Electric vehicle charging station methods and self adaptive resource allocation methods for express charge stations

One of the most effective precautions to limit the environmental pollution is increasing electric vehicle usage in transportation area. In particular, electric vehicles are an important factor in reducing CO2 emissions. There are beneficial effects of using electric vehicles, however the battery charging time is 30 minutes at the fast charging stations. The length of the charging duration, increases the number of vehicles waiting at a charging station and the time the vehicles have to wait at the stations. The waiting time can be reduced by directing the vehicles to the right directions. Moreover, for some of the vehicles, this time may be reduced by the means of express charging stations. This study has two goals. The first goal is developing station selection methods for vehicles, the second is developing express charge point management methods for stations. There are many studies in literature on station selection methods. The charging station selection algorithms differentiate from each other according to the information types the stations and vehicles send and collect, how they use the information and the performance criteria they use for decision making. In the vii literature, methods of sending the information of the vehicles to the central system and receiving the information from the central system have been frequently worked. These methods may not be used by those who are hesitant about sharing personal information. In addition, there may be disruptions in usage in areas where transportation communication infrastructure is insufficient. In these cases, the methods that enable vehicles make their decisions themselves can be used. There are a limited number of models in the literature in which vehicles take their own decisions and do not report to the central system. There is a very limited number of studies on express charging. In the literature, to the best of our knowledge- there is no study that dynamically changes the number of charge points separated for the express charge. For express charging stations, firstly, a method of dynamically sorting vehicles waiting for charging in the station according to their needs has been developed. In this method, the sorting model is used according to the highest rate of response time (HRRN) used in the CPU scheduling field. Vehicles that want to charge for a short time on this method will be prevented from waiting for long periods in the system. This method has also been examined in a multi-station competitive environment. Another method developed for express charging methods is to dynamically change the number of express charge points. This method divides the vehicles into two classes and makes automatic server assignment to keep the ratio of the waiting times of vehicles in different classes at a certain level. In this way, the station monitors its own performance and controls its operation, that is, it has a self-controlling structure. The developed method was investigated under different conditions. In addition, performance in a competitive environment has been observed by being transferred to a multiple simulation environment. Results show that the methods developed are performing as expected.