Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement

Jie Li, Yonggang Liu, Jun Cheng, Abbas Fotouhi and Zheng Chen

Energy, 2024, vol. 310, issue C

Abstract: Eco-driving control techniques have shown significant potential in reducing energy consumption in urban scenarios. The presence of slow-moving vehicles typically disrupts ecological velocity planning, leading to an increase in energy consumption. To solve it, this study proposes a hierarchical eco-driving control strategy, that integrates speed optimization and lane change decision-making in urban scenarios, to not only ensure traffic efficiency, but also save the energy consumption. Firstly, a data-driven energy model is leveraged in the upper level to estimate the energy consumption of candidate lanes and generate ecological lane change decisions. Then, in the lower level, the preceding vehicles and traffic lights are considered to plan an ecological velocity profile via deep reinforcement learning algorithm after transitions to the target driving lane, thereby enhancing the fuel economy and travel efficiency. A virtual driving environment model is established to verify the proposed method through numerous simulation cases. The results indicate that the proposed method effectively reduces energy consumption while maintaining favorable travel efficiency, compared with conventional benchmarks. Furthermore, the notable improvements are observed particularly in free traffic conditions.

Keywords: Eco-driving; Velocity optimization; Lane change; Deep reinforcement learning; Signalized intersection (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:310:y:2024:i:c:s0360544224030706

DOI: 10.1016/j.energy.2024.133294

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