Energy Efficient Driving in Dynamic Environment: Considering Other Traffic Participants and Overtaking Possibility

  • Zlatan AjanovićEmail author
  • Michael Stolz
  • Martin Horn
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


This chapter studies energy efficient driving of (semi)autonomous electric vehicles operating in a dynamic environment with other traffic participants on a unidirectional, multi-lane road. This scenario is considered to be a so called hard problem, as constraints imposed are varying in time and space. Neglecting the constraints imposed from the surrounding traffic, the generation of an energy optimal speed trajectory may lead to bad results, with the risk of low driver acceptance when applied in a real driving environment. An existing approach satisfies constraints from surrounding traffic by modifying an existing unconstrained trajectory. In contrast to this, the proposed approach incorporates a leading vehicle’s motion as constraint in order to generate a new optimal speed trajectory in a global optimal sense. First simulation results show that energy optimal driving considering other vehicle participants is important. Even in simple setups significantly (8%) less energy is consumed at only 1.3% travelling time prolongation compared to the best constant speed driving strategy. Additionally, the proposed driving strategy is using 4.5% less energy and leads to 1.6% shorter travelling time compared to the existing overtaking approach. Using simulation studies, the proposed energy optimal driving strategy is analyzed in different scenarios.


Overtaking Car following Ecodriving Optimal speed trajectory 



The project leading to this study has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675999, ITEAM project.

VIRTUAL VEHICLE Research Center is funded within the COMET—Competence Centers for Excellent Technologies—programme by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT), the 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). The COMET programme is administrated by FFG.


  1. 1.
    Bingham C, Walsh C, Carroll S (2012) Impact of driving characteristics on electric vehicle energy consumption and range. IET Intel Trans Syst 6(1):29–35CrossRefGoogle Scholar
  2. 2.
    Minett CF, Salomons AM, Daamen W, Van Arem B, Kuijpers S (2011) Eco-routing: comparing the fuel consumption of different routes between an origin and destination using field test speed profiles and synthetic speed profiles. In 2011 IEEE forum on integrated and sustainable transportation system (FISTS), ViennaGoogle Scholar
  3. 3.
    Hellström E (2005) Explicit use of road topography for model predictive cruise control in heavy trucks. MS thesis, Linkoping University, SwedenGoogle Scholar
  4. 4.
    Hellström E (2010) Look-ahead control of heavy vehicles. Ph.D. thesis, Linköping, SwedenGoogle Scholar
  5. 5.
    Saerens B (2012) Optimal control based eco-driving. Ph.D. thesis, Katholieke Universiteit Leuven, LeuvenGoogle Scholar
  6. 6.
    Kamal MAS, Mukai M, Murata J, Kawabe T (2011) Ecological vehicle control on roads with up-down slopes. IEEE Trans Intell Transp Syst 12(3):783–794CrossRefGoogle Scholar
  7. 7.
    Vajedi M, Azad NL (2016) Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control. IEEE Trans Intell Transp Syst 17(1):113–122CrossRefGoogle Scholar
  8. 8.
    Sciarretta A, Nunzio GD, Ojeda L (2015) Optimal ecodriving control: energy-efficient driving of road vehicles as an optimal control problem. IEEE Control Syst Mag 71–90Google Scholar
  9. 9.
    Mahler G, Vahidi A (2012) Reducing idling at red lights based on probabilistic prediction of traffic signal timings. In: 2012 American control conference (ACC), MontrealGoogle Scholar
  10. 10.
    De Nunzio G, Wit CC, Moulin P, Di Domenico D (2013) Eco-driving in urban traffic networks using traffic signal information. In: 52nd IEEE conference on decision and control, Florence, Italy, 10–13 Dec 2013Google Scholar
  11. 11.
    Kural E, Jones S, Parrilla AF, Grauers A (2014) Traffic light assistant system for optimized energy consumption in an electric vehicle. In: International Conference on Connected Vehicles and Expo (ICCVE)Google Scholar
  12. 12.
    The SARTRE Project. [Online]. Available: Accessed 11 Aug 2016
  13. 13.
    Mensing F, Bideaux E, Trigu R, Tattegrain H (2013) Trajectory optimization for eco-driving taking into account traffic constraints. Trans Res Part D Trans Environ 18:55–61Google Scholar
  14. 14.
    Schmied R, Waschl H, del Re L (2016) Comfort oriented robust adaptive cruise control in multi-lane traffic conditions. In: 8th IFAC international symposium on advances in automotive control, Norrköping, Sweden, 2016Google Scholar
  15. 15.
    Wang M, Hoogendoorn S, Daamen W, van Arem B, Happee R (2015) Game theoretic approach for predictive lane-changing and car-following control. Transp Res Part C Emerg Technol 58(Part A):73–92Google Scholar
  16. 16.
    Murgovski JSN (2015) Predictive cruise control with autonomous overtaking. In: 54th IEEE conference on decision and control (CDC), Osaka, Dec 2015Google Scholar
  17. 17.
    Kamal MAS, Taguchi S, Yoshimura T (2016) Efficient vehicle driving on multilane roads using model predictive control under a connected vehicle environment. IEEE Trans Intell Transp Syst (99):1–11Google Scholar
  18. 18.
    Shamir T (2004) How should an autonomous vehicle overtake a slower moving vehicle: design and analysis of an optimal trajectory. IEEE Trans Autom Control 49:607–610MathSciNetCrossRefGoogle Scholar
  19. 19.
    Bellman R (1954) The theory of dynamic programming. The Rand Corporation, Santa MonicazbMATHGoogle Scholar
  20. 20.
    Bertsekas D (2007) Dynamic programming and optimal control. Athena ScientificGoogle Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Area Electrics/Electronics and SoftwareVirtual Vehicle Research CenterGrazAustria
  2. 2.Institute of Automation and ControlGraz University of TechnologyGrazAustria

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