Host: The Japanese Society for Artificial Intelligence
Name : The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, 2019
Number : 33
Location : [in Japanese]
Date : June 04, 2019 - June 07, 2019
EVs which contribute to reduction of greenhouse-gas emission is studied and developed actively. However, the short cruising ranges and long charging times is a bottleneck to their further growth. One solution to the problems is utilization of FCSs (Fast Charging Stations). Since an increase in the use of FCSs is associated with a risk of longer waiting time and an overload on the power grid due to concentration of charging demand, Route navigation is under intense investigation. In this research, we optimize the charging behavior by route navigation, and resolve charging demand concentration. First, we propose a method to optimize charging behavior taking into consideration the waiting time of FCSs, and second, evaluate the effect of navigation by microscopic traffic simulator. Experimental results showed that the charging demand on FCSs is distributed by providing predicted waiting time information. We also showed that demand is further stabilized by asynchronous route re-search.