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

doi:10.19799/j.cnki.2095-4239.2023.0568

Abstract

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

CLC Number:

TM 919

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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