人工智能在长时液流电池储能中的应用:性能优化和大模型  

Application of artificial intelligence in long-duration redox flow batteries storage systems

作　　者：刘子玉 姜泽坤 邱伟[1] 徐泉[1] 牛迎春 徐春明[1] 周天航 LIU Ziyu;JIANG Zekun;QIU Wei;XU Quan;NIU Yingchun;XU Chunming;ZHOU Tianhang(China University of Petroleum(Beijing),Beijing 102249,China;Zhonghai Energy Storage Technology(Beijing)Co.,Ltd,Beijing 102249,China;National Elite Institute of Engineering,CNPC,Beijing 100096,China)

机构地区：[1]中国石油大学(北京),北京102249 [2]中海储能科技(北京)有限公司,北京102249 [3]中国石油国家卓越工程师学院,北京100096

出　　处：《储能科学与技术》2024年第9期2871-2883,共13页Energy Storage Science and Technology

基　　金：国家自然科学基金(22308378,22393963,22308380,22308376,20220242);中国石油大学(北京)科学基金(2462023XKBH005,2462024BJRC017,ZX20230080);碳中和联合研究院基金(CNIF20230209)。

摘　　要：近年来,人工智能(AI)技术在电池设计与优化领域取得了显著进展,特别是在液流电池的研究中展现出巨大的应用潜力。液流电池因其低成本、大规模、长循环寿命及高安全性,成为新型电力储能系统的研究重点。然而,传统的实验与仿真方法在探索液流电池设计空间方面效率较低,难以揭示其复杂的物理化学机制。本工作提出将计算机模拟与数据驱动的AI技术相结合,建立了具备高度可解释性的多物理场驱动模型,并通过机器学习辅助分析与优化液流电池设计。研究表明,机器学习模型在电压效率、库仑效率和容量预测方面表现优异,特别是梯度提升模型(gradient boosting,GB)在预测准确性上优于其他模型。通过SHAP分析识别关键影响因素,并结合电化学反应机理进行解释,为液流电池性能优化提供了科学依据。此外,本工作还开发了一个专门针对液流电池领域的大语言模型,通过精细的提示工程和文本分析流程,尽可能最小化“幻觉”,有效提升了信息处理的准确性。本工作的研究表明,AI驱动的模拟与优化方法为液流电池的设计与性能提升提供了新途径,未来随着计算能力和算法的不断发展,AI在液流电池及其他储能技术中的应用前景将更加广阔。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.

关 键 词：人工智能 液流电池 机器学习 大语言模型 

分 类 号：TM911[电气工程—电力电子与电力传动]