氧化还原液流电池及其能量管理研究进展

Energy Storage Pub Date : 2025-09-18 DOI:10.1002/est2.70267
Anshul Kumar Yadav,  Dhiraj, Anil Kumar Saini
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引用次数: 0

摘要

由于可再生能源与电力工业的彻底统一,电池技术一直是研究界的热点。氧化还原液流电池(rfb)是一种基于电解质的电池,由于其可扩展性、操作灵活性和环境友好性,在微电网(mg)中已经找到了可行的应用。考虑到MG系统的复杂性和电池有效运行的重要性,本文对rfb的电池和能量管理进行了系统和全面的综述。利用文献分析,本研究批判性地考察了电池和能源管理的现有文献,他们的研究趋势,以及相关的挑战。总结表明,现有的方法缺乏先进的技术,无法实现经验学习和量身定制的操作策略,从而无法与其他能源融合,实现更安全、可靠的操作。考虑到这些挑战,本文强调了新兴技术,包括人工智能(AI)、系统建模和数字孪生(DTs),以有效地开发、监测和进一步提高RFB的可靠性。物联网集成的BMS和能源管理系统(EMS)系统可以帮助数据收集,允许智能系统集成,执行准确的预测和系统优化,而人工智能代理可以帮助网络安全和故障响应,实现最先进的电池/EMS。随后,对研究界提出了现有的缺点和未来的展望,并期望作为促进针对RFB的EMS和BMS研究的催化剂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review on Battery and Energy Management for Redox Flow Batteries

Battery technology has been a hot spot in the research community, owing to the radical unification of renewable sources into the electric power industry. Redox flow batteries (RFBs), which are electrolyte-based, are preferred and have found viable applications in microgrids (MGs) due to their scalable nature, operational flexibility, and environmental friendliness. Acknowledging the complexity of the MG system and the importance of effective battery operation, this paper presents a systematic and comprehensive review on battery and energy management for RFBs. Utilizing the bibliographical analysis, this research critically examines the existing literature on battery and energy management, their research trends, and associated challenges. The summary reveals that existing approaches lack the implementation of advanced techniques that enable experiential learning and tailored operational strategies required for safer, reliable operation in convergence with other energy sources. Considering the challenges, the paper emphasizes emerging technology, including artificial intelligence (AI), system modeling, and digital twins (DTs), for effective development, monitoring, and furthering reliability in RFB. IoT-integrated BMS and Energy Management System (EMS) systems can aid data collection, allowing integration of intelligence systems performing accurate forecasting and system optimization, whereas AI agents can help with cybersecurity and fault response, realizing state-of-the-art battery/EMS. Subsequently, existing drawbacks and future prospects are presented for the research community and are expected to act as a catalyst to advance EMS and BMS research, tailored for RFB.

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CiteScore
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