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Model-Based Eco-Routing Strategy for Electric Vehicles in Large Urban Networks

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Comprehensive Energy Management – Eco Routing & Velocity Profiles

Abstract

The presented work discusses a novel eco-routing navigation strategy and energy consumption modeling approach for electric vehicles. Speed fluctuations and road infrastructure have a large impact on vehicular energy consumption, especially in urban environment. Neglecting these effects may lead to large errors in eco-routing navigation, which could trivially select the route with the lowest average speed. An energy consumption model that accurately considers both accelerations and impact of the road infrastructure is presented. This is achieved by separating the costs of all the possible turning movements in the transportation network by means of the adjoint graph. It is demonstrated that the proposed strategy is more effective and reliable than the state-of-the-art approaches in predicting vehicle energy consumption and in suggesting an energy-efficient route.

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References

  1. Sciarretta A, De Nunzio G, Ojeda LL (2015) Optimal ecodriving control: energy-efficient driving of road vehicles as an optimal control problem. IEEE Control Syst Mag 35(5):71–90

    Article  MathSciNet  Google Scholar 

  2. Ahn K, Rakha H (2008) The effects of route choice decisions on vehicle energy consumption and emissions. Transp Res Part D 13:151–167

    Article  Google Scholar 

  3. Ericsson E, Larsson H, Brundell-Freij K (2006) Optimizing route choice for lowest fuel consumption potential effects of a new driver support tool. Transp Res Part C 14:369–383

    Article  Google Scholar 

  4. Kubička M, Klusáček J, Sciarretta A, Cela A, Mounier H, Thibault L, Niculescu S-I (2016) Performance of current eco-routing methods. Intelligent vehicles symposium, 2016

    Google Scholar 

  5. Guo C, Yang B, Andersen O, Jensen CS, Torp K (2015) EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data. Geoinformatica 19:567–599

    Article  Google Scholar 

  6. Minett CF, Maria Salomons A, 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. IEEE forum on integrated and sustainable transportation systems, pp 32–39

    Google Scholar 

  7. Guo C, Yang B, Andersen O, Jensen CS, Torp K (2015) EcoSky: reducing vehicular environmental impact through eco-routing. In: 31st IEEE international conference on data engineering, pp 1412–1415

    Google Scholar 

  8. Ahn K, Rakha HA (2013) Network-wide impacts of eco-routing strategies: a large-scale case study. Transp Res Part D 25:119–130

    Article  Google Scholar 

  9. Boriboonsomsin K, Barth MJ, Zhu W, Vu A (2012) Eco-routing navigation system based on multisource historical and real-time traffic information. IEEE Trans Intell Transp Syst 13(4):1694–1704

    Article  Google Scholar 

  10. Yao E, Song Y (2013) Study on eco-route planning algorithm and environmental impact assessment. J Intell Transp Syst 17(1):42–53

    Article  Google Scholar 

  11. Richter M, Zinser S, Kabza H (2012) Comparison of eco and time efficient routing of ICEVs, BEVs and PHEVs in inner city traffic. IEEE vehicle power and propulsion conference, pp 1165–1169

    Google Scholar 

  12. Jurik T, Cela A, Hamouche R, Natowicz R, Reama A, Niculescu SI, Julien J (2014) Energy optimal real-time navigation system. IEEE Intell Transp Syst Mag 6(3):66–79

    Article  Google Scholar 

  13. Nie YM, Li Q (2013) An eco-routing model considering microscopic vehicle operating conditions. Transp Res Part B 55:154–170

    Article  Google Scholar 

  14. Bellman R (1958) On a routing problem. Q Appl Math 16(1):87–90

    Article  MathSciNet  MATH  Google Scholar 

  15. Abousleiman R, Rawashdeh O (2014) Energy-efficient routing for electric vehicles using metaheuristic optimization frameworks. 17th IEEE mediterranean electrotechnical conference, pp 298–304

    Google Scholar 

  16. HERE Maps. [Online]. Available: https://company.here.com/here/

  17. Guzzella L, Sciarretta A (2013) Vehicle propulsion systems. Springer, Berlin

    Book  Google Scholar 

  18. Hayes JG, Davis K (2014) Simplified electric vehicle powertrain model for range and energy consumption based on EPA coast-down parameters and test validation by argonne national lab data on the nissan leaf. IEEE transportation electrification conference, pp 1–6

    Google Scholar 

  19. Yen JY (1970) An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Q Appl Math 27:526–530

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 653288—OPTEMUS.

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Correspondence to Giovanni De Nunzio .

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De Nunzio, G., Thibault, L., Sciarretta, A. (2017). Model-Based Eco-Routing Strategy for Electric Vehicles in Large Urban Networks. In: Watzenig, D., Brandstätter, B. (eds) Comprehensive Energy Management – Eco Routing & Velocity Profiles. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-53165-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-53165-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53164-9

  • Online ISBN: 978-3-319-53165-6

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