Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in electric vehicles based on multi-method fusion of big data is proposed. Firstly, the anomalies are removed and early fault analysis is performed by t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising. Then, different features of the vehicle that have a large influence on the battery fault are identified by factor analysis, and the faulty features are extracted by a two-way long and short-term memory network method with convolutional neural network. Finally a self-learning Bayesian network is used to diagnose the battery fault. The results show that the method can improve the accuracy of fault diagnosis by about 12% when verified with data from different vehicles, and after comparing with other methods, the method not only has higher fault diagnosis accuracy, but also reduces the response time of fault diagnosis, and shows superiority compared to graded faults, which is more in line with the practical application of engineering.

The electric machine emulator (EME), using digital simulation and power electronics to emulate the characteristics of actual machines, can greatly accelerate the testing of electric drives. However, most existing EMEs are based on typical L filter and linear controller, which causes control conflicts and bandwidth limitation. To address this issue, this paper presents an EME based on LCL filter with passive damping for a three-phase permanent magnet synchronous motor. To improve the dynamic emulating accuracy, a dual closed-loop deadbeat predictive current control algorithm is proposed, which is computationally efficient and easy to implement. The system stability is analyzed in the discrete domain, and the parameter constraints of the filter are obtained. Then, two unknown input observers are designed to compensate for the disturbance currents and voltages caused by modeling errors. Moreover, instead of the empirical method, a theoretical one considering the harmonic suppression, bandwidth, stability and resonance is presented for filter design. Finally, the performance of the proposed EME is validated through simulation and experimental results under various conditions such as machine start-up, torque step change, and speed reversal.

The article proposes a single-phase suspension control strategy with self-decoupling to address the complexity of decoupling control strategies caused by the coupling between radial suspension forces in two radial directions of bearingless switched reluctance motor (BSRM). The proposed single-phase control strategy aims to achieve stable rotor suspension by adjusting the current of the suspension winding only when one of the torque windings is excited instead of all torque wingdings. A sharing suspension windings bearingless switched reluctance motor (SSW-BSRM) is taken as research objective, the relationship between radial force and radial displacement of rotor with proposed single-phase suspension control strategy in one electrical cycle is analyzed. When the rotor speed exceeds a certain value, the feasibility of switching from three-phase suspension control to single-phase suspension control is theoretically analyzed. The expression of the critical speed of the rotor is derived, which reveals the speed requirement for achieving stable rotor suspension under the proposed single-phase suspension control strategy. The simulation and experiment are carried out on a 12/8 SSW-BSRM, and the results verify the effectiveness of the proposed single-phase control strategy in achieving stabilized rotor suspension with self-decoupling in two radial directions. Furthermore, the proposed control strategy can also be applied to the suspension control of conventional BSRMs.

In the induction electric energy transmission system applied to fix point charging of vehicles, the mutual inductance change caused by position deviation or air gap change, and the equivalent load change caused by power demand change will make the output power and efficiency fluctuate, thus affecting the output stability of the system. In order to ensure the reliability of the charging process of the system and at the same time have a strong power output capacity, this paper proposes a load segmental tracking control strategy based on the optimal power efficiency. This control strategy adjusts the equivalent load to track different power efficiency indicator curves according to different mutual inductance states of the system and power demand when the vehicle is static charging, so that it can always maintain the optimal power efficiency state according to different charging conditions and improve the system performance. Finally, simulation and experimental verification are carried out under different states of mutual inductance. The results show that this method can achieve the equivalent load segmental tracking under different mutual inductance states, and always maintain the optimal power efficiency output.

As its name indicates, the bearingless switched reluctance motor does not have windings or permanent magnets on the rotor. This has the advantages of simple structure, high reliability and easy control. The sharing-suspension-windings bearingless switched reluctance motor inherits the above characteristics, and has obvious advantages in the research field of bearingless motors with its motor structure of decoupling torque and radial force. In this paper, the sharing-suspension-windings bearingless switched reluctance motor is taken as the research object. The finite element model of the sharing-suspension-windings bearingless switched reluctance prototype is established. The electromagnetic characteristics of the prototype are analyzed. As the premise of motor suspension, the structural decoupling of torque and radial force is analyzed and experimentally verified. Then, the flux-linkage saturation of the motor is derived at the position where the stator and rotor are completely aligned and the stator and rotor are completely unaligned. The torque model of the motor is derived based on the flux-linkage saturation, and the accuracy of the model is verified by the fitting comparison between the theory and the finite element simulation. It lays a theoretical foundation for the subsequent structure optimization design research of the sharing-suspension-windings bearingless switched reluctance motor.