV) lithium-ion batteries is challenged by complex electro-thermal dynamics. Despite recent advances in data-driven methods, a critical gap persists: existing models are trained on single-cell datasets and therefore fail to capture the cell-to-cell therm...
Z. Alimohammadi, A. Sattari, Z. Azimifar et al.· Scientific Reports· 0 citations
Physics-based lithium-ion battery models provide access to physically meaningful internal electrochemical states and processes, but cell-specific parameter inference from terminal current-voltage data is computationally expensive and limited by identifiability. We present a surrogate-accelerated inverse framework based...
A. Gumrukcuoglu, Josh T. Pearson, Jamie M. Foster et al.· 0 citations
This work presents an end-to-end AI-guided inverse-design framework that learns the conditional distribution of device parameters given target photovoltaic figures of merit and offers a transferable, reproducible, and statistically rigorous methodology for accelerating design of next-generation perovskite PV devices.
Parvez Amin Khan, Muhammad Tipu Sultan, Md Mahamudul Islam et al.· Frontiers in Artificial Inte...· 0 citations
The state-of-charge (SOC) estimation of lithium-ion batteries is essential for the performance, safety, and longevity of electric vehicles. While traditional physics-based models face difficulties in predicting SOC under complex conditions, data-driven methods demand large volumes of labeled data, limiting their prac...
Wei Zhang, Le-Tian Niu, Shao-Jie Yang et al.· Robotica (Cambridge. Print)· 0 citations
Rechargeable lithium-metal batteries (LMBs) offer the potential for substantially higher energy density than lithium-ion batteries, but their deployment in high C-rate, weight-critical applications requires accurate and efficient models suitable for embedded battery-management systems. This work presents a data-sparse...
Wesley Hileman, M. Trimboli, Gregory L. Plett· Conference on Control Techno...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
