An Investigation of factors Influencing electric vehicles charging Needs: Machine learning approach

Decarbonization of the world is greatly contributed to by the recent technological advancements that have fostered the development of electric vehicles (EVs). The EVs relieve transportation dependence on natural fossil fuels as an energy source. More than 50 % of the petroleum products produced worldwide are estimated to be used in the transportation sector, accounting for more than 90 % of all transportation energy sources. Consequently, studies estimate that the transportation sector produces about 22 % of global carbon dioxide emissions, posing significant environmental issues. Thus, using EVs, particularly on road transport, is expected to reduce environmental pollution. To accelerate EV development and deployment, governments worldwide invest in EV development through various initiatives to make them more affordable. This research aims to investigate the changing needs of EV users to establish factors to be considered in the selection of charging demands using machine learning, using an extreme gradient boosting model. The model reached high accuracy, with an R2-Score of 0.964 to 1.000 across all predicted needs. The model performance is greatly affected by age, median income, education, and car ownership. High values of people with high income, high education, and age between 35–54 years show a positive contribution to the model's performance, contrary to those with 65+, low income, and low education attainment. The outcomes of this research document factors that influence EV charging needs; therefore, it provides a basis for decision-makers and all stakeholders to decide where to locate EV charging stations for usability, efficiency, sustainability, and social welfare.