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World Electric Vehicle Journal is published by MDPI from Volume 9 issue 1 (2018). Articles in this Issue were published by The World Electric Vehicle Association (WEVA) and its member the European Association for e-Mobility (AVERE), the Electric Drive Transportation Association (EDTA), and the Electric Vehicle Association of Asia Pacific (EVAAP). They are hosted by MDPI on mdpi.com as a courtesy and upon agreement with AVERE.
Open AccessArticle

Real-time electric vehicle mass identification

1
Singapore University of Technology and Design, Singapore
2
Massachusetts Institute of Technology, USA
3
ETH-Zurich, Switzerland
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2013, 6(1), 141-146; https://doi.org/10.3390/wevj6010141
Published: 29 March 2013
PDF [399 KB, uploaded 17 May 2018]

Abstract

A technique capable of identifying electric vehicle (EV) mass in real-time has been a topic of research for several years due to the advantages it presents, such as the ability to dramatically improve range estimates, perform more effective torque vectoring for ABS/ESC, track delivery vehicle weight, etc.. Some crucial issues in mass identification impede an easy implementation of such an algorithm, however, and this work introduces a simple method to calculate EV mass on-the-fly using standard data available on most CAN buses and therefore without the need of additional sensors. The results presented here are achieved using an eight step technique suitable for accurate mass estimations during wide-open-throttle acceleration events. The algorithm’s instantaneous error is less than 10%, and converges to better than 3% absolute accuracy performance with subsequent measurements. A preliminary analysis of trips lacking hard acceleration presented in this paper show an inability to differentiate between loaded and unloaded conditions.
Keywords: modeling & simulation; real-time; mass identification; electric vehicles modeling & simulation; real-time; mass identification; electric vehicles
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wilhelm, E.; Rodgers, L.; Bornatico, R. Real-time electric vehicle mass identification. World Electr. Veh. J. 2013, 6, 141-146.

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