As a result of previous studies it has been proven that electric vehicles have seen enormous growth in recent years and will continue to increase in number in the United States in coming years. In Irdmousa’s analysis of alternative energy sources it is suggested that electricity is the most suitable energy for transportation purposes for the next 30 years when benefits such as emissions, availability, maintainability, efficiency and reliability are considered (2010). Despite the benefits that electric vehicles provide it is paramount to investigate the impact of plug in electric vehicles (PEVs) on the electric distribution grid to protect utility system components as well as electric distribution levels and quality. Misra states that the uncoordinated charging of PEVs on a residential power grid would result in increases in power losses, overloading of lines, power surges, and increased harmonics in the grid due to non-linearity (2017). In regards to EV batteries there are three different variables that are shared: capacity (kWh), range (km) and total energy consumption (kWh/km). Furthermore it is paramount to factor in the relationship between power consumed and the batteries State of Charge (SoC). Valsera et al. determined a relationship between the electric vehicle model to battery characteristics to its charging process (2011). However this charging process differentiates by where the tests are being conducted due to different charging standards. For example the standard charging process in Europe of IEC 61851 in Europe differentiates from the process in the United States of SAE J1772. Also voltage levels for charging differs depending on areas. Valsera et al. have a common power protection up to 16 amps in Spain (2010) while Cao et al. have a power protection up to 20 amps in the United States (2016). Different researchers also use different power profiles. Clemen-Nynst et al. (2010) and Misra et al. (2017) use constant power profiles while on the other hand Fard et al. (2015), Au (2012), Leemput et al.(2015), and Cao et al. (2016) all use variable power in their charging profiles. The vehicles charging infrastructure also plays a key role in assessing the charging demand and impact considering that it includes the electric vehicle charging point socket and availability to charge. Most papers on the subject to not take into account the electric vehicle infrastructure that is required when determining EV charging demand. The common assumption is that there are enough charging stations and complete compatibility between the EV and charging station connectors. This could be plausible in the coming future with a large EV population, however it could be an issue when considering fast charging (Olivella-Roselle al. ,2015). A key aspect when analyzing EV charging is the possibility of both slow and fast charging scenarios. EV charging can be controlled locally to smoothen the profile of power demand on the electric distribution network (Clement-Nyns et al., 2010). Optimal charging times can be used to both reduce strain on the grid during peak times (Misra et al. ,2017) as well as save the consumer money by charging during times of surplus when electricity is cheaper (Leemput et al. ,2015). This reduces the simultaneous electricity usage from a household and the EV and reduces grid losses (Au, 2012). A wide array of charging optimization programs are being tested to be used to enhance the charging process of EVs. This includes the use of convex quadratic programming, dynamic programming, sequential quadratic programming (Olivella-Rosell et al. ,2015), linear programming, and heuristic programming (Cao et al. ,2016). These various types of charging algorithms have varying benefits such as execution time, completeness, accuracy, and optimality. Possible effects on the electric grid due to electric vehicle increase are related to power quality output and grid saturation (Olivella-Rosell et al. ,2015). The majority of current studies analyze electric vehicles and their relevance to voltage drops and excessive transformer load such as Clement et al., Valsera et al. and Misra et al. They mainly analyze losses in Joules including imbalances and overloading.