Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach

TL;DR

This paper presents an explainable machine learning model to predict and optimize fuel consumption in industrial vehicle fleets, demonstrating significant potential for cost savings and emission reductions through driving behavior adjustments.

Contribution

It introduces an Explainable Boosting Machine model for fuel prediction that aligns with domain knowledge and quantifies the impact of driving behavior on fuel efficiency.

Findings

70% of fuel-factor categories match previous literature

Optimizing driving behavior can reduce fuel consumption by 12-15%

The model provides interpretable insights consistent with domain expertise

Abstract

Fuel optimization of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style. In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between the input factors and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70% of the categories associated to the fuel-factors are similar to the previous literature. With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12% and 15% in a large fleet (more than 1000 vehicles).