基于实时出行需求和交通路况的电动汽车充电负荷预测

现有的电动汽车充电负荷预测研究中缺乏对用户出行行为和交通路况的精确描述,为此构建了时空图谱注意力网络,对基于城市兴趣点的出行需求和道路交通流量的时空分布进行学习和预测,并计及了日期类型、天气温度和交通事件的影响.通过基于出行时间指数(travel time index,TTI)的Dijkstra算法得到耗时最短的行驶路...

Charging Load Prediction of Electric Vehicle According to Real-Time Travel Demand and Traffic Conditions

Existing research on charging load prediction of electric vehicle （EV） lacks accurate descriptions of user travel behaviors and traffic conditions. Therefore, a spatio-temporal graph attention network is constructed to learn and predict the spatio-temporal distribution of travel demand considering urban points of interest and road traffic flow, taking into account the effect of date type, weather temperature and traffic events. The Dijkstra algorithm based on the travel time index （TTI） is used to obtain the shortest travel time. An EV energy consumption model that takes into account the impact of traffic conditions and air temperature, and a charging station selection decision model that considers distance and comprehensive charging cost, are both established. According to the actual travel demand and traffic data of the second ring area in Xian, the charging demand of electric vehicles for private cars, taxis and internet-hailed vehicles is predicted, and the changes in travel demand are analyzed for charging stations in various grid spaces in the city. The EV charging load prediction provides a reference and basis for the planning of charging facilities.