Multivariate and location-specific correlates of fuel consumption

Multivariate and location-specific correlates of fuel consumption: A test track study

Melman, T. (TU Delft Human-Robot Interaction; Group Renault; ENSTA Paris)
Abbink, D.A. (TU Delft Human-Robot Interaction)
Mouton, Xavier (Group Renault)
Tapus, Adriana (ENSTA Paris)
de Winter, J.C.F. (TU Delft Human-Robot Interaction)

2021

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.

CAN-bus data
Driving metrics
Eco-driving
Principal component analysis (PCA)
Test track

http://resolver.tudelft.nl/uuid:5f8e3183-4c4f-4a85-8557-884fd222011b

https://doi.org/10.1016/j.trd.2020.102627

1361-9209

Transportation Research. Part D: Transport & Environment, 92

Institutional Repository

journal article

© 2021 T. Melman, D.A. Abbink, Xavier Mouton, Adriana Tapus, J.C.F. de Winter