Scheduling Strategy of Real-Time Charging Optimization of Electric Vehicles Based on Deep Learning

With the development of the times, more and more kinds of energy, and now, the country is vigorously advocating the use of new energy and clean energy, so electric vehicles came into being. Its main purpose is to replace the traditional car fuel consumption and use the new car power consumption to achieve the role of protecting the environment. But for electric cars, it needs to solve a problem with real-time charging compared to conventional cars. Because with the development of the industrial age, gas stations have been basically spread around the main road and various large residential areas, to facilitate the traditional car refueling. But electric vehicles have only been proposed and manufactured for a few years, and the charging piles have not yet been fully distributed everywhere to meet the charging needs of electric vehicles. Therefore, the charging of electric vehicles is one of the core issues of whether electric vehicles can be popularized. Therefore, the purpose of this paper is to use deep learning algorithm to optimize the scheduling of real-time charging of electric vehicles. This paper refers to the planning ideas of gas station establishment at home and abroad, and after collecting the data on the purchase and growth rate of electric vehicles in recent years, the statistical research is carried out, and then the deep learning algorithm is used to unify and study all the data, so as to get a roughly needed charging pile size and distribution map. Then the sandbox simulation is carried out to get the experimental results. Experimental results show that the strategy of charging optimization scheduling based on deep learning algorithm can help electric vehicles to realize real-time charging better, more convenient and more user-friendly.