Jonggrist Jongudomkarn

Interior permanent magnet (IPM) synchronous motors have gained widespread adoption in electric vehicles (EVs) owing to their durable rotor configurations, expansive operational speed range, and superior efficiency. Nonetheless, typical IPM motor designs frequently exhibit high torque ripple and constrained torque density. To address these issues, a torque enhancement method is introduced by applying both filleting and notching techniques to the stator core. These techniques help reshape the magnetic field directly at the stator, allowing for more precise control of torque production and torque ripple reduction while keeping the rotor structure unchanged. Design variables of the stator in a 12-slot/8-pole fractional-slot V-shaped IPM motor are optimized using a multi-objective genetic algorithm based on a sensitivity constraint for unidirectional operation. The electromagnetic performance of the motor is analyzed through 2D finite element simulations for both no-load and loaded scenarios. The proposed motor increases average torque by 2.45% and significantly reduces torque ripple by 47.73% compared to the conventional motor. These reflect a significant advancement in torque capability. Furthermore, the efficiency of the proposed motor reaches 93.8%. The findings suggest the potential of the proposed filleting and notching techniques for torque capability improvement in EV applications.

Due to its excellent dynamic performance, the model predictive current control (MPC) method is widely adopted in power inverter systems, such as current source inverters (CSI). However, short-circuit faults in power switches are common in CSI systems, leading to a periodic output current distortion. While previous fault diagnosis methods have primarily focused on CSI systems using conventional control strategies, this study introduces a novel approach based on the MPC method to enhance the reliability of current source converters by diagnosing short-circuit faults in power switches. The proposed method begins by calculating the phase of the reference current vector in real-time. It then leverages the cost function results from the MPC algorithm as the key diagnostic parameter derived through a comparative analysis of predicted system behavior under normal and fault conditions. In the presence of faulty switches, the algorithm identifies the phase of the reference current where the predicted currents align with the reference values. Additionally, it leverages the fact that during a fault, the MPC optimal vector remains unchanged from the moment the fault occurs. The duration and value of this unchanging optimal vector depend on the type and location of the switch fault, enabling precise fault detection and localization using both phase information and the MPC optimal current vector. This approach remains highly robust, as it analyzes both the recovery state, where reference currents are accurately tracked, and the faulty stage, where currents approach zero. To validate the effectiveness of the proposed method, a MATLAB/Simulink model was developed. Simulation results confirm the method’s robustness, as the proposed approach is capable of reliably identifying faults even under low current demand conditions, where conventional methods typically struggle. This is because traditional methods rely heavily on detecting changes in the measured current amplitude, which become increasingly subtle and difficult to distinguish as the current decreases, making fault identification unreliable. This approach provides a reliable and efficient solution for enhancing the safety and performance of CSI systems, particularly in applications where precise fault detection is crucial.

The combined heat-pump and biogas engine generator system (CHBS) serves as an alternative solution to address fluctuations in renewable generation output, offering several notable advantages such as a straightforward design that integrates a heat pump and thermal energy storage (TES) into an existing biogas plant to achieve functionality equivalent to an Energy Storage System (ESS) at cost-effective resource utilization compared to the Battery-based energy storage system. This paper introduces an adaptive control-based approach tailored for the CHBS system, designed to generate virtual inertia for frequency regulation while considering the TES’s State of Charge (SOC). The controller comprises two main loops: one for the biogas generator system and another for the heat pump. Unlike the conventional virtual inertia approach, the controller optimizes virtual inertia parameters to enhance frequency control performance by optimizing ESS charge or discharge within normal operating ranges while adjusting the parameters to minimize the power exchange rate as the TES charging state nears operational limits, ensuring that the power exchange never exceeds the available capacity of the TES. The equivalent capability of the proposed system is validated numerically by comparing it with a Battery Energy Storage System (BESS)-based system under practical conditions involving Renewable Energy Sources (RES) and fluctuating loads. The results demonstrate that the CHBS, utilizing the proposed adaptive control approach, achieves effective microgrid frequency regulation comparable to a BESS-based VI system while efficiently managing the TES’s SOC.

The double-sided axial flux (TORUS) permanent magnet (PM) machine has been shown to be an efficient structure for electric vehicles (EVs). This paper introduces the combined techniques of skewed PMs and Halbach-array arrangement to improve the TORUS-type axial flux PM motor performance. The suitable configuration for implementing this combination in the TORUS PM machine was investigated, considering two possible models. Each model of the skewed Halbach-array PM TORUS motor is optimized using response surface methodology to meet the performance requirements of an EV application. A comparative evaluation of machine performance via 3D finite element analysis is performed and detailed insight into the design and configuration of the skewed Halbach-array PM TORUS machine is presented. Under no-load conditions, the optimal skewed Halbach-array PM TORUS motor exhibits 4% higher back-EMF and 9.3% lower cogging torque compared to a benchmark TORUS motor. During on-load operation, the proposed motor shows an 8% increase in average torque compared to the benchmark, accompanied by a significant 7.8% reduction in torque ripple. The rationale behind this enhanced torque capability is explained in detail and is found to be due to enhanced flux-concentration and flux-cancellation effects within the motor. The efficiency of the proposed TORUS motor is enhanced. The results confirm the superiority of the combined skewed Halbach PM Array techniques to improve TORUS PM motor performance and suggest suitable configurations for this combined technique in EV motors.

Indirect boost matrix converter is potentially a great alternative to a back-to-back converter for permanent magnet synchronous generators based distributed generation since it can achieve a voltage-boost functionality without utilizing a bulky DC-link capacitor. Despite the success of the indirect boost matrix converter topology, there still exist some issues in the relevant control structure that must be resolved appropriately. First, the existing controls are grid-following controls, which is incapable of islanded operation. Secondly, the exiting controls generate a highly distorted current waveform, which needs to be suppressed by a passive damping resistor. Moreover, without an energy storage element, the distributed generations have no short-time power reserve unit for providing an inertial power to support the utility. In order to solve these issues, a novel approach based on a modified virtual synchronous control and a finite control set model predictive control scheme is proposed in this paper. The former is adopted to ensure proper operations in both grid-connected and islanded modes and to emulate the virtual inertial response by drawing inertial power from the input source. The latter utilizes multi-controls of real-time variables to avoid complicated coupling between the input side and the output side controls and to grant the indirect boost matrix converter with the capability of providing active filter resonance damping. Comparative studies between the proposed control and its existing counterpart are conducted with several simulations in PSCAD/EMTDC software to demonstrate the superior performances of the proposed strategy. Finally, the proposed control is verified in a scale-down experiment testbed.

Distributed generations (DG) are expected to be penetrated widely and largely in the future power grid. Virtual synchronous generator (VSG) control can play a vital role to support the frequency stability of such power grid thanks to its virtual inertia feature. However, power quality and current control are still challenging aspects of the VSG-based DGs operation under transient disturbances such as voltage sag. In this paper, a novel finite-set model predictive control (FS-MPC) based method for VSG was studied. The proposed scheme allows multiple inputs multiple outputs (MIMO) system to control voltage and current simultaneously. Under these constantly placed voltage and current constraints, the controller displayed the ability to prevent the system from overcurrent condition and to ride through fault, whereas the injection of non-sinusoidal current under conditions of unbalanced voltage sag was suppressed. Several simulation and experimental studies were conducted, which validate the superior current-limiting ability and FRT capability of the proposed strategy in comparison with existing methods.