液流电池储能系统状态评估模型研究进展  

Advances in State Assessment Modeling of Liquid-fluid Battery Energy Storage Systems

作　　者：姜泽坤 张博远 周子晨 冯家乐 梁珂 黄允恩 周天航 JIANG Zekun;ZHANG Boyuan;ZHOU Zichen;FENG Jiale;LIANG Ke;HUANG Yun’en;ZHOU Tianhang(State Key Laboratory of Heavy Oil Processing//China University of Petroleum(Beijing),Beijing 102249,China;Zhonghai Energy Storage Technology(Beijing)Co.,Ltd.,Beijing 102249,China)

机构地区：[1]重质油全国重点实验室·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

出　　处：《油气与新能源》2025年第2期82-91,102,共11页Petroleum and new energy

基　　金：国家自然科学基金委青年基金项目“复杂体系高相容性聚乙烯-聚丙烯共聚物结构精准设计”(22308376);智能电网重大专项(2030)“绿电绿氢重化工园区用能互动示范工程”(2024ZD0801900);新疆维吾尔自治区重大科技专项项目“可再生能源与‘绿氢’系统适配关键技术研发”(2024A01001)。

摘　　要：在全球能源转型的推动下,新能源技术正加速发展,液流电池凭借其长寿命、高安全性等优势,在大规模储能系统中承担着重要角色。对于液流电池,准确的系统状态评估可以有效提高电池的使用效率、延长其使用寿命,并优化并网启停等操作,从而提升整体能源管理的可靠性和经济性。综述了液流电池储能系统中荷电状态(SOC)和健康状态(SOH)估算方法的研究进展,重点分析了几种经典的评估方法,包括开路电压法、安时积分法、等效电路模型与卡尔曼滤波器相结合的算法,以及近年来兴起的基于人工智能的数据驱动模型,深入探讨了这些方法的优劣势,指出电池复杂电化学过程及外部环境变化对SOC和SOH估算的影响。未来液流电池管理系统智能化的关键方向是提升SOC和SOH估算的精度、降低计算复杂度,以及增强人工智能模型的可解释性。Driven by the global energy transition,the development of new energy technologies is rapidly accelerating,and flow batteries have become a key technology in large-scale energy storage systems due to their advantages of long life and high safety.For liquid-flow batteries,accurate system state assessment can effectively improve the efficiency of the batteries,extend their service life,and optimize the operations including grid start/stop,thus enhancing the reliability and economy of the overall energy management.This paper reviews the recent research progress of state-ofcharge(SOC) and state-of-health(SOH) estimation methods for liquid-flow battery energy storage systems,detailed analyzes several classical assessment methods including the open-circuit voltage method,the ampere-time integration method,the algorithm combining the equivalent circuit model and the Kalman filter,and the data-driven model based on artificial intelligence emerging in recent years.It deeply discusses the advantages and disadvantages of these methods and points out the impact of the complex electrochemical processes and external environmental changes of batteries on SOC and SOH estimation.The future development outlook section emphasizes improving the accuracy of SOC and SOH estimation,reducing the computational complexity,and enhancing the interpretability of AI models will be the key directions for the intelligence of liquid-flow battery management systems.

关 键 词：液流电池 荷电状态 健康状态 电池管理系统 等效电路模型 人工智能 

分 类 号：TM912[电气工程—电力电子与电力传动]