Electric vehicles (EVs) are going to take over the conventional cars industry like a wave in the foreseeable future. The exponential increase in sales of EVs is seen and market study has predicted that EV sales will increase by 15%–20% of total new vehicle sales by the year 2035. Despite being superior to fuel-driven vehicles, the increase in the number of EVs will have its challenges. For example, EVs are heavy uncontrolled charging loads that can lead to power surges in the grid which would then lessen the grid reliability. It is important to address the important question on how to maintain EV charging and use it in such a way that it will not pose a threat to the grid rather become its helping hand? The focus of this thesis will be to answer the aforementioned question. The main objective of the thesis is to minimize the cost of energy for charging EVs by developing and implementing a smart charging algorithm with V2G services considering the effects of battery degradation. The said objective is achieved by first formulating a mathematical model for the minimization optimization problem. Several assumptions were made to mimic real-life situations. Then an algorithm is developed and case studies on four sensitivity parameter are done. The parameters selected are the cost of penalty, cost of PV generation, cost of selling energy, and grid import power limitations. The case study is done to verify the sanity of the code and it has been observed that the algorithm developed prioritizes PV utilization, minimizes the overall cost of the node as well as EV charging cost. The algorithm incorporates the V2G application of EVs to support renewable (here, PV) sources. It has been observed that EVs that perform V2G generate financial benefit by exporting power to the grid and reduce the grid involvement by charging other EVs through V2G. V2G has adverse effects on the battery lifetime. Frequent charging and discharging can degrade the battery much quicker. The thesis tackles this problem by incorporating a battery degradation model in the developed algorithm for V2G. Two degradation model are used and compared to check the effectiveness and control while performing V2G. The first model is a simplified one and calculates the cost of battery degradation using energy exported from the battery. The second models the degradation considering one of the stress factors of cyclic aging which is C-rate. The newly developed model is observed to be more effective as the model is dependent on charging and discharging current rates. The EVs are seen charging just to their requested energy demand with relatively less power as compared to when the simplified degradation model is implemented. The V2G functionality is also observed to be reduced and utilized to reduce the grid involvement and reducing the overall cost of energy. Finally, the algorithm is compared with the uncontrolled charging algorithm. The developed smart charging algorithm is observed to reduce the charging cost of EVs significantly when the PV generation is sufficient. The smart charging algorithm utilizes the PV generation in supplying the energy demands of the local loads and EVs. This reduces the involvement of the grid. The grid is only observed to be involved when the cost of buying energy from the grid is lower to reduce any possible penalty for unfinished scenarios or when the PV generation is insufficient.