关键词: 人工智能, 液流电池, 机器学习, 大语言模型

Abstract:

In recent years, artificial intelligence (AI) has made significant advancements in battery design and optimization, showing particular promise in the study of redox flow batteries (RFBs). RFBs are attractive for their low cost, scalability, long cycle life, and high safety, positioning them as critical in advancing new energy storage systems. However, traditional experimental and simulation methods have been inefficient in navigating the design space of RFBs and uncovering their complex physicochemical processes. Our research team presents a novel approach that synergizes computational simulation with data-driven AI technologies to develop a highly interpretable, multiphysics-driven model. This model is further refined through machine learning enhancements, improving the analysis and optimization of RFB designs. Our findings indicate that machine learning models, especially the Gradient Boosting model, are highly effective in predicting voltage efficiency, coulombic efficiency, and capacity. Key factors influencing these metrics were identified using SHAP analysis and interpreted through electrochemical reaction mechanisms, providing a scientific foundation for optimizing battery performance. Additionally, we developed a large language model tailored specifically for the RFB field. By employing refined prompt engineering and text analysis techniques, this model reduces errors typically known as "hallucinations", thus significantly improving the accuracy of information processing. This research underscores the transformative potential of AI-driven simulation and optimization in enhancing the design and performance of RFBs, with the ongoing evolution of computational capabilities and algorithms likely to broaden AI applications in RFBs and other energy storage technologies significantly.

Key words: artificial intelligence, redox flow batteries, machine learning, large language model