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Development of eco-routing guidance for connected electric vehicles in urban traffic systems

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  • Chen, Jie
  • Hu, Maobin
  • Shi, Congling

Abstract

Despite extensive work on energy consumption of vehicles, the economic performance of vehicles in urban road networks urgently requires a route guidance strategy, which can optimize travel cost along the path. In this work, a set of cost minimization path was newly developed based on the operation information of connected electric vehicles including both energy consumption and travel time. At first, the energy consumption for vehicles was estimated using Comprehensive Power-based Energy consumption Model (CPEM). Then the real-time energy consumption and travel time of vehicles on each road were collected and sent to other vehicles to calculate the travel path. The traffic flow in city networks was investigated using cellular automaton simulation. Compared to previous static shortest path and dynamic quickest path, both electricity consumption and travel time can be reduced by adopting new path. The maximum energy and travel cost saving can achieve ≈4% in a wide range of traffic density and various networks. Combined with tolling scheme, the cost minimization paths can further improve traffic efficiency. With the rapid development of intelligent transportation system (ITS) technology, the cost minimization paths can be used to provide eco-driving route-choice suggestion for drivers.

Suggested Citation

  • Chen, Jie & Hu, Maobin & Shi, Congling, 2023. "Development of eco-routing guidance for connected electric vehicles in urban traffic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    • Handle: RePEc:eee:phsmap:v:618:y:2023:i:c:s037843712300273x
    DOI: 10.1016/j.physa.2023.128718
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    Cited by:

    1. Zhang, Wanning & Chen, Bokui & Liang, Xiaodan, 2024. "Neural Network based Route Guidance Strategy in intelligent transportation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 647(C).

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