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

Jianjun Cai, Yonggang Liu, Yuanjian Zhang, Shiquan Shen, Zhenzhen Lei and Zheng Chen

Energy, 2025, vol. 325, issue C

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.

Keywords: Eco-driving; Mixed vehicles; Multi-vehicle cooperative optimization; Model predictive control (MPC); Signalized intersections (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:325:y:2025:i:c:s036054422501816x

DOI: 10.1016/j.energy.2025.136174

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