Assessing EV Load Uncertainty in Rental Car Fleets: Statistical Insights for Effective Charging Infrastructure

Abstract

Many countries worldwide are actively working towards achieving the United Nations Sustainable Development Goals (UNSDGs) 2030, focusing on implementing sustainable solutions to environmental, economic, and social issues. One promising and environmentally friendly solution that has gained significant international attention is the use of electric vehicles (EVs), which help reduce emissions and reliance on fossil fuels. This paper presents a qualitative study aimed at assessing the uncertainty surrounding EV load in rental car fleets. As rental agencies increasingly adopt EVs, they face challenges in forecasting charging demand, especially within limited space and infrastructure. By providing valuable data and reliable statistical models, the study estimates EV consumption for car rental companies, which might face difficulties in charging multiple vehicles efficiently. Accurate EV load estimation is essential for designing effective charging infrastructure and developing optimal load management models that reduce technical issues and economic costs. Over twelve statistical functions were tested to capture variables such as fleet size, usage patterns, and charging behavior that influence EV load formation. Additionally, this paper includes case studies to support planners and developers in improving EV charging infrastructure, offering practical insights for scaling EV integration in the rental sector and contributing to broader sustainability goals.