锂离子电池/超级电容器混合储能系统能量管理方法综述

doi:10.19799/j.cnki.2095-4239.2023.0568

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (2): 652-668.doi: 10.19799/j.cnki.2095-4239.2023.0568

锂离子电池/超级电容器混合储能系统能量管理方法综述

宋元明¹^,²(), 刘亚杰¹^,², 金光¹, 周星¹^,³(), 黄旭程¹^,²

^1.国防科技大学系统工程学院
^2.多能源系统智慧互联技术湖南省重点实验室
^3.国防科技大学前沿交叉学科学院，湖南长沙 410073

收稿日期:2023-08-23 修回日期:2023-08-29 出版日期:2024-02-28 发布日期:2024-03-01
通讯作者: 周星 E-mail:ysong@nudt.edu.cn;395877464@qq.com
作者简介:宋元明（1997—），男，博士研究生，研究方向为储能系统优化与综合集成，E-mail：ysong@nudt.edu.cn；
基金资助:
湖南省科技创新计划资助项目(2021RC2074);国家自然科学基金项目(71901210);中国博士后科学基金面上项目(2021MD703975)

Review of energy management methods for lithium-ion battery/supercapacitor hybrid energy storage systems

Yuanming SONG¹^,²(), Yajie LIU¹^,², Guang JIN¹, Xing ZHOU¹^,³(), Xucheng HUANG¹^,²

^1.College of Systems Engineering, National University of Defense Technology
^2.Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology, National University of Defense Technology
^3.College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, Hunan, China

Received:2023-08-23 Revised:2023-08-29 Online:2024-02-28 Published:2024-03-01
Contact: Xing ZHOU E-mail:ysong@nudt.edu.cn;395877464@qq.com

摘要/Abstract

摘要：

锂离子电池/超级电容器混合储能系统因其良好的性能、较低的成本和较强的通用性，已成为应用最为广泛的混合储能系统。能量管理技术是混合储能系统的核心技术之一，也是当前主要的研究热点。为了系统地对混合储能系统能量管理方法进行综述，本文首先对锂离子电池/超级电容器混合储能系统的拓扑结构、能量管理架构以及功率分配控制进行了介绍；而后，本文将现有的混合储能系统能量管理方法分为基于经验、基于优化、基于工况模式识别和基于机器学习5大类并进行了详细的对比分析，重点针对规律性工况与随机性工况讨论了各类能量管理方法的效能，并分析了各类方法的鲁棒性与计算复杂度；最后，本文对现有的能量管理方法进行了总结，并对该领域未来的研究方向和发展趋势进行了展望。综合分析表明，提高对随机性负载未来工况的预测精度、建立更加精准的混合储能系统模型并通过云端协同进一步提升能量管理方法的实时性将是未来混合储能系统能量管理研究的重点。

关键词: 混合储能系统, 能量管理, 功率分配, 锂离子电池, 超级电容器

Abstract:

Lithium-ion battery/supercapacitor hybrid energy storage system has become the most widely used hybrid energy storage system because of its good performance, low cost and strong versatility. Energy management method is one of the core technologies of hybrid energy storage systems, and it is also the main research focus at present. In order to systematically review the energy management methods of hybrid energy storage systems, this paper first introduces the topology structure, energy management architecture and power distribution control of lithium-ion battery/supercapacitor hybrid energy storage systems. Then, this paper divides the existing energy management methods of hybrid energy storage system into four categories: experience based, optimization based, working condition pattern recognition based and machine learning based, and the efficiency of each type of energy management methods is discussed respectively for regular and random conditions; the robustness and computational complexity of each method are also analyzed. Finally, the current energy management methods are summarized and the future research directions and development trends in this field are prospected. Comprehensive analysis shows that improving the prediction accuracy of stochastic load in the future, establishing a more accurate hybrid energy storage system model, and further improving the real-time performance of energy management methods through cloud collaboration will be the focus of future energy management research of hybrid energy storage systems.

Key words: hybrid energy storage system, energy management, power allocation, lithium-ion battery, super capacitor

中图分类号:

TM 919

宋元明, 刘亚杰, 金光, 周星, 黄旭程. 锂离子电池/超级电容器混合储能系统能量管理方法综述[J]. 储能科学与技术, 2024, 13(2): 652-668.

Yuanming SONG, Yajie LIU, Guang JIN, Xing ZHOU, Xucheng HUANG. Review of energy management methods for lithium-ion battery/supercapacitor hybrid energy storage systems[J]. Energy Storage Science and Technology, 2024, 13(2): 652-668.

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储能类型	功率密度/(W/kg)	能量密度/(Wh/kg)	日自放电率/%	响应时间	效率/%	日历寿命/a	循环寿命/次
压缩空气^{[25, 30]}	—	30~60	0	数秒至数分钟	40~70	20~40	—
飞轮^{[25, 31-33]}	400~1600	5~130	20~100	数毫秒至数秒	80~90	15~20	10⁴~10⁷
超级电容器^[34-35]	3000~50000	5~20	2~40	数毫秒	85~98	5~12	10⁵~10⁶
铅酸电池^[36]	20~200	30~50	0.1~1	数毫秒	78~85	5~10	500~2000
锂离子电池^[37-38]	250~450	130~250	0.1~0.3	数毫秒	80~95	5~10	1000~3000
钠离子电池^[39-40]	100~120	100~150	0.1~0.2	数毫秒	80~90	1~5	1000~1200
液流电池^[41-43]	—	30~50	0~1	数毫秒至数秒	70~85	10~20	>10⁴
燃料电池^{[25, 44]}	3000~5400W/L	500~3000	0.5~2	数秒至数分钟	20~66	5~30	10³~10⁴

HESS拓扑结构			电池分系统输出电流	电池分系统输出电压	电容分系统输出电流	电容分系统输出电压	总线电压	对应文献
受控式	主动式拓扑		可控	可控	可控	可控	可控	[4-5, 48-53]
	半主动式拓扑	电池半主动	可控	可控	不可控	不可控	不可控	[64-70]
	半主动式拓扑	电容半主动	不可控	不可控	可控	可控	不可控	[11, 54-63]
无控式	被动式拓扑		不可控	不可控	不可控	不可控	不可控	[78-85]

能量管理方法		规律性工况管理效能	随机性工况管理效能	鲁棒性	离线计算复杂度	在线计算复杂度	对应文献
基于经验	基于逻辑门限/模糊逻辑	中等	较差	中等	较低	很低	[87-91]
基于经验	基于滤波	中等	较差	中等	较低	很低	[55, 61, 92]
基于优化	基于离线优化	良好	中等	中等	较高	很低	[12, 62, 93-95]
基于优化	基于模型预测控制	较好	良好	良好	很低	较高	[18, 57, 64, 66, 96-99]
基于工况模式识别		较好	较好	较好	中等	中等	[11-12, 60, 65, 100-102]
基于机器学习		较好	较好	良好	很高	较低	[67-68, 103-115]

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锂离子电池/超级电容器混合储能系统能量管理方法综述

Review of energy management methods for lithium-ion battery/supercapacitor hybrid energy storage systems

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