Comparative Analysis and Statistical Optimization of Fuel Economy for Sustainable Vehicle Routings

Abstract

In this 21st century, there has been an increase in the usage of renewable products for the economic drifting of vehicle transportations systems. Furthermore, due to recent trends in climate change, researchers have started focusing on statistical optimization techniques for sustainable vehicle routings. However, until now, a major gap has been noticed in the multidomain statistical analysis for optimizing the parametric levels of the vehicle fuel economy. Therefore, in this research work, two widely utilized cars (Toyota and GMC Yukon) are considered on a particular route of Jeddah for the collection of the fuel economy data under the realistic conditions of air conditioner temperature, traffic patterns, and tire pressure. The outcomes of the factorial design of the experiment highlight that the fuel economy is optimal under the low air conditioner temperature, light traffic patterns, and 34 PSI tire pressure. Three replications of the fuel economy have been considered, and the statistical significance of the correlated variables has been justified by implementing the analysis of variance (ANOVA) approach on the various levels of fuel economy. During the analysis, the statistical hypothesis for random exogenous factors has been developed by incorporating a multivariate regression model. The outcomes highlight that both air conditioner temperature and traffic patterns in Jeddah have a significant negative effect on fuel economy. Results also depict that the effect of air conditioner temperature, traffic patterns, and tire pressure is substantially higher for heavy-engine automobiles such as the GMC Yukon compared to light-engine cars (Toyota Corolla). Furthermore, a normality test has also been considered to validate the outcomes of the proposed model. Therefore, it is highly recommended to utilize the proposed methodology in optimizing the trends of fuel economy for sustainable vehicle routings. Based on the findings of multidomain statistical analysis, it is also highly recommended the utilization of the Toyota Corolla car model for investigating the correlation of external undeniable factors (braking frequency, metrological conditions, etc.) with the trends of vehicle fuel economy.

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

“…The second model focuses on refueling behavior and considers the number of refueling trips taken by vehicles but also considers the influence of weather conditions and time of year. These factors are considered important because a number of studies indicate that fuel consumption patterns are influenced by weather [30]. These studies show that there are observable trends in refueling frequency over different seasons, and the statistical significance of these patterns varies [30,35].…”

“…These factors are considered important because a number of studies indicate that fuel consumption patterns are influenced by weather [30]. These studies show that there are observable trends in refueling frequency over different seasons, and the statistical significance of these patterns varies [30,35]. This model utilizes Generalized Poisson modeling and count-based data.…”

“…In contrast, the GP-1 model considers the influence of temperature, precipitation, and period of year as factors influencing vehicle refueling trips. Many studies have shown that fuel consumption patterns are influenced by factors such as weather conditions as indicated in [30].…”