The use of renewable energy and electric cars (EVs) can reduce energy prices and minimize radiation to a substantial extent. The intermittent nature of renewable energy sources and their sporadic use, integrating renewable energy sources into the electric grid is a challenging process. If the supply of renewable energy is not adequate to fulfill the requirement, it is expensive to pull more energy from the power grid. This may also reduce the energy efficiency of the charging process. The focus of this study is to present methods for optimizing a controller for the charging of electric vehicle batteries using renewable energy sources for energy management. Electrification of transportation, in particular, is viewed as one of the key avenues to achieving considerable CO2 emission reductions. EVs have gained popularity in recent years, and more than 180,000 have been deployed worldwide too far. Even though this number represents just 0.02% of all cars on the road, the International Energy Agency has set a lofty goal of having over 10 million electric buses and light-duty vehicles (EV) on the road by 2030. The output of the Dynamic Programming optimization and a variety of other approaches used in energy optimizing software are compared to determine which one would accurately minimize the cost of energy required for charging and yet fulfill the aggregate battery charge maintaining criteria. Based on this study it is evident that the Dynamic Programming based optimization strategy provides the highest level of accuracy while maintaining a consistently high level of fuel savings in EVs.