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New environmental legislation on emission and fuel efficiency targets increasingly requires good transient engine performance and this in turn means that the previously acceptable static engine calibration and control methodologies based on steady-state testing must be re-placed by dynamical optimization using dynamical models. Although many advances have been made in predictive models for internal combustion engines, the phenomena involved are so many, complex and nonlinear that dynamical black-box models typically employing neural network structures must be determined from system identification through experimental testing. Such identified dynamical models are required to provide high accuracy multiple step-ahead predictions of emissions but must accordingly also be compactly implementable for speed and memory to allow for the required large scale optimization involving possibly many thousands of iterations.

This paper introduces a new method for driving style identification based on vehicle communication signals. The purpose of this method is to classify a trip, driven in a vehicle, into three driving style categories: calm, normal or aggressive. The trip is classified based on the vehicle class, the type of road it was driven on (urban, rural or motorway) and different types of driving events (launch, accelerating and braking). A representative set of parameters, selected to take into consideration every part of the driver-vehicle interaction, is associated to each of these events. Due to the usage of communication signals, influence factors, other than vehicle speed and acceleration (e.g. steering angle or pedals position), can be considered to determine the level of aggressiveness on the trip. The conversion of the parameters from physical values to dimensionless score is based on conversion maps that consider the road and vehicle types.