Abstract
In this study, an accurate suspension force modeling method for the magnetic bearings of flywheel batteries considering degree-of-freedom (DOF) interactions and their control system is proposed to solve the problem that the traditional flywheel battery suspension force model does not consider DOF interactions, which makes the control system control effect poor. Firstly, according to the structural characteristics of the flywheel battery used, a suspension force model is established for the radial and axial magnetic bearings, which are most seriously interfered with by the torsional degrees of freedom of the flywheel battery. Next, by proposing DOF interaction factors, the complex changes due to DOF interactions are cleverly summarized into several interaction factors applied to the fundamental model to achieve accurate suspension force modeling considering DOF interactions. To better adapt the established accurate model and ensure precise control of the flywheel battery system under various working conditions, the firefly algorithm is employed to optimize the BP neural network (FA-BPNN). This optimization regulates the control system’s parameters, enabling the achievement of optimal control parameters in different scenarios and enhancing control efficiency. Compared to the flywheel battery controlled using the fundamental model, the radial and axial displacements are reduced by more than 30 percent and 20 percent, respectively, in the uphill condition using the accurate model with FA-BPNN.
Published Version
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