Open AccessRoute Planning and Charging Navigation Strategy for Electric Vehicles Based on Real-time Traffic Information and Grid Information
Author(s) -
Qiang Xing,
Chen Zhong,
Ziqi Zhang,
Xueliang Huang,
Xiaohui Li
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/752/1/012011
Subject(s) - intersection (aeronautics) , dijkstra's algorithm , floating car data , grid , computer science , real time computing , real time data , node (physics) , vehicle information and communication system , simulation , transport engineering , shortest path problem , engineering , road traffic , traffic congestion , graph , structural engineering , theoretical computer science , world wide web , geometry , mathematics
. Aiming at the characteristics of Electric Vehicles (EVs) combined with transportation and mobile load, and their charging and driving behaviors will interact with the traffic system and power grid system. On the basis of this, a route planning and charging navigation strategy for EVs based on real-time traffic information and grid information was presented in this paper. First, a dynamic traffic network model was established according to the time-varying feature of traffic information. And also, the road resistance model with road segment impedance and intersection node impedance was proposed depending on the attribute of urban roads. Furthermore, based on the establishment of traffic network model, distribution network model and single EV model, a multi-objective optimization function integrated with the road travel time, the charging station load and the quantity of vehicles entering into the station was determined, which was solved to recommend the optimal driving and charging paths by dynamic Dijkstra dynamic search algorithm. At last, the actual road network of a certain zone in Nanjing was selected as an example to analyze the spatial-temporal distribution of EV charging load, and evaluated the impact of its charging and driving on traffic network and distribution network. Simulation results demonstrate its effectiveness and feasibility.