Harmonic excitation synchronous machines (HESMs) with brushless rotor excitation are promising for a range of applications, especially for electric vehicles, due to their compact and reliable design free from slip rings and additional exciters. However, the computer-based optimization of HESMs remains a challenging task, with effective approaches still being actively researched. This article proposes an efficient method for optimizing HESM parameters that accounts for pulse-width modulation effects, utilizing a reduced-order model and a tailored Nelder-Mead algorithm with an integrated internal optimization procedure. The proposed algorithmic enhancements significantly reduced the number of iterations required for convergence and improved key machine characteristics, namely lower losses and reduced current consumption.

Motor drives for cordless vacuum cleaners (VCs) is an emerging area, which involves ultra-high-speed (UHS) machines, rotating at frequencies exceeding 2 kHz. Simultaneously, their inverters are low-cost and control systems have to solve problems, inherent to this application. Since the fan load varies parabolically, the current at low speeds is close to zero, which significantly increases sensing error and worsens control. Moreover, parabolic torque increases dynamic load to motor and decreases stability of PI controllers. The UHS operation requires high controller gains, which makes them sensitive to noise at low speeds and the sensorless estimator has to operate with lower sampling frequency. This paper proposes simple solutions to all four problems, which do not require additional hardware and powerful microcontrollers. In order to operate at low loads, the authors proposed minimum current limiter, which limits signal to noise ratio. For proper handling of parabolic load, the paper suggests dynamic tuning of rate limiter. In order to guarantee stable operation of PI controllers in wide speed range, the authors recommended dynamic recalculation of controllers' gains. Then, the paper proposes simple modifications to conventional back-EMF-based estimator, which adapt it for operation with low discretization ratio, reaching 8.2. Finally, this study verifies the feasibility of all modifications, evaluates performance of the developed VC motor drive and compares it with the performance of competitive devices. It demonstrates that the developed motor drive operates at 132 krpm, consumes power of 880 W and demonstrates overall efficiency of 37.5%, which significantly surpasses the competitors.

This paper presents the design, development, and experimental evaluation of a high-speed integrated motor drive for cordless vacuum cleaners. The drive employs an axial integration structure, where the inverter PCB is soldered directly to the motor terminals, reducing wiring losses and improving electromagnetic compatibility. A novel PMSM with a star-shaped stator and toroidal winding provides high power density, reduced weight, and enhanced cooling. The motor operates at 120,000rpm, generating airflow enabling forced-air cooling of both the motor and the inverter. The inverter design is constrained by strict geometric and thermal requirements. Its layout is based on the symmetric positioning of six MOSFETs around the motor axis, achieving uniform airflow distribution and reducing local overheating. Experimental tests under variable suction and airflow conditions, simulating realistic VC operation, demonstrated improved thermal management compared with existing commercial solutions, confirming the effectiveness of the proposed integrated drive.

This article proposes an improved model-free predictive current control (MFPCC) scheme for dual three-phase permanent-magnet synchronous motor (DTP-PMSM) drives, in which virtual harmonic back electromotive force (back-EMF) identification is incorporated to achieve effective harmonic suppression. First, a comprehensive harmonic modeling framework is developed, showing that nonsinusoidal back-EMFs and voltage-source inverter (VSI) nonlinearities give rise to 12th- and 6th-order harmonic components in the dq and dqz subspaces, respectively. Second, to address these harmonic effects, a virtual harmonic back-EMF representation is introduced to unify VSI-induced distortions and spatial harmonic back-EMFs into an equivalent disturbance model. Based on this model, a current-mapping principle is developed to estimate the amplitude and phase deviations of the virtual harmonic back-EMFs. Third, the identified virtual harmonic back-EMFs are incorporated as a compensation term into the ULM voltage input, and the resulting augmented input is processed by the linear extended state observer (LESO)-based prediction to suppress harmonic disturbances beyond the LESO bandwidth. This mechanism enables robust harmonic suppression without complicated algorithms or extensive parameter tuning. Finally, experimental results validate the effectiveness of the proposed method over a wide speed range.

In order to reduce the cost of the sensor for switched reluctance motors (SRMs) and improve the accuracy of position estimation, a sensorless control strategy based on the orthogonal flux linkage and improved phase-locked loop (PLL) is proposed in this article, along with a fault-tolerant control scheme to ensure operational performance under single-phase fault conditions. The proposed control strategy is applied during the medium-high speed stage. First, the nonlinear rotor flux linkage containing position and speed information is expanded into a Fourier form. By using a dual second-order generalized integrator quadrature signal generator, the DC bias and high-order harmonics are eliminated, and a set of mutually orthogonal flux linkage signals is generated. Then, the differential of the motor speed is taken as the extended state variable of the extended state observer, the rotor estimated position and speed can be obtained by using the extended state observer-based phase-locked loop to process the orthogonal signals. Furthermore, a fault-tolerant control strategy is proposed to enhance the performance of this control strategy in the case of single-phase fault. Finally, a comparative experiment is conducted on a six-phase 12/10 SRM against the traditional method. The experimental results show that this control strategy not only validates the superiority of the proposed sensorless control strategy, but also demonstrates its satisfactory estimation performance under single-fault conditions.

To enhance the performance of the speed loop and current loop of permanent magnet synchronous motor (PMSM), this article proposes a model-free sliding mode speed controller (MFSMSC) and a model-free predictive current controller (MFPCC) based on an improved ultra-local model. First, the conventional MFPCC with an extended state observer (ESO) based on the ultra-local model significantly improves the robustness against motor parameter mismatches. However, in the conventional MFPCC, the estimated disturbance term includes both linear terms with system states and unknown nonlinear terms, which leads to inaccuracies in the observation results. Therefore, a new ultra-local model is proposed to improve the estimation accuracy of disturbances. In addition, an improved sliding mode observer (SMO) is used instead of the ESO to enhance the high-frequency noise suppression performance. On the other hand, to improve the dynamic performance and robustness of the speed loop, a MFSMSC is proposed to replace the conventional proportional-integral (PI) control. Comparative experiments are conducted on a surface-mounted PMSM (SPMSM) test platform to verify the effectiveness and superiority of the proposed control method.

This article proposes an enhanced model predictive current control (MPCC) strategy to address the challenges of achieving high-performance flux-weakening operation in permanent magnet synchronous motor drives. The core innovation lies in a synergistic integration of three key techniques within the MPCC framework. First, a novel flux-weakening controller is developed, which generates a continuous demagnetizing current reference via virtual voltage feedback, effectively reconciling the inherent discrete action of MPCC with the requirement for smooth trajectory tracking in the flux-weakening region. Second, to enhance control resolution, extended virtual vectors are synthesized from basic voltage vectors, enriching the actuation set and improving steady-state accuracy. Third, a nonlinear extended state observer is incorporated to estimate and compensate for lumped disturbances in real-time, thereby significantly improving the robustness of the system against parameter mismatches and unmodeled dynamics. Experimental results validate that the proposed method not only ensures stable operation over a wide speed range but also effectively suppresses current ripple and eliminates trajectory deviation caused by parameter inaccuracies, demonstrating superior dynamic performance and robustness compared to conventional approaches.

Due to the latest trends in green energy and energy saving, the algorithms focusing on increasing efficiency are becoming more and more important. For this reason, maximum torque per ampere (MTPA) algorithms for synchronous motors have become an indispensable part of their control systems. One of the most popular approaches to the implementation of these algorithms is the use of conventional MTPA equations together with predefined dependencies of motor parameters, where the most important are motor direct and quadrature inductances. To properly evaluate and calculate motor inductances, this article proposes a new simple technique, capable of operating in low-cost systems with weak microcontrollers (MCUs). The main feature of the proposed technique is inductance evaluation along the MTPA curve, which simplifies inductance measurements and decreases the complexity of this problem from O(x(2)) to O(x). Furthermore, it makes algorithm tuning easier and eliminates 2-D computations at every calculation step, which decreases the computational burden to MCU by about three times. Furthermore, it significantly saves memory and microcontroller resources, which is essential for low-cost systems. This technique was implemented and verified in a home appliances laboratory and later it was approved for usage in mass production.

To address the problems of flux linkage nonlinearity and reduce the cost of estimation in position estimation of sensorless switched reluctance motors (SRMs) at medium to high speeds, a sensorless SRM control scheme based on harmonic elimination and interval position estimation is proposed in this article. In addition, a single-phase fault-tolerant control strategy is proposed to address the possible occurrence of single-phase faults. First, the flux linkage-related quantity containing rotor position information is processed by Fourier expansion to obtain the flux linkage function. Then, by enhancing the harmonic signal elimination (HSE) operator, a multistage harmonic elimination flux observer (MSHE-FO) is designed, which enables the generation of a set of orthogonal flux linkage signals. Subsequently, the proposed interval search estimation (ISE) method is utilized to obtain the optimal estimated position. Furthermore, this article proposes a single-phase fault-tolerant control strategy based on the logical relationship between the phase current and the phase winding operational mode. Finally, experiments are conducted on a six-phase 12/10 SRM experimental platform. The experimental result not only verifies that the proposed sensorless control strategy offers higher control accuracy than the conventional phase-locked loop (PLL) method but also confirms the effectiveness of the fault-tolerant control strategy.

To address the issues of decreased control performance and insufficient current control capability of the traditional Torque Sharing Function (TSF) strategy for Switched Reluctance Motors (SRMs) under conditions of higher-speed operation, the improved TSF control strategy integrating dynamic region division and online torque correction is proposed. By analyzing the dynamic characteristics of the Torque Ampere Ratio (TAR) during the commutation process of incoming/outgoing phases, the commutation regions are adaptively divided in combination with the speed operating conditions, and zonal current reference profiles (CRP) are designed to optimize the phase current waveforms. Furthermore, an online correction mechanism for torque prediction deviation is introduced to achieve real-time compensation for the deviation between allocated torque and output capability. The experiments show that the proposed strategy significantly suppresses torque ripple and reduces copper losses over a wide range of speeds. Compared with the traditional TSF method, it effectively improves the torque tracking accuracy and the adaptability to operating conditions, and enables the efficient operation of SRMs within a wide range of speeds.

This article presents a sensorless control method of switched reluctance motor (SRM) based on flux linkage nonlinear modeling. This method combines radial basis function neural network () with improved coyote optimization algorithm (ICOA) to obtain accurate rotor position information. The radial basis function neural network (RBFNN) has fast convergence speed and strong approximation ability. The ICOA can dynamically adjust the parameters and structure of RBFNN, and reduce modeling errors by adaptive adjustment of the number of hidden layer nodes. In addition, the rotation speed of the rotor at any position is estimated to realize the accurate commutation of the motor and ensure the stable operation of the whole closed-loop system. Finally, the effectiveness of the RBFNN based on the ICOA is verified through the six-phase 12/10SRM experimental platform.

In this article, an improved torque sharing function (TSF) based on multiobjective optimization and phase-segmented control is presented in switched reluctance motor (SRM). First, the nondominated sorting genetic algorithm (NSGA) is adopted to globally optimize the full-phase turn-on angle, turn-off angle, and overlap angle, ensuring consistency of electromagnetic parameters across phases. A double-layer cooperative operation system (DCOS) with spatiotemporal symmetry is proposed for the windings of a six-phase motor, which is beneficial to the complementary superposition of torque vectors. The designed composite TSF incorporates nonlinear torque compensation to adjust torque distribution in commutation regions, mitigating torque deviations caused by inductance nonlinearity, current hysteresis, and residual currents. A bivariate cubic spline interpolation (BCSI) method is proposed to construct a globally smooth analytical torque model. The experimental results demonstrate that the proposed control method exhibits superior performance in aspects such as torque ripple reduction and copper loss minimization.

Energy management strategies (EMS) for plug-in hybrid electric buses (PHEBs) typically prioritize fuel economy, often leading to frequent operating mode switches that neglect driving comfort and degrade the overall ride quality. To address this issue, this study proposes a novel adaptive equivalent consumption minimization strategy (A-ECMS) that integrates both a driving comfort evaluation and a mode-switching penalty into the cost function. The Whale Optimization Algorithm (WOA) is employed to globally optimize the penalty parameters, achieving an optimal trade-off between fuel efficiency and mode-switching frequency. The proposed EMS is evaluated under the WLTP and CHTC driving cycles, and validated via Hardware-in-the-Loop (HIL) testing. The numerical results demonstrate that under the CHTC cycle, the proposed strategy significantly suppresses frequent mode transitions and reduces acceleration fluctuations, at the cost of an acceptable 6.73% increase in fuel consumption. Furthermore, the HIL tests confirm the real-time reliability of the strategy, with the maximum output errors for the engine and motor torque restricted to only 4.4% and 5.8%, respectively. These results highlight the quantitative impact and practical feasibility of considering driving comfort in PHEB energy management.

This article develops an enhanced torque sharing function (TSF) that integrates an asymmetric double-layer cooperative operation system (ADCOS) with multiobjective optimization for torque ripple suppression. The proposed strategy incorporates a nonlinear modulation factor, a Gaussian compensation term, and a torque redistribution mechanism. A novel asymmetric coordination scheme is designed for multiphase switched reluctance motors (SRMs), in which phases A, C, and E operate separately from phases B, D, and F. Based on a 60(degrees) electrical phase difference, this scheme establishes a complementary torque superposition framework between the two phase groups, thereby improving motor operational stability and average torque. All phase conduction angles are globally optimized via the nondominated sorting genetic algorithm (NSGA) with a dynamic penalty mechanism. Experimental results verify that the proposed strategy outperforms conventional methods in reducing both torque ripple and copper loss.

Recent advancements in battery materials have significantly improved their charge-to-weight ratio providing fast progress in battery-dependent technologies and expanding the market for cordless devices in particular. Cordless vacuum cleaners have become especially popular due to enhanced convenience compared to wired models. This growing demand has boosted research activity toward further size and weight reduction. As a result, motors and impellers have been considerably scaled down to just a few centimeters in diameter, while the operating frequency of ac voltage applied to energize the motors is now increased to exceed 2 kHz. However, the reduced size and mass of the motor metal components have negatively affected cooling efficiency, making heat dissipation a major challenge in the development of compact, high-speed motors. To overcome this thermal issue, this paper proposes a novel motor design having a six-pointed star-shaped stator with toroidal windings placed at its vertices. This configuration increases the stator surface area ensuring the improvement of the heat dissipation. Moreover, the enlarged spacing between windings improves cooling conditions allowing the motor to conduct higher current through the stator windings without overheating. A working prototype of the proposed motor was developed and tested to evaluate its performance and thermal characteristics. An experiment-based comparative analysis with commercial vacuum cleaner motors showed that the prototype outperformed them in both operational and thermal parameters confirming the effectiveness of the proposed concept.

To address vehicle shimmy compromising driving safety and stability, an active shimmy suppression scheme of vehicle shimmy system based on electromagnetic linear actuator (EMLA) is proposed. The dynamic model of vehicle shimmy system coupled with EMLA is established. The Hopf bifurcation characteristics of the vehicle shimmy system are analyzed by numerical continuation method, the stable region and limit cycle oscillation (LCO) amplitude of the vehicle shimmy system are obtained. An improved super-twisting sliding mode active disturbance rejection controller (ISTSM-ADRC) is proposed, which designs a continuous and smooth improved fal (Ifal) function to overcome the non-differentiability problem of the traditional fal function, and an ISTSM control law based on the tanh function to enhance response speed and disturbance rejection. The results show that the lower suspension stiffness and larger suspension damping can effectively suppress the vehicle shimmy, and the ISTSM-ADRC effectively reduces the response time of the system to reach a stable state, and has greater dynamic response performance and robustness, which provides guiding significance for vehicle shimmy suppression control in engineering application.

This article proposes a novel physics-informed neural network (PINN) controller for five-phase permanent magnet synchronous motors (PMSMs) that addresses the limitations of existing dual-plane current control methods. The notable constraints of existing machine learning controllers for PMSMs, particularly with respect to interpretability and stability, are examined to enable offline interpretability and online deployability. A neural network (NN) control model is derived for dual-plane operation of five-phase PMSMs by extending the NN control frameworks of three-phase PMSMs. The motor’s prior physical information is deeply integrated into the training process to enhance generalization. A hybrid optimization strategy is proposed to improve the fit between the predicted and actual operating voltages and to suppress prediction fluctuations, thereby better aligning the predicted values with the exact operational requirements. Experimental results validate the excellent steady-state performance and robust antiinterference performance of the proposed controller.

This paper reviews recent developments in the design optimization of electrical drive systems for electric vehicles (EVs) and proposes a pathway to develop next-generation AI design platforms that integrate system-level optimization methods and digital twins. First, a comprehensive review is presented to five design optimization models for EV motors, including multiphysics, multiobjective, multimode, robust, and topology optimization, as well as six efficient optimization strategies, such as multilevel optimization and AI-based approaches. Several recommendations on the practical application of these optimization strategies are also presented. Second, representative optimization methods for power converters and control systems of EV drives are summarized. Third, application-oriented and robust system-level design optimization strategies for EV drive systems are discussed. Finally, two proposals are presented and discussed for the design of next-generation EV drive systems and their integration with battery management systems. They are AI-powered automatic design optimization platforms that integrate large language models and a digital-twin-assisted system-level optimization framework. Two case studies on in-wheel motors and drive systems are also included to demonstrate the performance and effectiveness of various optimization methods.

This article proposes an enhanced direct instantaneous torque control (DITC) based on dynamic commutation strategy, which can address the torque ripple in switched reluctance motor (SRM) caused by nonlinear inductance and the discontinuous torque output capability in the commutation region. This method first analyzes the excitation characteristics of the SRM within each control cycle and dynamically updates the optimal turn-on and turn-off angles. On this basis, the adaptive dynamic commutation strategy is introduced to precisely coordinate the torque output capability of adjacent phases in the commutation region. This method enables maximized torque utilization of the excitation phase over a wide speed range. Finally, the experimental results show that compared to conventional control strategies, this method significantly suppresses torque ripple while effectively reducing the rms current. Meanwhile, it still maintains the transient response performance of DITC.