Abstract:
The driving cycles of electric vehicles are critical in their optimal design, planning, and policy formulation. The mountainous urban topography and distinct driving habits in Islamabad motivated the construction of a real-world urban driving cycle, termed the Islamabad driving cycle, with a road slope profile in this research work. A novel hybrid data collecting methodology is used, which includes a global positioning system, on-board diagnostics, and a digital surface model sourced from Japanese aerospace exploration agency. Random cars were chased during data collection to guarantee that all road occupants' driving patterns were captured. The driving cycle was built using the Markov chain Monte Carlo method, which took into account the weights of various road types in the research area. Using 15 characteristics, 4 distribution parameters, and the speed acceleration probability distribution, the Islamabad driving cycle is compared to other internationally legislated driving cycles. This comparison demonstrated that the Islamabad driving cycle is truly one-of-a-kind, and no other driving cycle can replace it. Powertrain simulations were conducted on 24 vehicle models comprising diverse vehicular technologies under 8 different driving cycles using the modified python application programming interface of FASTSim developed by national renewable energy laboratory. The results of the powertrain simulations were used to evaluate essential characteristics such as energy consumption, range, grid charging energy demand factors, and carbon dioxide emission factors. The validation results show that without the road slope profile, errors ranging from 10.2–22.2 % accumulated in the powertrain simulation, in addition to significant influences on the factors evaluated. As a result, precise simulation of electric vehicle performance on a larger scale in Islamabad attests to the need for road slope profiles for such hilly urban areas.
