The multiobjective optimization design of dual three-phase permanent magnet synchronous hub motors (PMSHMs) is challenging due to the high dimension and huge computation cost of finite-element analysis (FEA). A new multiobjective optimization strategy is proposed for dual three-phase PMSHMs in this article. All design parameters are divided into two subspaces according to the Pearson sensitivity analysis results to improve optimization efficiency. A new training method is adopted to improve the accuracy of the approximate model. By improving a multiobjective intelligent optimization algorithm, nondominated sorting genetic algorithm (NSGA) III, a new algorithm is proposed, which will greatly shorten optimization time. It is found that the proposed optimization method can significantly improve the performance, such as smaller torque ripple and higher maximum torque for the investigated PMSHM, while the computation resources are reduced. A prototype based on the optimization results is manufactured, and experiments are conducted on the platform to verify the accuracy of the optimization results and the FEA. The effectiveness of optimization and the accuracy of the simulation are verified by the experimental results.

To improve the strong dependence of predictive control parameters on finite control set models, an improved robust finite control set predictive current control (FCS-MPCC) algorithm for permanent magnet synchronous motor (PMSM) drive systems is proposed. Firstly, the problems of complex calculation and inaccurate selection of voltage vectors in traditional FCS-MPCC are analyzed, and an extended voltage vector-based MPCC (EV ² -MPCC) based on the deadbeat principle is proposed to improve the steady-state performance of FCS-MPCC and also improve the anti-disturbance performance of the system. Secondly, the performance of PMSM under parameter mismatch is analyzed, and a sliding mode observer-based stator current and disturbance observer is proposed. The observed disturbance term is used to compensate the reference voltage term for quickly selecting the optimal voltage vector, and the estimated current replaces the traditional predicted current. This method further improves the robustness of EV ² -MPCC. Finally, the experimental results prove the effectiveness of the scheme.

A deadbeat predictive current control (DPCC) method based on extended state observer (ESO) is proposed to improve the control performance of dual three-phase permanent magnet synchronous motors (DTP-PMSMs) under parameter mismatch. A variable gain coefficient related to the exponential function of tracking error is proposed to enhance the observer's convergence and robustness under complex conditions. Furthermore, the prediction delay problem of DPCC model under digital hardware is studied, and an improved prediction method is proposed to compensate for the delay time, reducing the response time and overshoot of the system under the DPCC algorithm. The proposed ESO is combined with the improved prediction model, and DPCC with two-sample delays is employed to reduce the prediction current error and enhance the control performance of the algorithm in practical applications. The stability of the system is demonstrated using the Hurwitz Criterion. Experimental results validate the superiority and feasibility of the proposed algorithm for the control and drive of DTP-PMSMs.

With the world’s energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.

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.