The motors’ flux-linkage, current and angle obtained from the system with sensors were chosen as the sample data, and the estimation model of rotor position based on relevance vector machine (RVM) was built by training the sample data. The kernel function parameter in RVM model was optimized by the particle swarm algorithm in order to increase the fitting precision and generalization ability of RVM model. It achieved higher prediction accuracy with staying at the same on-line testing time as the RVM. And because the short on-line computation, the motor can operate at 3000 r/min in sensorless control with particle swarm optimization-relevance vector machine (PSO-RVM), which is higher than support vector machine (SVM) and neural network (NN). By simulation and experiment on the test motor, it is verified that the proposed estimation model can obtain the angle of full electrical period accurately under low speed and high speed operations in current chopped control and angle position control, which has satisfactory estimation precision.

Bearingless switched reluctance motor (BSRM) adopts a doubly salient structure without windings on the rotor. BSRMs have the advantages of high rate of fault tolerance and simple structure, high power, super high speed and strong adaptability. They have broad application prospects in aerospace, flywheel energy storage, new energy and biomedical fields. Firstly, the suspension operation mechanism of a conventional double winding BSRM is described in this paper. The coupling between torque and suspension force is analyzed with a finite element method. On this basis, from the perspective of magnetic circuit optimization of the torque system and suspension system, the magnetic circuit design, decoupling mechanism and performance characteristics of self-decoupled BSRMs with different topological structures are described centering on the self-decoupled topology form of the BSRM. Finally, the study and development of BSRMs in the future are prospected based on the research status.

Under the circumstances where the demands for energy transition and grid energy storage technology are constantly increasing, the magnetic suspension bearing, as the core component of the flywheel energy storage system, its control strategy directly determines the dynamic response, energy efficiency level and operational reliability of the system. This study focuses on the control strategy of magnetic suspension bearings used in flywheel energy storage. Firstly, the basic structure, working principle, composition of the experimental platform and performance indicators of the magnetic levitation bearing in the flywheel energy storage system are summarized. Based on this, the core technical requirements for the grid-connected application of flywheel energy storage systems have been clarified, and it has been distilled that magnetic suspension bearings in grid scenarios need to meet three core performance requirements: high dynamic response, low operating energy consumption, and strong robust stability. Then, the control strategy of magnetic bearings is analyzed from three aspects: Firstly, the technical paths of the classical and high dynamic response control methods under high power conditions are analyzed; Secondly, the low energy consumption control technology route based on energy consumption sources is proposed; Thirdly, the application logic research of the strong robustness strategy under the disturbance scenarios of power grid faults is conducted. This study systematically reviews the technical framework of the flywheel energy storage magnetic suspension bearing control strategy, providing theoretical support and technical references for its efficient and stable application in the grid energy storage. Finally, the development trend of control strategies and future research directions are prospected.

As an emerging physical energy storage technology, the magnetic suspension flywheel battery boasts prominent advantages such as high working efficiency, long service life, and short charging time. However, improving the energy conversion efficiency of magnetic suspension flywheel battery systems and reducing their overall energy loss have long been critical bottleneck technologies that urgently need to be addressed for practical applications. To promote China’s green and low-carbon energy transition and accelerate the achievement of the “double carbon” goals, this paper summarizes two core components of flywheel battery systems—magnetic bearings and flywheel motors—along with two key technologies: topological structure and control strategy, based on numerous cutting-edge studies. Subsequently, focusing on further reducing the energy consumption of flywheel energy storage systems, technical prospects are extended from aspects including system material selection and intelligent integrated control, aiming to provide research directions for the low-energy-consumption operation of flywheel battery systems.

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.