Peak oil, climate change and the achievement of energy independence have set the world into a new energy transition seeking to decarbonize both the power generation and transportation sectors. The convergence and synergy between both sectors, an electrified transportation powered by renewable energy, holds true potential to significantly reduce the world’s dependence on fossil fuels and the consequent emission of greenhouse gases. Nonetheless the integration of electric vehicles on the power system is not a minor issue and its associated impacts need to be carefully analyzed and addressed. In this context the main objective of this thesis is to develop a smart charging solution to safely integrate Plug-in Electric Vehicles into low voltage distribution networks while mitigating their corresponding impacts. This is done by designing a control algorithm capable to simultaneously meet both voltage and thermal network constrains by managing the vehicle’s charging process in real-time, while at the same time being respectful to current charging standards, computationally light and implementable in any current radial distribution grid. The proposed charging algorithm is built under a “Multi-Agent System” architecture combining a local decentralized voltage management with a centralized thermal control conceived to minimize user impact under three alternative optimization techniques. The architecture is fully implemented and tested on a realistic environment using Simulink by modeling key elements such as the charging stations, the vehicles themselves, and the different dwellings. An extensive analysis of the algorithm’s performance is completed testing it under multiple relevant scenarios. Finally the control algorithm is transferred to a real-time simulator (dSPACE MicroLabBox) to be validated and further evaluate its behavior through hardware-in-the-loop (HIL) simulations using both real charging stations and Plug-in Electric Vehicles available in the laboratory A satisfactory validation of the algorithm’s execution under and compatibility with real hardware is revealed by the HIL tests. At the same time a successful management of the considered network voltage and thermal constrains is obtained under all implementations, causing a minimal impact over the participating users and effectively peak shaving their total aggregated demand. For a full vehicle penetration scenario, the proposed control successfully prevents over 200 voltage limit violations and achieves so while significantly reducing the total transformer loading deviation, almost halving it when compared to an equivalent unrestricted charging case, and ensuring a net zero impact to 95% of participating users under its most effective thermal implementation. The study also reveals that a full participation of all vehicle owners is not critical factor to ensure a proper grid operation under the designed algorithm, as satisfactory results are also obtained when only half of them are actively involved in network management. Finally an economic assessment covering the application of the designed control over conventional network reinforcements further demonstrates the advantages of such an active management approach reporting additional economic benefits to the DSO.