Browsing by Author "Zhang, Yuanjian"

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    Cooperative ecological adaptive cruise control for plug-in hybrid electric vehicle based on approximate dynamic programming
    (IEEE, 2022-10-26) Li, Jie; Liu, Yonggang; Fotouhi, Abbas; Wang, Xiangyu; Chen, Zheng; Zhang, Yuanjian; Li, Liang
    Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. Additionally, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.
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    Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties
    (Elsevier, 2023-06-19) Li, Jie; Fotouhi, Abbas; Pan, Wenjun; Liu, Yonggang; Zhang, Yuanjian; Chen, Zheng
    Eco-driving control poses great energy-saving potential at multiple signalized intersection scenarios. However, traffic uncertainties can often lead to errors in ecological velocity planning and result in increased energy consumption. This study proposes an eco-driving approach with a hierarchical framework to be leveraged at signalized intersections that considers the impact of traffic uncertainty. The proposed approach leverages a queue-based traffic model in the upper level to estimate the impact of traffic uncertainty and generate dynamic modified traffic light information. In the lower level, a deep reinforcement learning-based controller is constructed to optimize velocity subject to the constraints from the traffic lights and traffic uncertainty, thereby reducing energy consumption while ensuring driving safety. The effectiveness of the proposed control strategy is demonstrated through numerous simulation case studies. The simulation results show that the proposed method significantly improves energy economy and prevents unnecessary idling in uncertain traffic scenarios, as compared to other approaches that ignore traffic uncertainty. Furthermore, the proposed method is adaptable to different traffic scenarios and showcases energy efficiency.
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    Review on eco-driving control for connected and automated vehicles
    (Elsevier, 2023-11-11) Li, Jie; Fotouhi, Abbas; Liu, Yonggang; Zhang, Yuanjian; Chen, Zheng
    With the development of communication and automation technologies, the great energy-saving potential of connected and automated vehicles (CAVs) has gradually been highlighted. By means of interactions with surrounding vehicles and infrastructure, CAVs can automatically plan ecological driving behaviours to significantly reduce energy consumption, which is normally defined as eco-driving. Currently, eco-driving is recognised as an effective method to improve the energy economy of individual CAVs and promote the overall energy economy of transportation without requiring significant hardware investment. After reviewing the scattered eco-driving literature, this study systematically summarizes the state-of-the-art in this field for promoting its future development. The basic principles of eco-driving and energy management systems are firstly discussed to figure out the relationship between eco-driving and powertrain control. Then, related eco-driving studies are classified into three categories according to their applications in terms of single-vehicle scenario, car-following operation, and multi-vehicle co-operation. The key characteristics of various eco-driving studies are in-depth addressed, and the energy-saving potential for cooperative eco-driving is emphasized. Finally, the potential development trends are provided, thereby contributing to the development of eco-driving techniques.