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Multiobjective eco-driving speed optimisation with real-time traffic: Balancing fuel, NOx, and travel time

Author

Listed:
  • Liu, Enze
  • Lin, Zhiyuan
  • Chen, Haibo
  • Jia, Dongyao
  • Liu, Ye
  • Guo, Junhua
  • Li, Tiezhu
  • Wei, Tangjian

Abstract

Optimising driving velocity profiles is crucial for reducing vehicle fuel consumption and NOx emissions without altering core vehicle components. While many studies have addressed eco-driving, most have focused solely on minimising fuel consumption or have treated NOx emissions separately, resulting in distinct, non-integrated speed profiles, and have often neglected the influence of real-time traffic. To overcome these limitations, this paper introduces a novel Multiobjective Speed Profile Optimisation (MO-SPO) framework for eco-driving that simultaneously minimises fuel consumption, NOx emissions, and travel time while accounting for surrounding traffic. Two solution approaches are developed and compared: a two-phase Model Predictive Control (MPC) method and a newly proposed Deep Reinforcement Learning (DRL) method that directly integrates multiple objectives and real-time traffic constraints into the speed control policy.

Suggested Citation

  • Liu, Enze & Lin, Zhiyuan & Chen, Haibo & Jia, Dongyao & Liu, Ye & Guo, Junhua & Li, Tiezhu & Wei, Tangjian, 2025. "Multiobjective eco-driving speed optimisation with real-time traffic: Balancing fuel, NOx, and travel time," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225014355
    DOI: 10.1016/j.energy.2025.135793
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