The performance of battery electric vehicles (BEVs) is significantly affected by ambient temperature, especially in areas with substantial seasonal fluctuations. This study examines the influence of Uzbekistan’s continental climate on electric vehicle range, battery degradation, and operational expenses through the development of a MATLAB/Simulink-based electro-thermal vehicle model. Three representative BEVs were selected for the simulation study: the Nissan Leaf (24 kWh), the Chevrolet Bolt EV (60 kWh), and the 2017 Volkswagen e-Golf (35.8 kWh, DC fast-charging version). The modeling framework combines longitudinal vehicle dynamics, battery behavior that changes with temperature, HVAC auxiliary loads, and range data that has been tested in controlled laboratory campaigns. The model was validated using open-source test results at different ambient temperatures, and the measured data matched the model very closely. Using validated temperature–range relationships, monthly climatic data for Uzbekistan were applied to estimate annual charging frequency for a representative annual driving distance. Results indicate that real climatic operation increases annual charging demand compared to moderate-condition OEM expectations. This increase not only affects electricity expenditure but also accelerates battery cycling intensity. A piecewise multiplicative degradation model was therefore implemented to quantify capacity loss under climate-adjusted cycling conditions. The findings demonstrate that continental temperature extremes significantly influence both short-term vehicle efficiency and cycle-induced battery capacity degradation. The proposed framework provides a climate-aware predictive tool for realistic performance estimation and supports evidence-based planning for electric mobility deployment in regions with pronounced seasonal variability.

This study compares the regenerative braking capability and energy efficiency of a small-class electric vehicle based on the Wuling Macaron EV with FWD or RWD engine configuration. Both drivetrain configurations were simulated under NEDC and WLTP Class 3 driving cycles using MATLAB/Simulink and parallel regenerative braking control, with the vehicle’s maximum speed limited to 100 km/h to emulate real operating conditions. Drivetrain arrangement significantly affects regenerative energy recovery. FWD had greater negative battery current peaks during deceleration, faster energy recovery, and a slower SOC decline than RWD. FWD layouts achieved up to 15 % greater regeneration energy and 3.4 percent lower net energy use under WLTP conditions. Due to better front-axle load transfer, traction and regenerative torque improve. An average driving distance of 20,000 km in Uzbekistan results in an estimated annual energy savings of 98 kWh, or 88,000 UZS (≈8 USD) per vehicle. Data shows that electric automobiles with higher motor torque and power ratings may have better regenerative braking. The results show that front-wheel drive and proper motor features improve braking energy recovery and vehicle performance more efficiently and economically for small urban electric cars.