| 引用本文: | 申永鹏,孙嵩楠,刘东奇,孙建彬,赵 俊.车载混合储能系统Symlets小波变换能量管理方法[J].电力系统保护与控制,2022,50(6):74-81.[点击复制] |
| SHEN Yongpeng,SUN Songnan,LIU Dongqi,SUN Jianbin,ZHAO Jun.Symlets wavelet transform energy management method for a vehicle-mounted hybrid energy storage system[J].Power System Protection and Control,2022,50(6):74-81[点击复制] |
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| 摘要: |
| 针对电动汽车锂离子动力电池承受高频功率需求造成其循环寿命衰减的问题进行了重点研究。首先，分析了由锂离子动力电池、超级电容和多端口双向DC/DC变换器构成的混合储能系统工作特性，揭示了工况周期tmax、负载电流调节频率 与小波分解阶数 的关系。进而提出了车载混合储能系统Symlets小波变换能量管理方法，实现了功率需求高频暂态分量和稳态分量在功率型储能装置和能量型储能装置之间的功率分流。最后，通过实验验证了高速公路经济性测试(Highway Fuel Economy Test, HWFET)工况下的分流效果。实验结果表明，所提出的Symlets小波变换能量管理方法可降低锂离子动力电池所承受的高频功率需求，且系统输出电压的峰值波动比Haar小波低51.8%。 |
| 关键词: 电动汽车 混合储能系统 锂离子动力电池 超级电容 Symlets小波变换 |
| DOI：DOI: 10.19783/j.cnki.pspc.210839 |
| 投稿时间：2021-07-06修订日期：2021-11-07 |
| 基金项目:国家自然科学基金青年项目资助(61803345, 51807013)；河南省科技攻关项目资助(202102210303，212102210264)；湖南省自然科学基金项目资助(2019JJ 50669)；湖南省教育厅项目资助(18B137) |
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| Symlets wavelet transform energy management method for a vehicle-mounted hybrid energy storage system |
| SHEN Yongpeng,SUN Songnan,LIU Dongqi,SUN Jianbin,ZHAO Jun |
| (1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China;
2. School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China) |
| Abstract: |
| This paper focuses on the problem of the attenuation of cycle life caused by the high frequency power demand on the lithium ion battery of electric vehicles (EV). First, the working principle of hybrid energy storage systems (HESS) composed of a lithium ion battery, with ultra-capacitor (UC) and multi-port bidirectional DC/DC converter is analyzed, and the relationships among driving cycle tmax, load current adjusting frequency and order of the wavelet decomposition are analyzed. Then an algorithm of Symlets and an Symlets wavelet transform-based power management for HESS are proposed. The proposed Symlets wavelet transform strategy is capable of identifying the high frequency transient and low frequency power demand of the EV, and allocating power components with different frequency contents to corresponding sources to achieve an optimal power splitting. Finally, experiments are performed with the HWFET driving cycle. The results show that the proposed power management method reduces high frequency loads of the lithium ion battery effectively, and decreases the peak voltage fluctuation of the HESS to 51.8% compared to that of the Haar wavelet transform-based power management strategy.
This work is supported by the Youth Fund of National Natural Science Foundation of China (No. 61803345 and No. 51807013). |
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