Orderly control of electric vehicle load can improve load characteristics of the regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future

it is impossible to make a global optimal arrangement for the access of electric vehicles. Aiming at this problem

a real-time energy management system and optimization strategy for electric vehicles based on deep long short-term memory neural networks are proposed. Firstly

a three-tier management architecture for electric vehicles including the grid layer

regional energy management system and charging station energy management system is constructed

and arge-scale electric vehicles are managed hierarchically and partitioned；Then a district-station two-level interaction strategy based on deep long short-term memory neural network is proposed

and the historical optimal solution obtained by historical load information is used to train the learning network to guide new real-time optimization；The proposed strategy can further reduce the charging cost and improve the area under the premise of ensuring the users’charging demand load peak and valley characteristics. Finally

asimulation example verifies the effectiveness and superiority of the proposed layered architecture and management strategy.