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Semi-autonomous Driving Based on Optimized Speed Profile

  • Sebastiaan van AalstEmail author
  • Boulaid Boulkroune
  • Shilp Dixit
  • Stephanie Grubmüller
  • Jasper De Smet
  • Koen Sannen
  • Wouter De Nijs
Chapter
  • 481 Downloads
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Electric vehicles (EVs) are rapidly becoming a viable alternative to internal combustion engine vehicles (ICEVs). Despite the recent advances in battery technology, the driving range of an EV per charge is still shorter than that of an ICEV. The range of an EV can be increased by considering and factoring in all possible environmental information resulting in more energy efficient driving. A suitable way to reduce energy consumption can be devised by calculating an energy optimal speed profile and employing an advanced longitudinal control system to automatically track this speed profile. To this end, this chapter presents two model-based control approaches for longitudinal control of EVs that directly incorporate the most important design objectives, being tracking accuracy and ride comfort, in their control design: a novel exponentially stabilizing gain scheduling proportional-integral controller (gs-PI), and a state-of-the-art offset-free explicit model predictive controller with preview (e-MPC). These controllers were tested experimentally with an electric vehicle demonstrator on a 4 wheel drive rolling road for a realistic driving scenario. Comparison results are provided and show that both control approaches provide very promising behavior and achieve the prescribed performance criteria.

Keywords

Model Predictive Control Speed Profile Controller Area Network Ride Comfort Adaptive Cruise Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by the European Union Seventh Framework Programme FP7/2007-2013 under the iCOMPOSE project (grant agreement no. 608897). The authors also acknowledge the financial support of the COMET K2—Competence Centres for Excellent Technologies Programme of the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT), the Austrian Federal Ministry of Science, Research and Economy (BMWFW), the Austrian Research Promotion Agency (FFG), the Province of Styria and the Styrian Business Promotion Agency (SFG).

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Sebastiaan van Aalst
    • 1
    Email author
  • Boulaid Boulkroune
    • 1
  • Shilp Dixit
    • 1
  • Stephanie Grubmüller
    • 2
  • Jasper De Smet
    • 1
  • Koen Sannen
    • 1
  • Wouter De Nijs
    • 1
  1. 1.Flanders MakeLommelBelgium
  2. 2.VIRTUAL VEHICLE Research CenterGrazAustria

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