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.

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 two intelligent torque distribution strategies based on particle swarm optimization (PSO) and fuzzy logic control (FLC) to provide convenient torque allocation that maximizes hybrid electric vehicle (HEV) propulsion power. PSO torque distribution strategy uses torque transfer ratio (TTR) as a fitness function to select the best torque candidates and differential arrangements that maximize HEV propulsion torque. A proposed FLC controller with adequate membership functions is designed to ensure convenient torque vectoring across vehicle wheels. A new coordinated switching strategy is proposed in this paper to address the undesired transient ripples occurring during drivetrain commutations and power source switchings. The proposed coordinated switching strategy controls the switching period duration through transition functions fitting the transient dynamics of power sources. In non-uniform surfaces, intelligent torque allocation strategies converted $84\sim 86$ % of the generated torque into propulsion torque whereas the equal torque distribution strategy yielded a torque transfer ratio of 50%. Thanks to the proposed coordinated switching strategy, DC bus voltage ripples were reduced to a narrow band of $\pm ~5\text{V}$ , transient power ripples were limited to a narrow band of 600 W and torque jerks were almost suppressed. Real-time simulation using the RT LAB platform confirms that the proposed coordinated switching strategy has reduced transient torque overshoot from 69% to almost zero and this is expected to improve HEV driving comfort.

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid battery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is particularly important because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system performance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, cost-effective energy storage option. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. This work establishes a battery-supercapacitor storage system (HBSS) by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that effectively controls the different power sources. The algorithm is designed to manage the charge and discharge cycles of the hybrid battery-supercapacitor energy storage system (HBSS), thereby guaranteeing that the state of charge (SOC) for both batteries and supercapacitors is maintained within the specified range. The proposed management algorithm is designed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. The proposed method ensures their longevity and maximizes their performance by maintaining the SOC of these energy storage components within the specified limits. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.