复合储能纯电动汽车能量管理策略研究  

Research on the energy management strategy of pure electric vehicles with composite energy storage

作　　者：刘瑞元 孙宾宾[1] 张铁柱 孟令菊 LIU Ruiyuan;SUN Binbin;ZHANG Tiezhu;MENG Lingju(School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo Shandong 255000,China)

机构地区：[1]山东理工大学交通与车辆工程学院,山东淄博255000

出　　处：《电源技术》2022年第9期1071-1075,共5页Chinese Journal of Power Sources

基　　金：国家自然科学青年基金(51805301);山东省重点研发计划(国际科技合作)项目(2019GHZ016);中国博士后基金面上项目(2020M680091);山东省博士后创新项目(202003042)。

摘　　要：运行工况是影响纯电动汽车经济性的重要因素,提出了一种应用于锂电池-飞轮电池复合储能纯电动汽车的基于支持向量机(SVM)与小波变换(WT)的自适应小波能量管理策略,在支持向量机模型对汽车行驶工况进行实时识别的基础上,采用不同的小波分解层数对整车需求功率进行分解,将高频分量与低频分量分别交给飞轮电池与锂电池承担。与采用固定分解层数的传统小波能量管理策略进行了对比,仿真结果表明:自适应小波能量管理策略降低了高频电流对锂电池的冲击,抑制了锂电池温升,提高了纯电动汽车的经济性。Operating conditions are an important factor affecting the economy of pure electric vehicles.Therefore,an adaptive wavelet energy management strategy based on support vector machine(SVM) and wavelet transform(WT) was proposed for lithium battery-flywheel battery hybrid energy storage pure electric vehicle was proposed.Based on the support vector machine model for real-time identification of vehicle driving conditions,different wavelet decomposition layers are used to decompose the required power of the entire vehicle,and the high-frequency components and low-frequency components are respectively borne by the flywheel battery and the lithium battery.Compared with the traditional wavelet energy management strategy that uses a fixed number of decomposition layers,the simulation results show that the adaptive wavelet energy management strategy effectively reduces the impact of high-frequency current on the lithium battery,suppresses the temperature rise of the lithium battery,and improves the economy of pure electric vehicles.

关 键 词：飞轮电池 支持向量机 小波变换 能量管理 

分 类 号：TM912[电气工程—电力电子与电力传动]