With 27% of the world's total greenhouse gas emissions, the transportation sector is clearly the most polluting. About 57% of this comes from fuel-powered cars. The transition toward sustainable and environmentally friendly practices is being accelerated by this alarming situation. An electric car's responsiveness, efficiency, strength, and price are all impacted by the electric machinery that make them up. The traction machines used in automobiles are described in detail in this book. An extensive taxonomy of electric machines outlining several electric motor types and their basic operating principles begins this study's comprehensive examination of electric vehicle traction machines. Next, several electric vehicle systems are compared using the most essential performance parameters, including as efficiency, torque density, reliability, materials needed, production complexity, and application compatibility. The results demonstrate that there is no universally optimal motor topology. On the contrary, performance metrics, reliance on rare-earth materials, manufacturability, and system integration limitations must all be carefully considered in order for the implementation to be practicable. Reason being, there are a number of machine technologies that excel in this area. Increasing power density, controlling heat, the precariousness of the rare-earth supply, and sustainability are some of the new challenges and constraints discussed in the paper that will impact the development of electric traction technology. Overall, the study paints a detailed picture of the state of the art in traction machine technology and how various models compare in terms of performance. Making the correct decision and planning for future technical progress will be aided by this.

Early and reliable diagnosis of inter-turn short-circuit (ITSC) faults is critical to maintaining the reliability, availability, and safe operation of doubly fed induction generators (DFIGs) used in wind energy conversion systems (WECSs). Incipient winding faults are particularly challenging to identify because their electrical signatures can be masked by the inherent spectral complexity of DFIG operation and variations in wind and operating conditions. This study proposes a hybrid Fast Fourier Transform-Adaptive Neuro-Fuzzy Inference System (FFT–ANFIS) diagnostic framework for the detection, localization, and severity assessment of ITSC faults in both stator and rotor windings. The proposed approach employs the FFT method to extract fault-sensitive harmonic components from stator-current signals, which are subsequently used as diagnostic features by an Adaptive Neuro-Fuzzy Inference System (ANFIS). By integrating spectral feature extraction with nonlinear neuro-fuzzy classification, the proposed framework provides an efficient and interpretable mechanism for distinguishing healthy and faulty operating conditions and assessing fault severity. The methodology is evaluated using MATLAB/Simulink simulations under healthy and multiple ITSC fault conditions with different fault locations and severity levels. The results demonstrate 100% classification accuracy for stator faults, rotor faults, and multiple short-circuit (MSC) fault conditions, together with near-zero prediction error in fault-severity estimation. These results confirm the high discriminative capability of the selected FFT-based spectral features and the effectiveness of ANFIS in establishing the nonlinear relationship between fault signatures and fault conditions. In addition, the proposed framework maintains low computational complexity and is therefore suitable for real-time condition-monitoring applications. Compared with existing diagnostic approaches, the proposed method provides a unified framework for multi-fault diagnosis while combining high diagnostic accuracy, computational efficiency, and interpretable decision-making. The proposed FFT–ANFIS framework consequently offers a practical approach for early fault detection and condition-based maintenance of DFIG-based wind turbines, with the potential to reduce unplanned downtime, maintenance requirements, and energy-production losses.

The integration of distributed generation (DG) and short circuit analysis affects the radial distribution power system in terms of voltage profile, line loss and short circuit levels. This paper investigates the impact of DG placement and sizing with security analysis in the IEEE 33-bus distribution system. Modified Whale Optimization Algorithm (MWOA) is proposed and implemented in MATLAB to solve the DG location and sizing problem and compared to Fireworks Algorithm (FWA), Genetic Algorithm (GA) and Modified Plant Growth Simulation Algorithm (MPGSA) in terms of effectiveness, precision, and results quality. The short circuit analysis is performed using an ETAP environment under different fault types.

This study investigates a Genetic Algorithm (GA)-based maximum power point tracking (MPPT) method for photovoltaic (PV) systems under varying irradiance and partial shading conditions. The proposed GA approach is compared with Perturb and Observe (P&O) and Artificial Neural Network (ANN) techniques using MATLAB/Simulink. Simulation results show that the GA-based MPPT achieves faster convergence (≈0.3 s), near-zero steady-state tracking error, and higher extracted power. Under normal conditions, GA delivers 2123 W compared to 1980 W for ANN and 1900 W for P&O. Under partial shading, GA successfully tracks the global MPP with 853 W, outperforming ANN (622 W) and P&O (748 W). The results confirm superior robustness and tracking efficiency of the GA-based MPPT.

Maintaining frequency stability in islanded microgrids (MGs) has become increasingly challenging due to the growing penetration of renewable energy sources, particularly photovoltaic systems, wind turbine generators (WTGs), and plug-in hybrid electric vehicles (PHEVs). The intermittent nature of renewable generation and continuous load variations introduces significant power imbalances, resulting in frequency deviations and degraded system stability. Although the classical Ziegler–Nichols (ZN) tuning method is attractive because of its simplicity and ease of implementation, it is generally limited to conventional proportional–integral–derivative (PID) controllers and is often inadequate for renewable-dominated MGs. To overcome these limitations, this paper proposes a modified ZN-based tuning strategy for a novel multistage PID (MPID) controller. Unlike the conventional ZN method, the proposed approach extends its applicability to the MPID structure by introducing an additional proportional gain (KPP), enabling the tuning of five controller parameters while preserving low computational complexity and practical implementation. The proposed controller is implemented and validated using a detailed MATLAB/Simulink model of an isolated MG comprising PV systems, WTG, diesel generators, and PHEVs. Its performance is comprehensively evaluated under multi-step load disturbances, renewable power fluctuations, combined disturbances, and different PHEV charging/discharging modes and battery state-of-charge levels. Furthermore, the proposed controller is benchmarked against conventional ZN-PID, ZN-FOPID, and both PID- and MPID-based controllers tuned using Particle Swarm Optimization, Cuckoo Search Algorithm, Moth–Flame Optimization, and Grasshopper Optimization Algorithm. Simulation results demonstrate that the proposed ZN-MPID controller achieves the best overall dynamic performance, with a settling time of 4.109 s, zero overshoot, a maximum frequency undershoot of 1.801 × 10−4 Hz, and the lowest error indices (ISE = 3.073 × 10−6, ITSE = 0.697 × 10−6, and ITAE = 3.40 × 10−4). Compared with the investigated metaheuristic-based PID controllers, the proposed controller reduces the settling time by up to 86.1% and the error indices by up to 95.5%. It also consistently outperforms all investigated MPID tuning methods, confirming the effectiveness of the proposed modified ZN tuning strategy. Overall, the proposed methodology provides an efficient, low-complexity, and practical solution for frequency regulation in renewable-dominated isolated MGs.

This work presents a techno-economical, as well as environmental assessment, of a grid-connected microgrid (MG) power system incorporating photovoltaic (PV), wind energy, battery energy storage, as well as a backup power plant. The case study is applied to Sheikh Bouamama Airport located in Mecheria, Algeria. The hybrid system is optimally modeled using HOMER Pro by optimizing both levelized cost of energy (LCOE) and net present costs (NPCs), while addressing emission reduction and economic power supply. Two grid-connected configurations are considered: a conventional power system without renewable energy integration, as well as a renewable energy-based MG power system, involving PV, wind energy, and battery energy. The load-following as well as cycle charge dispatch strategies of HOMER Pro were adopted for optimizing power supply analysis in the studied MG. The results demonstrate that renewable energy integration significantly enhances system performance, achieving reductions of 52.08% in NPC, 70.07% in LCOE, and more than 90% in annual operating costs. In addition, the renewable energy fraction reaches approximately 89.4%, while diesel fuel consumption and CO₂ emissions are reduced by about 79.7% and 83.0%, respectively. The findings confirm that grid-connected hybrid MGs represent a cost-effective and eco-friendly solution for powering regional airports in semi-arid regions.

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency, compact structure, and excellent dynamic performance. However, achieving accurate speed control with high robustness under load disturbances and parameter uncertainties remains a significant challenge. Conventional proportional-integral (PI) controllers often suffer from overshoot, slow dynamic response, and sensitivity to nonlinear operating conditions. To address these limitations, this paper proposes an intelligent control strategy that combines third-order sliding mode control (TOSMC) with the Golden Jackal Optimization (GJO) algorithm for optimal PMSM speed regulation. The proposed TOSMC-GJO approach aims to enhance the operational performance, robustness, and reliability of PMSM drives. The control structure consists of an optimized outer-loop speed controller and an inner-loop predictive current controller to improve current quality and eliminate the need for conventional PI tuning. The controller parameters are optimized using a fitness function designed to minimize tracking error, overshoot, settling time, torque ripples, and total harmonic distortion (THD). Simulation results under variable speed and load torque conditions demonstrate that the proposed TOSMC-GJO controller achieves superior performance compared with PI control and TOSMC optimized using Grey Wolf Optimization (GWO). The proposed strategy eliminates speed overshoot and reduces the response time to 0.0052 s, compared with 0.0056 s for TOSMC-GWO and 0.011 s for PI control. In addition, the THD of stator currents is reduced to 6.12%, improving current quality and reducing harmonic distortion. The proposed controller also provides smoother torque response, better disturbance rejection capability, and improved waveform symmetry. These results confirm that integrating high-order nonlinear control with metaheuristic optimization significantly improves the dynamic performance, operational reliability, and robustness of PMSM drive systems under demanding operating conditions.

This paper introduces a novel hybrid PSO–FPA metaheuristic algorithm that integrates the global exploration capability of the Flower Pollination Algorithm (FPA) with the adaptive convergence and dynamic search behavior of Particle Swarm Optimization (PSO) for the efficient synthesis of Concentric Circular Antenna Arrays (CCAAs). By embedding PSO’s inertia-weighted velocity update and acceleration coefficients into FPA’s global and local pollination phases, the proposed approach establishes a self-adaptive optimization framework capable of achieving an effective trade-off between global exploration and local exploitation. The algorithm is applied to the simultaneous optimization of excitation amplitudes and ring radii under four design configurations, considering both with and without central element scenarios. The optimization objective focuses on minimizing Side Lobe Levels (SLL) while maintaining high directivity and narrow Half-Power Beamwidth (HPBW). Comprehensive numerical simulations demonstrate that the proposed hybrid PSO–FPA algorithm outperforms conventional metaheuristics—including FPA, PSO, Artificial Bee Colony (ABC), and Whale Optimization Algorithm (WOA)—in terms of sidelobe suppression, convergence speed, and pattern symmetry. The hybrid method achieves a minimum SLL of − 45.01 dB, representing an improvement of approximately 38–42% over traditional techniques, and enhances beam symmetry and directivity by 24–28%, achieving up to 13.14 dB of main-lobe gain with minimal beamwidth degradation. Moreover, the joint optimization of amplitudes and ring radii yields a balanced radiation performance, characterized by focused beams with sidelobes below − 45 dB and computation times under 12 s per design. The results confirm that the proposed PSO–FPA metaheuristic delivers superior sidelobe suppression, enhanced beam control, and rapid convergence, making it a robust and scalable optimization tool for next-generation antenna synthesis in radar, wireless communication, and smart sensing systems requiring precise directional control and interference mitigation.

This paper presents a multi-objective optimization approach based on a modified whale optimization algorithm (MWOA) to determine the optimal placement and sizing of distributed generators (DGs) and distribution static synchronous compensator (D-STATCOM) devices in radial distribution networks. MWOA enhances the classical WOA with adaptive control parameters and improved position update rules that guarantee fast convergence, effective global exploration, and precise local solutions refinement. The proposed framework performs DG sizing, D-STATCOM allocation, and network reconfiguration simultaneously to reduce power losses, improve voltage stability, and maintain protection reliability. In addition, Electrical Transient Analyzer Program (ETAP)-based short-circuit analysis is conducted to investigate fault levels and post-fault recovery after the integration of DG and D-STATCOM. Simulation studies were performed in MATLAB for the Institute of Electrical and Electronics Engineers (IEEE) 33-bus and the IEEE 69-bus test systems. The results show that the MWOA reduced active power losses by up to 89.87% and reactive losses by 90.75% in the IEEE 33-bus system, and by 95.27% and 92.30%, respectively, in the IEEE 69-bus system, while improving the voltage profile to above 0.98 p.u. ETAP analyses confirmed that while DG integration increases fault currents by about 50%, the coordinated operation with D-STATCOM improves post-fault voltage recovery by 15%-20%. Compared with the existing methods in the literature, the proposed MWOA outperforms them in loss reduction, voltage regulation, and reliability, which confirm it as a robust tool for renewable integrated distribution systems.

Ensuring the optimal functioning of Distribution Networks (DNs) has become a critical priority in modern power systems due to the rapid integration of Renewable Energy Sources (RESs) and the complex operational challenges they introduce, such as intermittent generation, dynamic load fluctuations, and the need for reliable protection coordination. While the incorporation of Distributed Generators (DGs) significantly enhances system efficiency and voltage stability, it simultaneously complicates loss minimization and protection design. This paper presents a novel integrated optimization-protection framework that simultaneously addresses optimal DG placement and sizing as well as coordinated protection design under variable operating conditions. This paper presents a novel, integrated optimization-protection framework that simultaneously addresses optimal optimizes DG placement and sizing as well as and designs coordinated protection design under variable operating conditions. Unlike existing works that focus solely on loss minimization or voltage profile (VP) improvement, this study uniquely combines optimization, load flow, short-circuit (SC) analysis, and protection coordination within a single framework. The methodology is validated on three DNs (IEEE 33-bus, IEEE 69-bus, and a newly developed 19bus system) that closely represent real operational conditions. The results confirm the superiority of MALO, achieving remarkable reductions in Active Power Loss (APL) by 92.25 %, 94.05 %, and 73.5 %, and in Reactive Power Loss (RPL) by 92.43 %, 91.94 %, and 50.65 %, respectively, along with improved VPs. Optimization and load flow simulations were performed using MATLAB, while Electrical Transient Analyzer Program (ETAP) was utilized for SC current analysis and protection coordination. A customized protection plan was designed for the 19-bus system, ensuring high reliability, time selectivity, and effective coordination before and after DG integration. The key novelty of this work lies in its comprehensive integration of optimization and protection planning, representing a practical and original contribution toward the development of self-adaptive, intelligent protection systems in smart distribution grids. Future research will extend this framework using hybrid intelligent algorithms and deep learning techniques in collaboration with SONELGAZ, Algeria's national utility, to further enhance the reliability of real-world DNs.

In this paper, an optimization analysis of concentric circular antenna arrays (CCAAs), taking into consideration the inter-element coupling effects and non-uniform element spacing. The Dynamic Harris Hawks Optimization (DHHO) algorithm simultaneously optimizes excitation amplitudes and inter-element spacings to minimise sidelobe levels while preserving directivity and beamwidth. The simulation is carried out using MATLAB, based on a mutual impedance matrix model that accounts for inter-element coupling, providing a rigorous electromagnetic model-based assessment of the array's radiation performance for algorithm benchmarking. The DHHO introduces a dynamic exploration–exploitation strategy. Unlike standard Harris Hawks Optimization (HHO), which uses linear energy decay and may converge prematurely, DHHO employs non-linear adaptive energy control, enabling longer global exploration in early iterations and smoother local exploitation in later stages. This improves population diversity, convergence stability, and robustness in complex optimization landscapes. Four optimization scenarios are considered: amplitude-only optimization, joint amplitude and spacing optimization, amplitude optimization with mutual coupling, and full optimization including both spacing and coupling effects. In the most demanding scenario, DHHO achieves a sidelobe level (SLL) of −48.94dB, compared to −41.01dB for standard HHO, −31.99dB for the whale optimization algorithm (WOA), −29.79dB for the Flower Pollination Algorithm (FPA), and −23.63dB for Particle Swarm Optimization (PSO), corresponding to improvements of approximately 19% over HHO, 35% over WOA, 40% over FPA, and more than 50% over PSO. The directivity is also stable and remains close to 14dB, and its half-power beamwidth differs by less than 15%, ensuring that the beam quality remains unchanged. Statistical boxplot analysis shows that DHHO reduces SLL variability by 40–60% compared to standard HHO, demonstrating higher consistency and repeatability. These results highlight that the dynamic adjustment of exploration and exploitation in DHHO significantly improves optimization efficiency. The proposed algorithm presents an effective approach to generating realistic CCAA, which is well-suited for current practices in radar, satellite, and next-generation wireless communication technologies.

This study investigates the problems associated with the nonlinear power-voltage characteristics of photovoltaic (PV) systems, especially under partial shading conditions (PSC), which reduce energy efficiency and tracking accuracy. To overcome these limitations, two improved maximum power point tracking (MPPT) controllers based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques are proposed. The controllers are designed with a suggested architecture that uses the power-voltage derivative ([Formula: see text]) and the voltage time derivative ([Formula: see text]) as input features, enabling predictive, non-iterative control. This approach eliminates the steady-state oscillations inherent in conventional perturb-and-observe (P&O) algorithms and achieves superior dynamic response under rapidly changing environmental conditions. Simulation results demonstrate significant improvements compared with the traditional P&O method. The proposed ANN and ANFIS controllers achieved average tracking efficiencies ([Formula: see text]) of 99.4% and 99.75%, respectively, with a response time reduction of about 55% and steady-state oscillation suppression exceeding 70%. The ANFIS controller exhibited higher stability, reducing the duty-cycle fluctuation index ([Formula: see text]) by approximately 20% compared with the ANN controller, resulting in smoother and more reliable power extraction. A comparative evaluation with recently published metaheuristic and hybrid AI-based MPPT approaches confirmed that the proposed ANFIS model achieves equal or better performance while maintaining very low computational complexity. The average execution time per control step remained below 0.2 ms, confirming the suitability of both controllers for real-time deployment on low-cost digital signal processors (DSPs). These findings demonstrate that the proposed intelligent MPPT framework provides a fast, accurate, and computationally efficient solution for improving the reliability and energy yield of PV systems operating under dynamic and partially shaded conditions.

This paper presents a techno-economic and environmental optimization of a grid-connected hybrid microgrid (MG) designed to meet the combined electrical and thermal loads of Ain Ouarka and the neighboring town of Assela in Naama, Algeria, with surplus electricity exported to the Sonelgaz/SKTM national grid. The proposed MG integrates photovoltaic (PV) power generation, wind turbines, a geothermal cogeneration heat and power (CHP) unit, a diesel generator (DG), a boiler, lithium-ion battery storage, and a bidirectional converter. To ensure realistic operation, hourly real electrical and thermal load profiles were simulated over a full year. While HOMER Pro was used for component sizing and feasibility analysis, its standard dispatch strategies, load following (LF) and cycle charging (CC), rely on a rule-based search space that cannot simultaneously optimize electrical, thermal, and storage decisions in multi-energy systems. LF minimizes fuel use but cannot coordinate CHP thermal flows or battery SOC trajectories, whereas the CC dispatch strategy forces generators to operate at rated power even when this increases fuel consumption and operational costs, often trapping the optimization in locally optimal solutions. To address these limitations, a MATLAB Link-based Mixed-Integer Linear Programming (ML-MILP) controller was incorporated to perform single-objective hourly dispatch optimization aimed at minimizing instantaneous operating cost. HOMER then evaluates these optimized dispatch schedules and outcomes through long-term multi-objective optimization, minimizing the net present cost (NPC), levelized cost of energy (LCOE), and annual operating cost while maximizing renewable fraction (RF) and reducing emissions. Three system scenarios were examined. Scenario A, based solely on DG, grid electricity, and a boiler, resulted in an NPC of 17.6 M, an LCOE of 0.373 /kWh, and an operating cost of 942,420 /year. Scenario B, a renewable-fossil hybrid without CHP, reduced costs to an NPC of 12.0 M and an LCOE of 0.254 /kWh. The hybrid with complete integration, Scenario C, optimized with ML-MILP and involving geothermal-based CHP and thermal coupling, performed best with an NPC of 4.19 M, LCOE of 0.0854 /kWh, and a yearly operating cost of 408,050 , which translated to a saving of 76.2 %, 72.9 %, and 56.7 % over Scenario A, with a further 27.5 % reduction in emissions of CO2 and grid dependency reduced from 100 % to 28.6 %. Sensitivity analysis performed for fuel costs, total investment costs, and discount rate further validates the proposed approach applied to the optimal MG configuration. These results confirm that the incorporation of geothermal-based MGs with solar

The inner current control loop (CCL) is critical for ensuring dynamic stability and accurate power sharing in droop-controlled voltage source inverters (VSIs) operating in islanded microgrids (MGs). Conventional PI controllers lack robustness and degrade under nonlinear loads. To overcome this limitation, this paper proposes a saturated exponential super-twisting sliding mode controller (SEST-SMC) with gains optimally tuned using the Savannah Bengal Tiger Optimization (SBTO) algorithm. The proposed control law combines exponential and saturation functions to mitigate chattering, guarantee finite time convergence, and bound control effort. Simulation and hardware-in-the-loop (HIL) results confirm the effectiveness of the SBTO-tuned SEST-SMC, current tracking error is reduced by 73

As Algeria advances toward green hydrogen deployment under the SKTM/Sonelgaz 2030 strategy, this study proposes the design and techno-economic-environmental optimization of a hydrogen-based microgrid (MG) to supply Cheikh Bouamama Airport in Mecheria, Naama. The proposed system integrates a photovoltaic (PV) generator, wind turbines (WTs), a battery energy storage system (BESS), an electrolyzer, a hydrogen storage tank, a fuel cell, and bidirectional interaction with the national grid, where surplus renewable electricity is converted into green hydrogen or exported to improve economic returns. A MATLAB-linked Mixed-Integer Linear Programming (ML-MILP) framework is used as an advanced dispatch strategy, which performs globally optimal dispatch and thus performs better than conventional Load Following (LF) and Cycle Charging (CC) methods used in other scenarios. Four scenarios are analyzed, and the ML-MILP-optimized Green Hydrogen (H2) MG performs better than the other scenarios. Scenario 4 achieves 30%, 26%, and 3.5% less in terms of Net Present Cost (NPC) than Scenarios 1, 2, and 3, respectively, with a reduced NPC of 1.10 M$. Similarly, the Levelized Cost of Electricity (LCOE) is reduced to 0.128 $/kWh, which is 68%, 66%, and 23% less than Scenarios 1, 2, and 3, respectively. At the same time, the Levelized Cost of Hydrogen (LCOH) is 4.15 $/kg, thus ensuring the economic viability of on-site production of green hydrogen. Similarly, the annual operating cost is reduced to 10,534 $/yr, which is greater than 90% less than the fossil-based scenarios, despite having 964,400 $ as the initial investment cost. The renewable fraction (RF) is increased to 90.8%, and CO2 emission is reduced from 192,848 kg/yr, 212102 kg/yr, and 61551 kg/yr in Scenarios 1, 2, and 3 to 38681 kg/yr in Scenario 4, resulting in a reduction of up to 82%. Comparative analysis of the results with recent hydrogen-based MG studies carried out in different nations of the world also confirms that the ML-MILP optimized configuration is competitive and superior in terms of cost reduction, renewable energy share, and emission reduction compared to existing approaches. Sensitivity analysis of multiple parameters under different techno-economic and environmental conditions also validates the robustness and flexibility of the proposed hydrogen-based MG configuration for sustainable aviation energy supply in Algeria.

In this paper an adaptive droop control strategy is proposed for voltage source converter based multi-terminal direct current (VSC-MTDC) systems to achieve a higher power-sharing accuracy, DC voltage regulation, and operating robustness during converter failures. The scheme integrates the robustness of voltage margin control with the distributed balancing capability of conventional droop regulation in a way that converters operating under constant power mode initially can switch automatically to droop mode at any time DC voltage differences exceed pre-set operating thresholds. Event-driven adaptability enhances synchronized inter-terminal response, enables equitable power readjustment, and mitigates voltage instability under heavy transient disturbances. A ±400 kV four-terminal VSC-MTDC system was planned and rigorously simulated in PSCAD/EMTDC for comparing the proposed method with conventional fixed-slope droop control. Quantitative results confirm that the proposed method realizes 45–60% reduction of peak DC voltage excursions, 40–55% reduction of active power overshoot, and 35–50% improvement in voltage settling time. Additionally, the passive converters in conventional schemes deliver up to +65 MW of extra support, thereby preventing the remaining terminals from being overloaded and maintaining safe operating margins. The dynamic coordination with advanced capabilities allows solid post-fault operation for single as well as multi-converter outages without protection-led shutdowns and improves the entire system's resilience. The demonstrated voltage stability, fault tolerance, and converter utilization enhancements confirm the adaptive droop control as a viable approach for the next-generation resilient, large-scale HVDC microgrids with high-penetration renewable energy integration.

Model predictive control (MPC) has emerged as one of the most effective control strategies for autonomous vehicle path tracking owing to its capability to explicitly handle dynamic constraints and optimize future control actions. However, its tracking performance strongly depends on the appropriate tuning of prediction, control, and weighting parameters, which remains a challenging and computationally demanding task under varying driving conditions. This paper proposes an Adaptive Curvature-Aware MPC (CAMPC) framework optimized through a hybrid Bayesian Optimization–Tree-structured Parzen Estimator (BO–TPE) approach to automatically identify optimal MPC parameters while accounting for upcoming road curvature. The proposed controller incorporates future curvature information to adapt the vehicle speed profile and steering behavior, thereby improving tracking accuracy and control smoothness in complex road geometries. The framework is evaluated in the CARLA autonomous driving simulator under three challenging driving scenarios, including a roundabout, an urban environment, and a highly curved road. Experimental results demonstrate that the proposed CAMPC consistently outperforms a conventional PID controller, achieving improvements of 47%, 58%, and 85% in trajectory-tracking performance across average and maximum lateral and angular errors while reducing steering, braking, and acceleration efforts by more than 80%. Furthermore, the controller satisfies real-time execution requirements with an average computation time of 31 ms and a 95th-percentile latency of 35 ms, confirming its suitability for practical autonomous driving applications. These results demonstrate that integrating curvature-aware prediction with adaptive BO–TPE parameter optimization significantly enhances the robustness, accuracy, computational efficiency, and real-time capability of MPC for autonomous vehicle path tracking in challenging driving environments.

In this paper, a new multi-stage proportional-integral-derivative (MPID) controller based on the coefficient diagram method (CDM) is proposed to solve the frequency deviation problem in isolated microgrids (MGs) with renewable energy sources (RESs) and plug-in electric vehicle batteries (PHEVs). The MPID controller is implemented with a two-loop proportional-integral (PI) controller and proportional-integral-derivative (PID) structure and uses the CDM method for fine-tuning parameters, improving the dynamic response, stability, and adaptability of the system.Various simulations and comparative evaluations were carried out under different operating conditions, such as load variation and RES disturbances and intermittence, to test the effectiveness and robustness of the proposed method.In contrast to conventional PID tuning approaches, the CDM method dynamically optimizes parameter adaptation, improves system stability, and significantly reduces overshoot and settling time. The results showed that the MPID-CDM controller achieved considerable optimization in terms of performance metrics. These included a 68.40% reduction in integral time absolute error, a 20.7% improvement in settling time, a 58% reduction in overshoot compared to the controller using the standard PID tuned by particle swarm optimization, and a 47% improvement in overshoot compared to the standard PID tuned by cuckoo search algorithms. In addition, integrating PHEVs into the control technique improved frequency regulation by 35% in the case of high load variations, indicating their importance in stabilizing the MG energy management system and improving system efficiency under dynamic and uncertain conditions.

Accurate and robust sensorless speed control of Permanent Magnet Synchronous Motor (PMSM) drives is essential for high-performance Electric Vehicle (EV) applications operating under rapid speed variations, load disturbances, and repeated start–stop conditions. This paper proposes a robust sensorless PMSM control framework based on Integral Sliding Mode Control (ISMC) combined with Extended Kalman Filter (EKF) and Model Reference Adaptive System (MRAS) observers. Unlike previous studies focusing separately on control or observer design, this work provides an integrated robustness and estimation accuracy evaluation under realistic EV driving conditions. The proposed ISMC scheme is designed to improve disturbance rejection capability and eliminate the conventional reaching phase of sliding mode control. EKF and MRAS observers are employed for sensorless rotor speed estimation, and their performances are comparatively analyzed under multiple EV operating scenarios, including step speed changes, dynamic driving profiles, and repeated start–stop operation. In addition, a weighted performance index is introduced to quantitatively compare the considered control configurations. Simulation results demonstrate that the proposed ISMC–EKF configuration achieves superior transient and steady-state performance compared with conventional PID-based control. The rise time is reduced from approximately 3.5 ms to 2.7 ms, while the settling time decreases from nearly 10 ms to about 5–6 ms. Furthermore, overshoot is reduced from 5% to 0.3%, and torque ripple is significantly minimized under dynamic EV conditions. Robustness analysis under parameter variations additionally confirms the disturbance rejection capability and stability of the proposed framework. The obtained results demonstrate that the proposed ISMC–EKF framework provides an efficient and reliable solution for high-performance sensorless PMSM drives in next-generation EV propulsion systems.

This study presents a new improved primal–dual interior point method associated with a generation scaling factor (PDIPM–GSF) for congestion-aware optimal power flow (OPF) in power systems with renewable integration in a deregulated electricity market. The proposed framework combines the robustness of the primal–dual interior point method with an adaptive generation scaling strategy that adjusts generator outputs according to transmission line flow sensitivities, enabling early congestion mitigation, improved power flow feasibility, and accurate determination of locational marginal prices (LMPs). The proposed approach is evaluated on the IEEE 30-bus test system and a real 114-bus Algerian power network. The results demonstrate that the proposed framework can reduce generation costs and improve social profit under both single-sided and double-sided market operation through enhanced coordination between generation, demand, and congestion management. The impact of integrating wind energy is analyzed under different levels of wind energy penetration in the first IEEE 30-bus test system, showing that the integration of wind energy can not only improve social welfare and save generation costs but also lower transmission losses. Additionally, the results show that the placement of wind farms can improve the performance of the proposed PDIPM–GSF framework in terms of congestion management and reduction in LMPs at important nodes. For the 114-bus Algerian transmission network, renewable energy integration further demonstrates the capability of the proposed framework to reduce transmission losses and generation costs while improving overall market performance. These results demonstrate that the proposed PDIPM–GSF framework provides an effective smart optimization approach for renewable-integrated power systems by improving congestion management, facilitating large-scale WE integration, enhancing market efficiency, and ensuring reliable LMP computation. The proposed method, therefore, represents a practical and efficient solution for supporting future renewable energy transition and competitive electricity market operation in Algeria and other renewable-rich power systems.