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