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基于模糊控制的复合储能系统能量管理策略 被引量:2

Energy management strategy of composite energy storage system based on fuzzy control
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摘要 针对当前纯电动汽车所用锂电池在大功率充放电状态下出现的容量衰减过快、充放电效率较低的问题,设计了一种锂电池为主能量源,超级电容为辅助能量源的复合储能系统,提出了基于模糊控制与逻辑门限控制的能量管理策略,利用Matlab/Simulink软件搭建了锂电池/超级电容复合储能电动汽车前向仿真模型,并结合中国乘用车行驶工况进行了仿真分析。仿真结果表明,采用基于模糊控制与逻辑门限控制的能量管理策略不仅可以降低锂电池的输出功率和放电电流,减少锂电池的充放电次数,还能提高储能系统整体的充放电效率,相比于单能源系统可减少39.83%的锂电池容量衰减,降低8.44%的整车能耗。 Aiming at the problems of excessively fast capacity decay and low charging and discharging efficiency of lithium batteries used in current pure electric vehicles under high-power charging and discharging conditions,a composite storage system with lithium batteries as the main energy source and supercapacitors as the auxiliary energy source is designed.An energy management strategy based on the combination of fuzzy control and logic threshold control is proposed for the energy system.The forward simulation model of electric vehicles with lithium battery/supercapacitor composite energy storage is built using Matlab/Simulink software,and based on the driving conditions of Chinese passenger cars,simulation analysis was carried out.The simulation results show that the energy management strategy can not only reduce the output power and discharge current of the lithium battery,decrease the number of charging and discharging times of the lithium battery,but also improve the overall charging and discharging efficiency of the energy storage system.Compared with a single energy system,it can reduce the capacity attenuation of lithium batteries by 39.83%and the energy consumption of the vehicle by 8.44%.
作者 胡自豪 孙宾宾 鲁花蕾 顾天琪 高松 HU Zihao;SUN Binbin;LU Hualei;GU Tianqi;GAO Song(School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo 255049,China)
出处 《山东理工大学学报(自然科学版)》 CAS 2022年第5期21-27,共7页 Journal of Shandong University of Technology:Natural Science Edition
基金 国家自然科学基金项目(51805301) 山东省自然科学基金项目(ZR2019BEE043) 中国博士后基金面上项目(2020M680091)。
关键词 电动汽车 复合储能系统 模糊控制 前向仿真 容量衰减 electric car composite energy storage system fuzzy control forward simulation capacity fading
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