World Journal of Engineering Research and Technology

Adel Elgammal*

ABSTRACT

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for modern power systems. While EVs offer a sustainable alternative to internal combustion engines, their integration into the electrical grid—particularly during peak demand hours—can lead to increased stress on infrastructure, higher operational costs, and diminished grid efficiency. To address these challenges, this paper proposes a novel approach that combines ModelPredictive Control (MPC) with Multi-Objective Particle Swarm Optimization (MOPSO) to optimize EV charging schedules. This research introduces an intelligent and adaptive charging management strategy that not only aligns with the goals of energy efficiency and operational economy but also paves the way for sustainable integration of electric mobility into future power systems. MPC dynamically adjusts the charging profiles based on real-time data and future predictions of grid load, electricity prices, and EV usage patterns. However, single-objective optimization often falls short in managing the trade-offs between competing goals such as minimizing electricity costs, reducing peak load impact, and ensuring timely charging. To overcome this limitation, MOPSO is employed to solve the multi-objective optimization problem inherent in EV charging management. MOPSO is integrated within the MPC framework to identify a Pareto-optimal set of charging strategies that balance multiple objectives, including: (i) minimizing total electricity cost, (ii) reducing peak-to-average ratio (PAR) of grid load, and (iii) maximizing battery state-of-charge (SoC) satisfaction across all EVs. The synergy between MPC and MOPSO enables the system to iteratively forecast, evaluate, and refine EV charging actions under dynamic grid and market conditions. The proposed method is validated through extensive simulations using a smart grid test environment with realistic load profiles, time-of-use (ToU) pricing schemes, and EV mobility data. Comparative analysis with conventional rule-based and heuristic optimization methods demonstrates significant improvements in both cost savings and load flattening. Results show that the MPC-MOPSO approach reduces peak demand by up to 18%, lowers total charging cost by approximately 22%, and maintains over 95% SoC satisfaction for all participating EVs. Additionally, sensitivity analyses are conducted to evaluate the robustness of the model under varying grid constraints, EV penetration levels, and user behavior uncertainty. The results affirm the scalability and adaptability of the proposed framework for real-world applications, including smart charging infrastructure, fleet management systems, and utility-driven demand response programs.

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