Smart Driving Towards Eco-Friendly Transportation: Fuzzy Logic Approach to Optimize Vehicle Speed Based on Road Slope and Vehicle Extra Weight

Abstract

The pressing need to address climate change by decreasing greenhouse gas emissions and improving air quality necessitates the development of eco-friendly and sustainable transportation solutions. Traditional modes of transportation largely contribute to environmental deterioration, so adopting innovative and sustainable transportation solutions is important for reducing these effects and increasing energy efficiency. This study introduces a pioneering methodology employing two separate Fuzzy Logic systems (FL) that leverage vehicle-to-infrastructure (V2I) communication technology systems. The designed FL is used to estimate a vehicle's optimal speed in order to optimize energy consumption and reduce CO2 emissions. The optimal speed is estimated based on specific factors such as vehicle velocity, road speed limit, and other parameters to estimate the optimal speed with the aim of reducing energy consumption and emissions. We have used SUMO (Simulation of Urban MObility) and Python to explore diverse scenarios, replicating road conditions with varying slopes and vehicle weights. The simulation findings highlight the transformative impacts of both FL systems combined with V2I on energy consumption and emissions, which allow cars to react and adjust their speed to changing road conditions in real-time. The vehicle's extra-weight Fuzzy Logic system and the road slope FL system exhibit a remarkable average reduction of 10% and 20%, respectively. The findings are a robust foundation for developing intelligent and eco-friendly transportation systems, contributing to the broader goal of sustainable and efficient mobility.