Title: 'Journey Mapping', re-defined drive cycle: an accurate vehicle performance prediction tool

Authors: Kavya Prabha Divakarla; Ali Emadi; Saiedeh N. Razavi

Addresses: Faculty of Engineering, Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada ' Faculty of Engineering, Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada ' Faculty of Engineering, Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada

Abstract: With the increasing popularity of hybrid electric vehicles (HEVs), updating their test procedures to have more accurate vehicle performance prediction has become essential. Traditionally, vehicles are tested using standardised drive cycles, which are not sufficient to represent real-life driving scenarios for different conditions that a vehicle might encounter during its life-cycle across all users. This results in high discrepancies between the predicted and the actual vehicle performance. As such, this study highlights the application of a novel concept called Journey Mapping (JM), which re-defines drive cycles to provide more realistic and accurate vehicle performance prediction, for studying a test HEV's performance. JM incorporates real-life conditions that might influence a vehicle during its journey from an origin to a destination. The JM model was able to predict the test HEV's performance with only about 2% error, on average, between the predicted and the actual performance, for the scope of this study.

Keywords: automotive drive cycles; data acquisition hybrid and electric vehicles; predictive models and simulations.

DOI: 10.1504/IJEHV.2017.085349

International Journal of Electric and Hybrid Vehicles, 2017 Vol.9 No.2, pp.169 - 186

Received: 18 Feb 2017
Accepted: 05 Mar 2017
Published online: 23 Jul 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article