Abstract
This study assesses the fuel consumption and emissions of fuel cell vehicles (FCVs) and internal combustion engines (ICEs) under real driving conditions, considering an FC component and a vehicle equipped with a gasoline direct injection (GDI) engine. The simulation model for the internal combustion engine is based on machine learning and implements the Wiebe combustion model. The study uses weather conditions in Tehran and natural navigation routes in the city to increase the model's realism. The results show that fuel consumption depends on critical cycle parameters, such as average speed, maximum speed, and stopping time. Driving behavior and routes also significantly impact pollution levels, with HC and NOx showing no clear correlation, indicating that each particle's production pattern is independent. The study found that navigation time for fuel cell cars is directly related to hydrogen consumption and battery state of charge. Machine learning can be used to improve the performance of both types of vehicles under real driving conditions. Additionally, the study shows that driving behavior and route can reduce NOx production in engine production by 52 % and energy consumption by 38 %.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call