人工智能与储能技术融合的前沿发展

The forefront of the integration of artificial intelligence and energy storage technologies

随着大规模储能系统和电气设备的不断适应,电池和超级电容器(supercapacitors)的储能能力面临着越来越多的需求和挑战。其中漫长的研发周期及低效率的材料筛选是储能材料(energy storage materials,ESM)开发的两大难题,将人工智能(artificial Intelligence,AI)应用于ESM的研发是解决该问题的新方案。而机器学习(machine Learning,ML)作为AI的子领域,已被证明是从数据中获得见解的强大工具,ML可以挖掘大数据背后有价值的信息和隐含的关联,有助于揭示ESM的关键结构或性质与性能关系,大大加快了ESM的研发和筛选,同时AI为储能系统的设计和运行提供了先进的预测工具。因此,未来AI与储能技术的融合研究将是值得关注的新兴领域。本文首先阐述了AI的关键技术框架,包括监督学习、无监督学习以及可解释的人工智能(XAI)。然后从ESM设计、识别筛选和性能预测三个方向出发,分别总结了AI在这些储能领域的最新研究进展,包括机器学习在储能材料研究中常用的数据库列表,并分析了这一融合技术对智能电网优化、可再生能源集成与管理的贡献。最后,本文展望了AI与储能技术的融合面临的机遇挑战,以及未来需要重点关注的研究方向。

With the continuous adaptation of large-scale energy storage systems and electrical equipment,the energy storage capacity of batteries and supercapacitors is facing increasing demands and challenges.The long research and development cycle and inefficient material screening are two major problems in the development of energy storage materials(ESM).The application of artificial intelligence(AI)to ESM is a new solution to this problem.Furthermore,machine learning(ML),an aspect of AI,has proven to be a powerful tool to for gaining insights from data.ML can mine data behind valuable information and implicit correlation,help to reveal the key structure of ESM or properties and performance relationships,and significantly accelerate ESM development and screening.At the same time,AI for energy storage system design and operation provides advanced prediction tools.Therefore,future integration research on AI and energy storage technology will be an emerging field worthy of attention.This study first provides an overview of the key technical framework for AI,including the ML process,supervised and unsupervised learning,and explainable AI.Then,the latest research progress of AI in ESM design,identification,screening,and performance prediction is summarized.A list of databases commonly used in ML in energy storage materials research is also provided.The contribution of this fusion technology to smart grid optimization and renewable energy integration and management is briefly analyzed.Finally,this study looks at the opportunities and challenges facing the integration of AI and energy storage technology,as well as the research directions to focus on in the future.