基于多任务动态优化的EV充电引导算法设计OA

In order to improve the comprehensive performance of the vehicle-pile-network system,a charging guidance algorithm for electric vehicles(EV)is proposed based on the multi-task dynamic optimization.On the basis of the basic architecture and mathematical model of the vehicle-pile-network system,a deep learning algorithm is used to optimize the structure of the multi-task dynamic optimization algorithm,so as to obtain an improved multi-task dynamic optimization algorithm.The goals such as power grid load balance,charging demand and charging network energy efficiency are regarded as dynamic optimization tasks,and the improved algorithm is used to comprehensively analyze the multi-source data in the system,so as to realize accurate analysis of charging pile network operation and maintenance data and optimization of charging process,and improve the operation level of charging pile network and system energy efficiency.The testing experiments were conducted by using EV charging data as samples,and the horizontal comparisons with other similar algorithms were carried out.The results show that,in comparison with other comparison algorithms,the load volatility of the proposed algorithm is less than 0.3%,which provides a new technical scheme for the improvement of the energy efficiency of the EV charging pile and the development of intelligence.