Research on AI Energy Management of Ultra-Speed Magnetic Levitation Flywheel Energy Storage

In this paper, an intelligent energy management scheme for ultra-high-speed maglev flywheel energy storage system (FESS) based on Deep Q Network (DQN) algorithm is proposed, and the DQN model is built using the PyTorch framework to discretize the charge-discharge threshold of the flywheel energy storage system according to the system state. In order to realize the simulation and control of the system, Simulink is used for system simulation, and the interaction with the DQN model is realized through the interface. In this paper, the system design, model implementation, training process and simulation results are introduced in detail, and the effectiveness of the intelligent energy management system in improving energy utilization efficiency and system stability is proved.