<![CDATA[The rapid deployment of Electric Vehicles (EVs) has significantly increased grid congestion, particularly in regions with limited capacity for infrastructure expansion where system operators no longer permit customers to extend grid connections. Dynamic energy pricing has emerged to incentivize consumers to optimize energy use through time-of-day tariffs. However, existing smart charging approaches typically optimize grid constraints, cost, or user preferences in isolation, with limited integration of these objectives. This paper proposes a cloud-based Model Predictive Control (MPC) framework for smart EV charging that simultaneously enforces grid power limits, minimizes charging cost, and satisfies user-defined requirements. The proposed method incorporates day-ahead market (DAM) electricity prices, real-time building load, photovoltaic (PV) forecasts, and EV user inputs within a multi-objective optimization problem solved using a receding horizon strategy. The approach is validated through both simulation and a real-world deployment in a commercial building with multiple EV chargers. Results show that the proposed strategy achieves charging cost reductions of up to 95% under favorable overnight pricing conditions and up to 87% in real-world operation with grid constraints, while maintaining user satisfaction. The findings demonstrate the practical feasibility and contribution of an integrated, cloud-based MPC approach for scalable, cost-efficient, and grid-compliant EV charging.]]>
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