Route-Sensitive Fuel Consumption Models for Heavy-Duty Vehicles

This article investigates the ability of data-driven models to estimate instantaneous fuel consumption over 1 km road segments from different routes for different heavy-duty vehicles from the same fleet. Models are created using three different techniques: parametric, linear regression, and artificial neural networks. The proposed models use features derived from vehicle speed, mass, and road grade, which can be easily obtained from telematics devices, in addition to power take-off (PTO) active time, which is needed to capture the power requested by accessories in several heavy-duty vehicles. The robustness of these models with respect to the training data selection is improved by using k-fold cross-validation. Moreover, the inherent underestimation or overestimation bias of the model is calculated and used to offset the fuel consumption estimates for new routes. The study shows that the target application dictates the choice of model features. In fact, the results indicate that depending on the vocation the linear regression and neural network models, which use the same input features, are able to adequately differentiate between the fuel consumption of two vehicles from the same fleet as well as between the fuel consumption of a single vehicle over two different routes. However, the parametric model, which does not utilize PTO active time, is unable to differentiate between two vehicles from the same fleet. This latter model is more suitable for comparing fuel consumption across different fleets of vehicles. In summary, vocation-specific models should be used to optimize fuel consumption for a given fleet of vehicles, whereas general models can only provide insight into aggregated fuel consumption for entire fleets. Moreover, both the accuracy and the precision of the models as measured by their confidence interval should be taken into consideration when comparing fuel consumption estimates for two vehicles from the same fleet or the fuel consumption estimates of an individual vehicle for two different routes. This study shows that the artificial neural network models have narrow 95% confidence intervals and are therefore more precise than the equivalent linear regression models.