Impacts of Extreme Ambient Temperatures on Electric Vehicle Charging Demand: A Case Study in Qatar

This study investigates the energy consumption patterns of an electric vehicle (EV) under extreme ambient temperature conditions using real-world GPS traces collected from the State of Qatar. The objective is to assess seasonal variations in charging demand and overall energy use of the vehicle. A real-world driving profile, derived from GPS data collected from actual vehicles, is integrated with regional meteorological data to conduct comparative analyses using emobpy, a Python-based EV simulation tool. The analysis uses a 2023 BMW i5 (81.2 kWh) as a representative EV model for Qatar, for which battery state-of-charge, energy consumption, and spatial-temporal driving profiles are generated for the coldest (January) and hottest (July) months of the year. Results indicate a 20.9% increase in energy consumption in July relative to January, driven primarily by elevated thermal management and cabin cooling loads. Despite these seasonal differences, the EV completed all daily travel requirements without battery depletion, relying solely on residential nighttime charging. These findings confirm the viability of home-based charging in hot climates and underscore the necessity of accounting for seasonal energy demand variations in EV adoption and infrastructure planning.