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Hierarchical cooperative eco-driving optimization for multidimensional mixed vehicles at signalized intersections

Author

Listed:
  • Cai, Jianjun
  • Liu, Yonggang
  • Zhang, Yuanjian
  • Shen, Shiquan
  • Lei, Zhenzhen
  • Chen, Zheng

Abstract

Electric vehicles (EVs) and connected and automated vehicles (CAVs) are increasingly developed, while traditional fuel vehicles (FVs) and human-driven vehicles (HDVs) still dominate the market. Therefore, future road traffic will consist mainly of multidimensional mixed vehicles, including EVs-FVs and CAVs-HDVs. To improve their overall energy efficiency at signalized intersections, this paper proposes a hierarchical cooperative eco-driving optimization method based on the control of “1+n” mixed vehicle platoon, which consists of one leading CAV and n following HDVs and includes both EVs and FVs. Firstly, based on the energy consumption characteristics of EVs and FVs, a speed planning algorithm considering traffic uncertainties is designed in the upper layer to efficiently determine the reference speed trajectory for the leading CAV. Furthermore, in the lower layer, a model predictive control-based eco-driving strategy is proposed to optimize the platoon's overall energy efficiency by controlling the leading CAV to follow the reference trajectory. Simulation and hardware-in-loop experiment results validate the effectiveness and feasibility of this approach, reducing average energy consumption by 3.48 % for EVs and 8.23 % for FVs compared to a non-optimized model. Additionally, the algorithm's sensitivity to varying vehicle penetration rates and its robustness to HDV lane-changing behavior are assessed.

Suggested Citation

  • Cai, Jianjun & Liu, Yonggang & Zhang, Yuanjian & Shen, Shiquan & Lei, Zhenzhen & Chen, Zheng, 2025. "Hierarchical cooperative eco-driving optimization for multidimensional mixed vehicles at signalized intersections," Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:energy:v:325:y:2025:i:c:s036054422501816x
    DOI: 10.1016/j.energy.2025.136174
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