Multivariate and location-specific correlates of fuel consumption
A test track study
Timo Melman (TU Delft - Mechanical Engineering, ENSTA Paris, Group Renault)
David Abbink (TU Delft - Mechanical Engineering)
Xavier Mouton (Group Renault)
Adriana Tapus (ENSTA Paris)
Joost de Winter (TU Delft - Mechanical Engineering)
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Abstract
Current predictors of fuel consumption are typically based on computer simulations or data collections in real traffic, where the route and vehicle type are not under the researcher's control. Here, we predicted fuel consumption using test track data, an approach that allowed for location-specific predictions. Ninety-one drivers drove a total of 4617 laps, in two vehicles (Renault Mégane, Renault Clio), on two routes (highway and mountain), and with two eco-driving instructions (normal and eco). A multivariate analysis at the level of laps showed a strong predictive value for metrics related to speed, RPM, and throttle position, but with a considerable amount of variance attributable to route and vehicle type. A subsequent location-specific analysis showed that the predictive correlation of driving speed and throttle position fluctuated strongly during the lap and at some locations even became negative. We conclude that there is considerable potential in instantaneous location-specific prediction of fuel consumption.
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