Machine learning-enhanced gaussian peak modeling for energy diffusion patterns in lithium-ion battery electrodes

Energy storage systems are crucial for the advancement of modern technology, yet achieving optimal efficiency in these systems remains a significant challenge. This study investigates how spatial distribution impacts energy diffusion within battery electrodes using Gaussian peak models. We explore two distinct configurations of Gaussian peaks separate and overlapping to understand their effects on diffusion patterns. Separate Gaussian peaks produce simpler diffusion profiles with well-defined influence regions, while overlapping peaks create complex diffusion patterns with intricate contours reflecting the combined effects of multiple sources. To enhance the accuracy and efficiency of our simulations, we leverage machine learning (ML) optimization techniques. An ML-based optimizer is employed to refine the parameters of the diffusion model, resulting in more precise and efficient simulations. This approach allows us to better understand and predict how different peak arrangements affect diffusion dynamics. By simulating these scenarios in rectangular and square electrode geometries, we reveal how the spatial arrangement of energy sources influences diffusion behavior. Our findings provide valuable insights for optimizing battery designs, suggesting that tailored diffusion profiles can significantly improve energy storage efficiency and reliability. This research not only advances the theoretical understanding of energy diffusion processes but also demonstrates how ML optimization can be used to enhance the performance of lithium-ion batteries and other energy storage systems.