考虑进站策略的网联电动公交车节能驾驶优化研究

doi:10.16097/j.cnki.1009-6744.2025.02.008

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (2): 82-94.DOI: 10.16097/j.cnki.1009-6744.2025.02.008

• 智能交通系统与信息技术 • 上一篇下一篇

考虑进站策略的网联电动公交车节能驾驶优化研究

南斯睿¹，于谦²，李铁柱^*3，尚赞娣⁴，陈海波⁵

1. 西安建筑科技大学，城市发展与现代交通学院，西安710055；2.长安大学，运输工程学院，西安710055； 3.东南大学，交通学院，南京211102；4.交通运输部科学研究院，北京100029；5.利兹大学，交通研究所，利兹LS29JT，英国

收稿日期:2024-11-08 修回日期:2025-01-12 接受日期:2025-01-20 出版日期:2025-04-25 发布日期:2025-04-19
作者简介:南斯睿(1994—)，女，陕西西安人，助理教授，博士。
基金资助:
陕西省教育厅科学研究计划项目 (24JK0520)；西安建筑科技大学新型城镇化专项研究基金项目(2024SCZH36)；综合交通运输大数据应用技术交通运输行业重点实验室开放课题(2024B1204)。

Energy-saving Driving Optimization for Connected Electric Buses Considering Station Arrival Strategies

NAN Sirui¹,YU Qian²,LI Tiezhu^*3,SHANG Zandi⁴,CHEN Haibo⁵

1. College of Urban Development and Modern Transportation, Xi'an University of Architecture and Technology, Xi'an 710055, China; 2. School of Transportation Engineering, Chang'an University, Xi'an 710055, China; 3. School of Transportation, Southeast University, Nanjing 211102, China; 4. China Academy of Transportation Sciences, Beijing 100029, China; 5. Institute of Transport Studies, University of Leeds, Leeds LS2 9JT, UK

Received:2024-11-08 Revised:2025-01-12 Accepted:2025-01-20 Online:2025-04-25 Published:2025-04-19
Supported by:
Scientific Research Program Funded by Education Department of Shaanxi Provincial Government (24JK0520)；The New Urbanization Found of XAUAT (2024SCZH36)；Open Foundation of Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport (2024B1204)。

摘要/Abstract

摘要： 针对公交车在进出站和信号交叉口高能耗的问题，本文提出一种考虑进站策略的节能驾驶优化方法。首先，基于利用城市交通能力仿真(SimulationofUrbanMobility,SUMO)平台搭建智能网联场景，构建能够反映能耗、行驶效率和安全性的强化学习复合奖励函数；其次，将进站策略和预设交通规则作为约束集成于柔性演员-评论家(SoftActor-Critic, SAC)深度强化学习框架中，优化车辆进出站及接近信号交叉口的轨迹；最后，以实际行驶、基于深度Q网络(DeepQ-Network,DQN)算法常规、基于SAC算法、基于规则约束和DQN算法(DQN-ruled)的优化方法作为基准方案，与本文提出的基于规则约束和SAC算法(SAC-ruled)的优化方法进行对比。结果表明：通过SAC-ruled算法优化后的驾驶轨迹在多种场景下均优于基准方案。在跟驰运动中，与基准方案相比，所设计的节能驾驶优化方法较基准方案的车辆能耗最高减少35.97%，行驶时间提升21.67%；在换道运动中，车辆能耗最多可降低41.40%，行驶时间提升16.94%。此外，通过敏感性分析验证，本文提出的基于SAC-ruled算法的节能驾驶优化方法在应对车流量波动方面表现出更强的适应性。本文建立的节能驾驶优化模型可集成节能辅助驾驶系统，鼓励驾驶员主动节能。

关键词: 智能交通, 节能驾驶优化, 深度强化学习, 纯电动公交, 柔性演员-评论家算法

Abstract: Considering the high energy consumption of bus operation especially when stopping at bus stations and signalized intersections, this paper proposes an energy-saving driving optimization method based on stop approach strategies. The SUMO platform is used to build intelligent connected vehicle simulation scenarios. A composite reward function is developed, and the driving efficiency, safety, and energy consumption are factored in. Stop arrival strategies and predefined traffic rules are incorporated as constraints into the Soft Actor-Critic (SAC) deep reinforcement learning framework to optimize vehicle trajectories when bus stops at the stations and approaches signalized intersections. The proposed SAC-ruled algorithm is tested under different scenarios, using real-world driving data and the conventional SAC-based optimization method as baseline methods. Results show that the proposed energy-saving driving optimization method shows a 35.97% reduction in vehicle energy consumption and a 21.67% improvement in travel time compared to the baseline methods. In lane-changing scenarios, energy consumption is reduced by up to 41.40%, with a 16.94% improvement in travel time. The proposed method demonstrates great adaptability to traffic flow fluctuations, as validated by sensitivity analysis. This method can be integrated into energy-saving assistance systems, encouraging drivers to adopt energy-saving behaviors.

Key words: intelligent transportation, energy-saving driving optimization, deep reinforcement learning, electric bus, soft actor critic algorithm

中图分类号:

U495

南斯睿, 于谦, 李铁柱, 尚赞娣, 陈海波. 考虑进站策略的网联电动公交车节能驾驶优化研究[J]. 交通运输系统工程与信息, 2025, 25(2): 82-94.

NAN Sirui, YU Qian, LI Tiezhu, SHANG Zandi, CHEN Haibo. Energy-saving Driving Optimization for Connected Electric Buses Considering Station Arrival Strategies[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 82-94.

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http://www.tseit.org.cn/CN/Y2025/V25/I2/82

参考文献

[1]汪光焘,王婷.贯彻《交通强国建设纲要》推进城市交通高质量发展[J]. 城市规划, 2020, 44(3): 31-42. [WANG G T, WANG T. Implementing the outline for building a leading transportation nation to promote high quality development of urban transportation[J]. Urban Planning, 2020, 44(3): 31-42.]

[2] NAN S, TU R, LI T, et al. From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus[J]. Energy, 2022, 261: 125188.

[3] ZHANG Y, YUAN W, FU R, et al. Design of an energy saving driving strategy for electric buses[J]. IEEE Access, 2019, 7: 157693-157706.

[4]张雅丽,袁伟,付锐,等.纯电动公交车进出站节能驾驶策略的设计与仿真[J].交通运输系统工程与信息, 2021, 21(4): 106-117. [ZHANG Y L, YUAN W, FU R, et al. Design and simulation of energy saving driving strategy for pure electric bus entering and leaving stops [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21: 106-117.]

[5]ZHANG Y, FU R, GUO Y, et al. Environmental screening model of driving behavior for an electric bus entering and leaving stops[J]. Transportation Research Part D: Transport and Environment, 2022, 112: 103464.

[6] 庄伟超,丁昊楠,董昊轩,等.信号交叉口网联电动汽车自适应学习生态驾驶策略[J].吉林大学学报(工学版), 2023, 53(1): 82-93. [ZHUANG W C, DING H N, DONG HX, et al. Learning based eco-driving strategy of connected electric vehicle at signalized intersection[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 82-93.]

[7] YANG L, HU Z, WANG L, et al. Entire route eco-driving method for electric bus based on rule-based reinforcement learning[J]. Transportation Research Part E: Logistics and Transportation Review, 2024, 189: 103636.

[8] JO K, JO Y, SUHR J K, et al. Precise localization of an autonomous car based on probabilistic noise models of road surface marker features using multiple cameras[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3377-3392.

[9] 李铁柱,谢炳言,刘天昊,等.基于规则的信号交叉口纯电动公交节能驾驶策略[J].交通运输系统工程与信息, 2024, 24(4): 139-150. [LI T Z, XIE B Y, LIU T H, et al. A Ruled-based energy-saving driving strategy for battery electric bus at signal intersection[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(4): 139-150.]

[10] JIN K, LI X, WANG W, et al. Energy-optimal speed control for connected electric buses considering passenger load[J]. Journal of Cleaner Production, 2023, 385: 135773.

[11] SHAN X, WAN C, HAO P, et al. Connected eco-driving for electric buses along signalized arterials with bus stops [J]. IET Intelligent Transport Systems, 2023, 17(3): 579 591.

[12] ZHANG Y, FU R, GUO Y, et al. Eco-driving strategy for connected electric buses at the signalized intersection with a station[J]. Transportation Research Part D: Transport and Environment, 2024, 128: 104076.

[13] NAN S, LIAO F, LI T, et al. Identifying the electricity saving driving behaviors of electric bus based on trip level electricity consumption: A machine learning approach[J]. Environmental Science and Pollution Research, 2023, 30(34): 82743-82759.

[14] GUO Q, ANGAH O, LIU Z, et al. Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors[J]. Transportation Research Part C: Emerging Technologies, 2021, 124: 102980.

[15] 辛琪, 王嘉琪,杨文科,等.信控路段混行交通生态驾驶深度强化学习模型[J].交通运输系统工程与信息, 2024, 24(3): 127-139. [XIN Q, WANG J Q, YANG W K, et al. Eco-driving under mixed autonomy at signalized intersection: A deep reinforcement learning model[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 127-139.]

考虑进站策略的网联电动公交车节能驾驶优化研究

Energy-saving Driving Optimization for Connected Electric Buses Considering Station Arrival Strategies

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