城市电动汽车服务网络服务能力动态评估方法研究

李逸欣; 杨军; 陈杰军

李逸欣,杨军,陈杰军.城市电动汽车服务网络服务能力动态评估方法研究[J].电测与仪表,2018,55(15):08-14.
Li Yixin,Yang Jun,Chen Jiejun.The Dynamic Assessment Method of Urban Electric Vehicle Service Network’s Service Capability[J].Electrical Measurement & Instrumentation,2018,55(15):08-14.

城市电动汽车服务网络服务能力动态评估方法研究

The Dynamic Assessment Method of Urban Electric Vehicle Service Network’s Service Capability

DOI：

中文关键词: 城市电动汽车服务网络支持向量机能效最大化原理云模型综合评价法层次分析法

英文关键词:electric vehicle service network, support vector machines, energy efficiency maximization principle, cloud model comprehensive evaluation method, analytic hierarchy process

基金项目:国家自然科学基金资助项目(51277135)

作者中文名	作者英文名	单位
李逸欣	Li Yixin	武汉大学电气工程学院
杨军	Yang Jun	武汉大学电气工程学院
陈杰军	Chen Jiejun	武汉大学电气工程学院

摘要点击次数: 1717

中文摘要:

城市电动汽车服务网络是为电动汽车运行提供能量补给的基础支撑系统。科学评估城市电动汽车服务网络服务能力,对于合理建设电动汽车服务网络和普及电动汽车具有重要意义。本文基于云模型综合评价法和层次分析法建立充电站服务能力裕度指标体系和城市各充电站权重估计指标体系,通过抽取城市服务网络中典型充电站进行服务能力评估,再根据权重评估结果对电动汽车服务网络服务能力进行评估。为了实现评估的动态性和实时性,需要根据电动汽车发展情况和充电需求的变化实时更新评价体系参数。本文基于改进灰色预测法和支持向量机,建立电动汽车预测模型对电动汽车数量进行预测,基于能效最大化原理对充电站充电负荷进行预测。根据充电站充电需求预测结果,实时更新评估体系中充电站所需充电桩数量参数,构成电动汽车服务网络服务能力动态评估体系。

英文摘要:

The urban electric vehicle service network is the basic support system to provide energy for electric vehicle (EV). Evaluation of the service capability of urban EV service network is of great significance to the rational construction of EV service network and the large-scale promotion and popularization of EVs. This paper, based on the cloud model comprehensive evaluation method and the analytic hierarchy process, the three-level nine-index charging capacity index system and the three-level six-index urban charging station weight estimation index system are established. The service capability evaluation of the EV service network is evaluated according to the evaluation result of typical charging station and the weight of the typical charging station in the service network. In order to realize the dynamic evaluation of service network service capability, it is necessary to update the evaluation system parameters in real time according to the development of electric vehicle and the change of charging demand. This paper proposes an EV demand growth model based on the improved gray forecasting method and the support vector machine to predict the number of EVs. Energy efficiency maximization principle is used to predict the charge load of the charging stations. According to the charging forecast results of the charging station, the quantitative parameters of the charging piles needed in the charging station in the evaluation system are updated in real time to form the dynamic assessment system of EVs service network’s services capability.

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