A model-based eco-routing strategy for electric vehicles in large urban networks

A novel eco-routing navigation strategy and energy consumption modeling approach for electric vehicles are presented in this work. Speed fluctuations and road network infrastructure have a large impact on vehicular energy consumption. Neglecting these effects may lead to large errors in eco-routing navigation, which could trivially select the route with the lowest average speed. We propose an energy consumption model that considers both accelerations and impact of the road infrastructure, also separating the costs of all the possible turning movements in the transportation network by means of the adjoint graph. The approach makes use of only aggregated data available from existing map services. We demonstrate that the proposed strategy is more effective and reliable than the existing approaches in predicting vehicle energy consumption and in suggesting an energy-efficient route.

[1]  M. Richter,et al.  Comparison of eco and time efficient routing of ICEVs, BEVs and PHEVs in inner city traffic , 2012, 2012 IEEE Vehicle Power and Propulsion Conference.

[2]  Hesham Rakha,et al.  The effects of route choice decisions on vehicle energy consumption and emissions , 2008 .

[3]  Richard Bellman,et al.  ON A ROUTING PROBLEM , 1958 .

[4]  Rami Abousleiman,et al.  Energy-efficient routing for electric vehicles using metaheuristic optimization frameworks , 2014, MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference.

[5]  Yuanyuan Song,et al.  Study on Eco-Route Planning Algorithm and Environmental Impact Assessment , 2013, J. Intell. Transp. Syst..

[6]  Karin Brundell-Freij,et al.  Optimizing route choice for lowest fuel consumption - Potential effects of a new driver support tool , 2006 .

[7]  Silviu-Iulian Niculescu,et al.  Energy Optimal Real-Time Navigation System , 2014, IEEE Intelligent Transportation Systems Magazine.

[8]  Christian S. Jensen,et al.  EcoSky: Reducing vehicular environmental impact through eco-routing , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[9]  Hesham Rakha,et al.  Network-wide impacts of eco-routing strategies: A large-scale case study , 2013 .

[10]  W. Marsden I and J , 2012 .

[11]  Bart van Arem,et al.  Eco-routing: Comparing the fuel consumption of different routes between an origin and destination using field test speed profiles and synthetic speed profiles , 2011, 2011 IEEE Forum on Integrated and Sustainable Transportation Systems.

[12]  Christian S. Jensen,et al.  EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data , 2014, GeoInformatica.

[13]  Matthew J. Barth,et al.  Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information , 2012, IEEE Transactions on Intelligent Transportation Systems.

[14]  Silviu-Iulian Niculescu,et al.  Performance of current eco-routing methods , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[15]  Lino Guzzella,et al.  Vehicle Propulsion Systems , 2013 .

[16]  Yu Nie,et al.  An Ecorouting Model Considering Microscopic Vehicle Operating Conditions , 2013 .

[17]  Antonio Sciarretta,et al.  Optimal Ecodriving Control: Energy-Efficient Driving of Road Vehicles as an Optimal Control Problem , 2015, IEEE Control Systems.