Efficient EV Load Estimation for Rental Car Areas in Saudi Arabia

This thesis presents a probabilistic framework for estimating and analyzing electric vehicle charging loads in rental car areas while accounting for uncertainties associated with user behavior and charging characteristics. The study utilizes real-world vehicle mobility data, including travel distances, pick-up and drop-off times, and rental durations, together with battery specifications, EV ranges, and charging standards. Statistical models and Monte Carlo simulation are employed to estimate EV charging load profiles and evaluate different charging infrastructure scenarios, supporting effective planning and management of charging infrastructure in the rental car industry.