基于融合模型驱动和数据驱动的电动汽车充电负荷预测

EV Charging Load Forecasting Based on Model Driven and Data Driven

目前对电动汽车充电负荷预测的研究未解决实际应用中无法实时获得起迄点(origin destination,OD)数据并考虑车主的现实感知决策心理的问题。针对此问题,在综合考虑动态交通信息、环境温度、实时车流量、排队论等方法的基础上,建立一种城市交通系统OD流量预测的新型深度学习架构,完成电动汽车充电负荷时空分布预测。首先分析城市交通路网信息、日类型和天气等多种因素对电动汽车行驶规律的影响,通过双向长短期记忆递归神经网络算法分别获得相应私家车和出租车驾驶行为的起讫点。其次引入考虑动态交通信息及交通路口流量的路段阻抗与节点阻抗模型和考虑环境温度和车辆实时速度的空调能耗模型,采用实时Dijkstra算法为电动汽车起讫点规划最小出行成本的行驶路径,模拟电动汽车用户的驾驶行为。最终在不同应用场景下完成不同类型电动汽车的路径规划试验和充电需求预测试验。结果表明,所得充电需求时空分布特征与客观需求相符合。

At present,research on predicting the charging load of electric vehicles has not solved the problem of not being able to obtain real-time origin destination(OD)data in practical applications and considering the actual perception and decision-making psychology of car owners.In response to this issue,based on comprehensive consideration of dynamic traffic information,environmental temperature,real-time vehicle flow,queuing theory,and other methods,this paper establishes a new in-depth learning framework for urban transportation system OD flow prediction,and completes the spatiotemporal distribution prediction of electric vehicle charging load.This paper firstly analyzes the impact of various factors such as urban traffic network information,day type,and weather on the driving rules of electric vehicles.The starting and ending points of corresponding private car and taxi driving behaviors are obtained through a bidirectional short-term memory recursive neural network algorithm.Secondly,a link impedance and node impedance model considering dynamic traffic information and intersection flow is introduced,as well as an air conditioning energy consumption model considering ambient temperature and vehicle real-time speed.The real-time Dijkstra algorithm is used to plan the minimum travel cost travel path for the starting and ending points of electric vehicles,simulating the driving behavior of electric vehicle users.Finally,path planning experiments and charging demand prediction experiments for different types of electric vehicles are completed in different application scenarios.The results show that the spatiotemporal distribution characteristics of the charging demand obtained are consistent with the objective demand.