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The study presents an optimal control approach for managing a hybrid Photovoltaic/Wind Turbine/Battery system in an isolated area. The system includes multiple energy sources connected to a DC bus through DC/DC converters for maximum power point tracking. The proposed hybrid MPPT approach (HMPPT) manages the energy production from different sources, while the power flow method is used to balance the load and renewable power. The study shows that integrating the HMPPT algorithm and power flow approach results in improved system performance, including increased power generation and reduced stress on the batteries. The study also proposes an accurate sizing method to further improve system efficiency. The study demonstrates the effectiveness of the proposed approach by presenting results for twelve different days with varying weather conditions. The results show that the proposed approach effectively manages the energy production and load, resulting in optimal system performance. This study provides valuable insights into the optimal control of hybrid renewable energy systems, and highlights the importance of considering different energy sources and optimal sizing for maximizing system efficiency.

This paper presents a detailed analysis and comparative study of three torque control methodologies with fuzzy logic, namely direct torque control (DTC), fuzzy direct torque control (FDTC), and model predictive direct torque control (MPDTC), for PMSM control applied to an electric vehicle. The three control strategies are designed and developed to control torque in order to achieve vehicle requirements, such as minimum torque and flux ripples, fast dynamic response, and maximum efficiency. To enhance the performance and efficiency of the overall drive, a bidirectional DC/DC buck-boost converter is connected to the Li-ion battery. In addition, a fuzzy logic controller (FLC) is used in the outer loop to control the speed of the PMSM. As a result, the tuning difficulty of the conventional proportional-integral (PI) controller is avoided and the dynamic speed response is improved. Simulation results obtained from the three control techniques establish that the proposed system via the MPDTC technique reduces the torque ripples, flux ripples, reduces the THD of the PMSM current, and achieves a faster transient response. Additionally, the MPTDC technique enabled the electric vehicle to cover the longest distance, with approximately 110.72 km in a charging cycle. The real-time simulation is developed using the RT LAB simulator, and the obtained results confirm the superiority of the MPDTC technique over conventional DTC and FDTC techniques.

This paper proposes a novel Fuzzy-MPDTC control applied to a fuel cell battery electric vehicle whose traction is ensured using a permanent magnet synchronous motor (PMSM). On the traction side, model predictive direct torque control (MPDTC) is used to control PMSM torque, and guarantee minimum torque and current ripples while ensuring satisfactory speed tracking. On the sources side, an energy management strategy (EMS) based on fuzzy logic is proposed, it aims to distribute power over energy sources rationally and satisfy the load power demand. To assess these techniques, a driving cycle under different operating modes, namely cruising, acceleration, idling and regenerative braking is proposed. Real-time simulation is developed using the RT LAB platform and the obtained results match those obtained in numerical simulation using MATLAB/Simulink. The results show a good performance of the whole system, where the proposed MPDTC minimized the torque and flux ripples with 54.54% and 77%, respectively, compared to the conventional DTC and reduced the THD of the PMSM current with 53.37%. Furthermore, the proposed EMS based on fuzzy logic shows good performance and keeps the battery SOC within safe limits under the proposed speed profile and international NYCC driving cycle. These aforementioned results confirm the robustness and effectiveness of the proposed control techniques.

High performance and comfort are key features recommended in hybrid electric vehicle (HEV) design. In this paper, a new coordination strategy is proposed to solve the issue of undesired torque jerks and large power ripples noticed respectively during drive mode commutations and power sources switching. The proposed coordinated switching strategy uses stair-based transition function to perform drive mode commutations and power source switching’s within defined transition periods fitting the transient dynamics of power sources and traction machines. The proposed technique is applied on a battery/ supercapacitor electric vehicle whose traction is ensured by two permanent magnet synchronous machines controlled using direct torque control and linked to HEV front and rear wheels. Simulation results highlight that the proposed coordinated switching strategy has a noteworthy positive impact on enhancing HEV transient performance as DC bus fluctuations were reduced to a narrow band of 6 V and transient torque ripples were almost suppressed.

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive.

This paper introduces a decentralized active fault tolerant control technique for actuator faults in a DC microgrid with load variations and source perturbations without requiring communication between distributed generators or load current measurement. The proposed control technique consists of two sections, each designed separately. One section aims to maintain the system’s stability and desired performance; the other section compensates for the actuator fault through fault reconstruction. To achieve this, a decentralized unknown input proportional-integral observer (UI-PIO) estimates the fault accuracy regardless of the load variations. The proposed linear quadratic regulator (LQR)-based UI-PIO strategy optimizes the trade-off between control performance and energy consumption, ensuring efficient operation under normal conditions and minimizing disruptions during fault states while robust to load variation. The stability and robustness of the closed-loop system under load variations are ensured through Lyapunov’s conditions, solved by the linear matrix inequality technique. The proposed decentralized LQR-UI-PIO circumvents the limitations of load variations, source perturbations, and actuator fault by allowing local controllers within the MG to independently compute control actions based on locally available information and a shared global objective. Finally, the hardware-in-the-loop setup is implemented to evaluate and validate the efficiency of the proposed decentralized fault-tolerant technique. The obtained results highlight the effectiveness and efficiency of the proposed approach in detecting and reconstructing faults, maintaining stability, and improving performance, which makes this approach of interest in other industrial applications.

Removing the gearbox from the single-motor configuration of an electric vehicle (EV) would improve motor-to-wheel efficiency by preventing mechanical losses, thus extending the autonomy of the EV. To this end, a permanent-magnet Vernier machine (PMVM) is designed to ensure such operation. This machine avoids the high volume and large pole-pair number of the armature winding since its operating principle resembles that of a synchronous machine with an integrated magnetic gear. Therefore, such a structure achieves low-speed and high-torque operation at standard supply frequencies. From the specification of an urban vehicle, the required specification for direct-drive operation is first determined. On this basis, an initial prototype of a Vernier Machine with permanent magnets in the rotor that can replace the traction part (motor + gearbox) is designed and sized. This first prototype uses radial contiguous surface-mounted magnets and its performance is then analyzed using finite element analysis (FEA), showing a relatively high torque ripple ratio. The rotor magnets are then arranged in a quasi-Halbach configuration and simulations are performed with different stator slot openings and different ratios of the tangential part of the magnet in order to quantify the effect of each of these two quantities in terms of average torque, torque ripples and harmonics of the back-electromotive force at no load. Since the design and optimization of this motor is finite element-assisted, a coupling process between FEA Flux software and Altair HyperStudy is implemented for optimization. This method has the advantages of high accuracy of the magnetic flux densities and electromagnetic torque estimates, and especially the torque ripples. The optimization process leads to a prototype with an average torque value that meets the specification, along with a torque ripple ratio below 5% and a high power factor, while keeping the same amount of magnet and copper.

Hybrid systems combining fuel cells with battery and supercapacitor storage offer a promising solution for efficient and flexible energy management. Fuel cells, which convert chemical energy into electrical energy, provide a clean and sustainable power source but face challenges like slow response times and limited power density. Integrating batteries and supercapacitors addresses these issues: batteries offer high energy density and stable power supply over longer durations, while supercapacitors deliver rapid bursts of energy and quick charge-discharge cycles. Effective power management in hybrid fuel cell systems is crucial for maximizing efficiency, performance, and lifespan while ensuring reliable energy supply. The hybrid system consists of a PEM fuel cell, batteries, and a supercapacitor (SC) and introduces a power management strategy to ensure the supply of the load.