Electric vehicles demand efficient and robust motor control to maximize range and performance. This paper presents an innovative adaptive fractional-order sliding mode (FO-SM) control approach tailored for Direct Torque Control with Space Vector Modulation (DTC-SVM) applied to induction motor drives. This approach tackles the challenges of parameter variations inherent in real-world applications, such as temperature changes and load fluctuations. By leveraging the inherent robustness of FO-SM and the fast dynamic response of DTC-SVM, our proposed control strategy achieves superior performance, significantly reduced torque ripple, and improved efficiency. The adaptive nature of the control system allows for real-time adjustments based on system conditions, ensuring reliable operation even in the presence of uncertainties. This research presents a significant advancement in electric vehicle propulsion systems, offering a powerful and adaptable control solution for induction motor drives. Our findings demonstrate the potential of this innovative approach to enhance the robustness and performance of electric vehicles, paving the way for a more sustainable and efficient future of transportation. In fact, the paper proposes using an adaptive approach to control the electric vehicle’s speed based on the fractional calculus of sliding mode control. The adaptive algorithm converges to the actual values of all system parameters. Moreover, the obtained performance results are reached without precise system modeling.

The present paper proposes the use of fractional derivatives in the definition of sliding function, giving a new mode control applied to induction motor drives in electric vehicle (EV) applications. The proposed Fractional-Order Sliding Mode Direct Torque Control-Space Vector Modulation (FOSM-DTC-SVM) strategy aims to address the limitations of conventional control techniques and mitigate torque and flux ripples in induction motor systems. The paper first introduces the motivation for using fractional-order control methods to handle the nonlinear and fractional characteristics inherent in induction motor systems. The core describes the proposed FOSM-DTC-SVM control strategy, which leverages a fractional sliding function and the associated Lyapunov stability analysis. The efficiency of the proposed strategy is validated via three scenarios. (i) The first scenario, where the acceleration of the desired speed is defined by pulses, leading to Dirac impulses in its second derivative, demonstrates the advantage of the proposed control approach in tracking the desired speed while minimizing flux ripples and generating pulses in the rotor pulsation. (ii) The second scenario demonstrates the effectiveness of filtering the desired speed to eliminate Dirac impulses, resulting in smoother rotor pulsation variations and a slightly slower speed response while maintaining similar flux ripples and stator current characteristics. (iii) The third scenario consists of eliminating the fractional derivatives of the pulses existing in the expression of the control, leading to the elimination of Dirac impulses. These results demonstrate the potential of the FOSM-DTC-SVM to revolutionize the performance and efficiency of EVs. By incorporating fractional control in the control scheme for PV-powered EVs, the paper showcases a promising avenue for sustainable transportation.

The primary disadvantage of solar photovoltaic systems, particularly in partial shadowing conditions (PSC), is their low efficiency. A power–voltage curve with a homogenous distribution of solar irradiation often has a single maximum power point (MPP). Without a doubt, it can be extracted using any conventional tracker—for instance, perturb and observe. On the other hand, under PSC, the situation is entirely different since, depending on the number of distinct solar irradiation levels, the power–voltage curve has numerous MPPs (i.e., multiple local points and one global point). Conventional MPPTs can only extract the first point since they are unable to distinguish between local and global MPP. Thus, to track the global MPP, an optimized MPPT based on optimization algorithms is needed. The majority of global MPPT techniques seen in the literature call for sensors for voltage and current in addition to, occasionally, temperature and/or solar irradiance, which raises the cost of the system. Therefore, a single-sensor global MPPT based on the recent red-tailed hawk (RTH) algorithm for a PV system interconnected with a DC link operating under PSC is presented. Reducing the number of sensors leads to a decrease in the cost of a controller. To prove the superiority of the RTH, the results are compared with several metaheuristic algorithms. Three shading scenarios are considered, with the idea of changing the shading scenario to change the location of the global MPP to measure the consistency of the algorithms. The results verified the effectiveness of the suggested global MPPT based on the RTH in precisely capturing the global MPP compared with other methods. As an example, for the first shading situation, the mean PV power values varied between 6835.63 W and 5925.58 W. The RTH reaches the highest PV power of 6835.63 W flowing through particle swarm optimization (6808.64 W), whereas greylag goose optimizer achieved the smallest PV power production of 5925.58 W.

This paper presents a comparative study between two permanent magnet motor structures, one with trapezoidal waveforms and the other with sinusoidal waveforms, from the perspective of production cost, dynamic characteristic performance, and energy economy. The novel element of the research is also in using an electromagnetic converter to reduce the vehicle’s overall energy consumption and to push the multiple disadvantages of using the insolated bipolar transistors. This study is based on the sizing of the electromagnetic converter and the two motor structures using the method of combined analytical finite elements. The program for sizing is coupled with two control laws relating to the motor types in order to choose the top performers according to good dynamic characteristics and energy saving. The simulation results show, on the one hand, the equivalence of these structures from a dynamic characteristic point of view and, on the other hand, that the trapezoidal waveform motor structure is more economical. The design of an excitation system optimizing the consumed energy equipped by a hybrid excitation motor is also developed.

Power-generating ships (PGSs)are considered some of the largest mobile energy resources. A novel model is proposed in this work to evaluate the integration of PGSs into power grid operations. The proposed model optimally coordinates the ships to enhance grid objectives, providing optimal variables for generation resource scheduling and routing of the ships. Two case studies were used to simulate the system and validate the effectiveness of the proposed model. The proposed model significantly contributes to the field of applied mathematical modeling by developing complex algorithms for scheduling energy generation and addressing the logistical challenges of routing mobile power sources. This dual aspect emphasizes the model’s robustness in handling multidimensional optimization problems inherent in integrating mobile energy resources with static grid systems. Integrating PGSs into power grid operations represents a practical implementation of complex engineering solutions designed to enhance the flexibility and reliability of energy networks. The model not only improves the operational efficiency of the grid but also contributes to the resilience of the energy infrastructure by providing a mobile and adaptable energy resource. This approach exemplifies the potential for innovative engineering solutions to address contemporary challenges in energy distribution, ultimately leading to more sustainable and resilient power systems.

Hybrid renewable energy systems (HRESs) require advanced control strategies to ensure grid stability and reliable integration under variable operating conditions. Although Conventional Synergetic Control (SC) is an effective nonlinear controller, it often lacks robustness and adaptability. This limitation arises from fixed, manually tuned parameters, limited dynamic flexibility, and suboptimal performance under parametric uncertainties and external disturbances. This paper proposes a Fractional-Order Terminal Synergetic Controller (FOTSC) for both the rotor-side and grid-side converters of a doubly-fed induction generator (DFIG) in a hybrid wind-photovoltaic (PV) system, where the PV system acts as a supplementary source. The controller parameters are optimally tuned using an Enzyme Activation Optimizer (EAO), which minimizes a formulated objective function based on the Integral of Time multiplied by the Absolute Error (ITAE) criterion to systematically balance the transient response and steady-state accuracy. The FOTSC introduces fractional-order dynamics and terminal attractors to ensure finite-time convergence, whereas EAO optimization enhances the robustness and consistency across operating conditions. MATLAB/Simulink simulation results highlight that the optimized FOTSC (OFOTSC) outperforms both the manually-tuned FOTSC and Conventional SC, reducing the overshoot in active power, reactive power, rotor currents, DC-link voltage, and grid currents by up to 58%, and shortening the settling times by up to 43%. In terms of power quality, the OFOTSC achieves a Total Harmonic Distortion (THD) of 1.37% for the grid-injected currents, which is well below the IEEE 519-2014 standard limit of 5%. This represents a significant reduction of 84% compared to the Conventional SC (THD = 8.75%) and 67% compared to the manually-tuned FOTSC (THD = 4.14%). These findings confirm that the EAO-tuned FOTSC significantly enhances the stability, power quality, and robustness of DFIG-based HRES.