A High-Performance AEFC Strategy with Intelligent Parameter Tuning for Magnetic Suspension Flywheel Battery

In order to reduce the influence of external radial disturbances on the control accuracy and stability of the vehicle magnetic suspension flywheel battery system during driving, and to further enhance the system’s disturbance rejection ability, this paper designs a control method based on the Accelerated Engineering Fastest Controller (AEFC) and the improved differential optimization algorithm. A mathematical model of the flywheel battery system is established, and the AEFC scheme with engineering disturbance rejection is adopted in the control loop. The improved differential optimization algorithm is used to obtain the optimal control parameters of AEFC, and a multi-criteria optimization function combining tracking error and smoothness is established. The overall control scheme effectively integrates the characteristics of rapid tracking, interference suppression, and rapid parameter adjustment. The experimental results show that compared with the Engineering Fastest Controller (EFC), in the vehicle turning process, the AEFC controller can reduce the offset by 28% during vehicle driving, and compared with the traditional PID control, it can reduce the offset by 41.94%. In the process of vehicle uphill and speed change, the control effect of AEFC also has a significant improvement.