Abstract
Electric vehicles (EVs) are increasing popular as clean transportation. However electric vehicle (EV) users often encounter a shortage of electricity in the driving process. Especially when vehicle charging path planning system request reaches peak hour, it will increase the system calculation pressure. To promote penetration and popularity of electric vehicles, it is of critical importance to determine location and size of charging stations more scientifically and improve the computational power of path planning. In this paper, a method is developed for charge warning and path planning of insufficient energy EVs. The method utilizes the appropriate energy consumption factor to construct the electricity early warning model, analyzes the consumption of electricity in the vehicle in real time, and promptly warns the users when the vehicle has insufficient electric energy. Meanwhile, it combines the actual traffic information and map information to construct a network topology based on time weight, queuing mechanism, and charging calculation model to obtain path network model data, charging station queuing times and charging times as parameters. EV path planning problem is solved to minimize the total times of travel using the Dijkstra algorithm with the input path network model data, charging station queuing times and charging times as parameters. At the same time, the Spark computing framework is joined innovatively to improve the computing speed and solve the problem of path planning peak period. The Spark framework is utilized to parallelize the optimized Dijkstra algorithm. The experiment shows that the electric vehicle charging warning and path planning method provides an effective way for drivers of electric vehicles to charge.
Highlights
With the development of the automotive industry, electric vehicles have begun to receive the attention of the government and researchers
In order to ensure that the Dijkstra algorithm based on storage and priority queue can find the optimal path in the shortest time, and the scalability of the system for the later processing of larger scale network nodes, this paper designs and implements the parallelization of the algorithm on Spark
SHORTEST PATH ALGORITHM PARALLELIZATION In the process of path planning, since that the number of urban road network nodes is generally more than 1000, and the formed picture is complex and sparse, in order to ensure that the Dijkstra algorithm based on storage and priority queue can find the optimal path in the shortest time, and the scalability of the system for the later processing of larger scale network nodes, this paper designs and implements the parallelization of the algorithm on Spark
Summary
With the development of the automotive industry, electric vehicles have begun to receive the attention of the government and researchers. Yao et al.[22] propose an optimal charging path planning strategy for smart electric vehicles, combining electric vehicles, power grids and transportation networks to provide users with charging navigation. In order to ensure that the Dijkstra algorithm based on storage and priority queue can find the optimal path in the shortest time, and the scalability of the system for the later processing of larger scale network nodes, this paper designs and implements the parallelization of the algorithm on Spark It provides a new idea for the further improvement of navigation system. The main contributions of this paper are as follows: (1) This paper establishes a shortest time path planning strategy based on the big data platform, which integrates traffic network information, queuing information of charging station, vehicle information and other parameters, and utilizes dijkstra algorithm to find the best path quickly.
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