The Maximum Power Point Tracking controller (MPPT) is a key element in Photovoltaic systems (PV) since it allows maintaining the PV operating point at its maximum under different temperatures and sunlight irradiations. Metaheuristic algorithms such as the ant colony optimization (ACO) are adopted and have shown their superiority to many other techniques. The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed MPPT schemes for PV power generation systems. Which executed by manipulating direct duty ratio of the boost converter. P&O method miserably fails to recognize various MPPT controllers. This paper proposes ACO technique to solve real-life problems.

The objective of this study was to develop a Fuel Cell Hybrid Electric Vehicle (FCHEV) powertrain with the aim of enhancing battery usage autonomy. The vehicle, which participated in the Eco-Marathon competition as a prototype, incorporates batteries, a Direct Current (DC) electric motor, and a Proton Exchange Membrane (PEM) fuel cell. The design permits the operation of the fuel cell to be conducted in a more efficacious and fuel-efficient manner. The study employs the MATLAB-Advisor software to construct powertrain models that are then validated in laboratory settings. These models are subsequently compared with the performance of the actual FCHEV prototype and adapted for use in automotive applications. The FCHEV power model calculates instantaneous energy consumption using input variables, such as vehicle speed, acceleration, and road gradient. Furthermore, Real Cycle drive was carried out to improve the trade-off between energy consumption, fuel cells, battery State of Charge (SOC) dynamics, and battery power smoothness, while ensuring that all essential limitations were met. The addition of a fuel cell to an electric car model enhances its range by 250%, significantly improving its adoption and usage.

The finite element model (FEM) for induction motors (IM) was developed and validated through experimental testing. The validated FEM provides a reliable basis for further optimization of the electric machine. A strong sliding mode technique, in conjunction with field-oriented control (FOC), is proposed for speed control of the IM. The sliding mode controller ensures steady functioning in the face of ambiguities and disruptions, while FOC enables precise control of the motor’s magnetic field. This combination enhances both the efficiency and accuracy of speed control in IM, making it a valuable tool for industrial applications. The proposed sliding mode control (SMC) was fine-tuned using the advantages produced by the ant colony optimization algorithm. This approach aids in resolving issues and delivers optimal speed and field responses. Simulation and experimental results demonstrate the effectiveness of the proposed approach. The optimized induction motor achieved a 28% reduction in rotor Joule losses, resulting in improved energy efficiency. Additionally, using Ant Colony Optimization to adjust the SMC parameters led to a 99.74% reduction in speed tracking error and a 99.59% reduction in flux error compared to traditional manual tuning. These substantial improvements confirm the superiority of the proposed method for high-performance and energy-efficient electric vehicle applications.

This study presents the Multi-Method Convergence Protocol (MMCP), a decision-making framework designed to overcome the mono-objective limitations of HOMER Pro (Hybrid Optimization Model for Electric Renewables) and the instability commonly observed among traditional MCDM approaches. Applied to a hybrid PV–wind–grid Smart Grid (Intelligent Electrical Power Grid) in the Provence-Alpes-Côte d’Azur region (France), the protocol transforms techno-economic simulation outputs into robust and explainable multi-criteria decisions. MMCP integrates five sequential stages—normalization, AHP-based (Analytic Hierarchy Process) weighting, multi-method ranking (TOPSIS, PROMETHEE II, ELECTRE II (Elimination and Choice Expressing Reality II), and VIKOR), Borda–Copeland (Borda Count Ranking Method–Copeland Pairwise Aggregation Method) co-aggregation, and statistical validation—using Kendall’s τ_b (Kendall’s Rank Correlation Coefficient) and Spearman’s ρ (Spearman’s Rank Correlation Coefficient). Results reveal strong convergence between compensatory and non-compensatory models (τ_b ≥ 0.75; ρ ≥ 0.90), confirming the internal coherence and structural stability of the rankings. Scenario 17 emerges as the optimal configuration, combining low LCOE (Levelized Cost of Energy) with reduced emissions and balanced renewable penetration. The near-linear alignment between aggregation methods validates the protocol’s reliability and methodological transparency. Overall, MMCP provides a scalable and traceable foundation for sustainable Smart Grid planning and evidence-based energy governance.