高速公路电动汽车充电站日充电负荷随机模糊建模

Daily load random fuzzy modeling of highway electric vehicle charging stations

提出了一种高速公路电动汽车充电站日负荷随机模糊建模方法。首先,基于某高速公路实际数据,分析工作日与节假日两种时间类型下的日车流量概率分布特征并拟合其概率密度函数,发现该函数参数具有模糊特性。然后,采用极大似然法分别对该两种时间类型下的日车流量函数参数进行模糊特征挖掘,在置信区间内定义其边界,从而分别建立两种时间类型下的日车流量随机模糊模型。进而考虑车辆类型、充电开始时间、各时段进站车流量和初始荷电状态(State of Charge,简称SOC)等因素,建立高速公路充电站日充电负荷随机模糊模型。最后,运用蒙特卡洛模拟输出结果,验证了该方法的有效性和正确性。

The daily load random fuzzy modeling of highway electric vehicle charging station is proposed in this paper,which is based on practical highway daily traffic data to analyze the characteristic of traffic flow on weekday and weekend,and to fit the traffic probability density function.The fuzzy characteristics are shown in this function model.After utilizing the maximum likelihood method to mine the fuzzy characteristics of traffic model parameters in two kinds of period,and defining its boundary in the confidence interval,the daily traffic random fuzzy model is established.Finally,the daily highway electric vehicle charging load model is established considering charge time,vehicle types,traffic and initial state of charged（SOC）in each period.The experimental results based on Monte Carlo simulation method can verify the effectiveness of the proposed method.