Driving segment simulation for determination of the most effective driving features for HEV intelligent control

Abstract

This paper presents a methodological approach for determination of the most effective driving features for hybrid electric vehicle intelligent control, using the driving segment simulation. In this approach, driving data gathering is first performed in real traffic conditions using Advanced Vehicle Locator systems. The vehicle's speed time series are then divided into small segments. Subsequently, 19 driving features are defined for each driving segment, and the influence of the driving features on the vehicle's fuel consumption (FC) and exhaust emissions is investigated, using driving the driving segment simulation. The simulation approach is also verified by experimental test. Finally, the driving features are ranked by a new approach based on the definition of an effectiveness index and a correlation analysis. The results demonstrate that the velocity-dependent driving features such as ‘energy’, ‘mean of velocity’, ‘displacement’ and ‘maximum velocity’ are more effective on vehicle's FC and exhaust emissions. However, because of high dependency between these features, this study suggests independent driving features among the most effective driving features.