Electric vehicles (EV) in the transportation sector will play a major role in achieving low-carbon emissions from the mobility of vehicles. With a future increase in electric vehicles and autonomous electric vehicles, the demand for charging increases and this comes with new problems that require modern solutions. The integrity of the power grid infrastructure can be at risk as grid congestion and power mismatch can cause problems as the act of charging the vehicles adds an extra burden on the power grid. Today, there exist several scenarios on how a vehicle can be charged and a number of technical solutions that are advantageous based on different aspects such as user's need, price of the electricity, and electricity network.   This thesis focuses on identifying different charging scenarios in mapping the information that is required and studying the effects of these charging scenarios through a smart charging algorithm. A smart charging algorithm to optimise for charging and discharging of the electric vehicle is developed and tested in a MATLAB environment with the aim to achieve a balanced grid load profile. The simulation results verify the potential of the algorithm to reduce the adverse effects of electric vehicle charging. Additionally, a model to map autonomous electric vehicles to charging station in accounts of a low state of charge is also developed in MATLAB