基于改进核密度估计的电动出租车快速充电行为研究

电动汽车充电行为研究是充电负荷时空分布预测、充电基础设施规划和有序充电管理的基础.本文基于上海市电动出租车的实测数据,划分充电片段并提取快速充电行为特征变量,开展相关性分析以揭示变量之间的内在相关性,从工作日和休息日两个时间维度揭示快速充电行为规律,提出一种基于扩散方程的自适应扩散核密度估计模型应用于快速充电行为特征变...

Fast Charging Behavior of Electric Taxi Based on Improved Kernel Density Estimation

Charging behavior of electric vehicle (EV) is the basis of spatial-temporal distribution prediction of chargingload, charging infrastructure planning and vehicle charging management. This paper collected the electric taxischarging data in Shanghai and extracted the characteristic variables of fast charging behavior for the defined chargingsegments. The correlation analysis was carried out to reveal the intrinsic relationship between variables and the law offast charging behavior for both weekday and weekend study periods. An adaptive diffusion kernel density estimationmodel (ADKDE) was proposed on the basis of the diffusion equation and applied to the probability estimation ofcharacteristic variables of fast charging behavior. The goodness of fit test was performed to verify the effectiveness ofthe ADKDE model. The results indicate that the fast-charging behavior of electric taxis is significantly differentbetween weekday and weekend. The proposed ADKDE model can improve the accuracy of the probability modeling ofthe EV charging behavior and the accuracy of model fitting.