Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach
This work addresses cost and emission reduction for industrial fleet operators, but it is incremental as it applies an existing explainable AI method to a specific domain.
The study tackled fuel optimization for industrial vehicle fleets by developing an Explainable Boosting Machine model to predict fuel consumption using real-world data, estimating that optimizing driving behavior could reduce fuel consumption by 12-15% in large fleets.
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).