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This article presents a model-free predictive current control (MFPCC) method for dual three-phase permanent magnet synchronous machines with an optimal modulation pattern. First, an ultralocal model based on the dq subspace is established, and a novel nonlinear disturbance observer is designed to effectively compensate for voltage distortion caused by inverter characteristics. Additionally, the introduction of an intermediate variable eliminates the estimation peaks in the high-frequency state, improving both steady-state and transient performance of the controller while suppressing the influence of parameter mismatch on control performance. Second, two candidate modulation patterns and their corresponding pulse synthesis patterns are predefined, and the closed-loop control of the xy subspace is realized through an efficient cost function. The switching mode with the best xy current performance at the current moment is then switched in real time to obtain improved steady-state performance and deeper harmonic current suppression. Finally, the experimental results have verified the effectiveness of the proposed MFPCC method.

This article proposes a predictive cascaded speed and current control to improve the dynamic performance and robustness of permanent magnet synchronous motor drives against machine parameter variations. First, a novel model predictive speed controller is developed to achieve high performance and a wide speed range for the speed outer loop, which can minimize the system's inertial link overshoot and settling time. Second, the current inner loop utilizes an incremental current model to eliminate the complexities and variations caused by flux linkage parameters in complex working scenarios. Third, the enhanced model reference adaptive system is proposed for parameter identification, integrating the new fuzzy logic controller within the framework. Additionally, comprehensive compensation of the inverter distortion voltage is implemented to ensure improved dynamic and steady-state effectiveness, resulting in enhanced performance and higher control accuracy. Finally, experimental validation on a dSPACE system with a sampling frequency of 20 kHz demonstrates the stability and effectiveness of the proposed method.

The power-split hybrid electric vehicle (PS-HEV) is a complex and high-dimensional system that only through overall system-level optimization, considering the synergies and complementation between different components and subsystems, can achieve the best overall performance and benefits, which has been a technology challenge in the study. This article presents a system-level optimization method using nested design for engines and motors to solve the problem of the coupling between the parameters of the physical system and those of the control algorithm. Cohen's f effect is employed to conduct sensitivity analysis and determine the influence of the selected design parameters on the optimization objective. The Kriging model and NSGA II algorithm are proposed to optimize the motor, whereas the fire hawk algorithm is selected to optimize the fuel consumption objective. To evaluate the performance of the optimal torque distribution, a comparison is made with the original PS-HEV system, and the feasibility of the proposed scheme is verified through the hardware-in-the-loop test. The results demonstrate the efficacy of the proposed system-level optimization method in achieving superior performance of the PS-HEV system.

In a fixed pole bearingless induction motor (FPBIM), the influence of the suspension winding is neglected in the traditional mathematical model, leading to the inaccurate speed estimation of speed sensorless control. Aiming at this problem, this article proposes an improved mathematical model. First, considering the influence of the suspension winding, the resistance matrix and inductance matrix are extended based on the traditional natural coordinate model and the FPBIM's natural coordinate model (in the a-b-c stationary frame) is established. Second, through the rotation transformation by an extended transformation matrix, the FPBIM's two-phase stationary coordinate model (in the α–β stationary frame) is obtained and the flux linkage equations are extracted from it. Finally, to verify the effectiveness and feasibility of this improved mathematical model, it is applied to the speed sensorless control based on model reference adaptive system. The simulations and experiments show that the FPBIM improved mathematical model gets more accurate speed estimation and better rotation performance under no-load, load, load disturbance and displacement disturbance, while the suspension performance is not affected.

The plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this article proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working conditions are calculated by using the dynamic programming algorithm. The neural network is trained by these data in the offline part so that it can provide the appropriate gear-switching signal in time in the online model. For power-split control, this article mainly selects an improved equivalent fuel consumption minimization strategy (ECMS) and uses the grey wolf optimization algorithm to iteratively solve the optimal equivalent factor. The proposed control strategy has not only been verified under various operating conditions but also verified by relevant experiments on the hardware-in-the-loop platform. The results show that the proposed strategy has better fuel economy than ECMS and the rule-based strategy under the provided mixed operating conditions. Compared with the dynamic programming algorithm, the fuel consumption is only increased by 3.23%, but the problem of the curse of dimensionality is avoided.