Collaborative method of bus trajectory control and station charging considering signal control

doi:10.3785/j.issn.1008-973X.2025.08.023

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (8): 1755-1766 DOI: 10.3785/j.issn.1008-973X.2025.08.023

Collaborative method of bus trajectory control and station charging considering signal control

Chengcheng YANG1,2(

),Kun GAO2,Sheng JIN1,3,*(

),Congcong BAI1,Donglei RONG1,4,Xi GAO1,Xinyi SHEN1

1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg 41296, Sweden
3. Zhejiang University Zhongyuan Institute, Zhengzhou 450000, China
4. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China

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Abstract

A collaborative method of trajectory control and station charging for connected electric buses (CEBs) considering arterial signal coordination was proposed to address the mileage anxiety problem existing in the operation process of electric buses. For regular vehicles (RVs) and CEBs running on the road, the mixed integer linear programming method was used to construct an arterial signal coordination control model considering the operation efficiency of RVs and the constraints of non-stop at intersections for CEBs. On this basis, a rule-based approach was used to construct a collaborative optimization model of bus trajectory control and station charging by considering the passenger demand, the bus headway maintenance, bus bunching, the non-stop constraints at intersections, the speed boundary and the charging duration boundary of CEBs. This model can enable CEBs to flexibly adjust the stop time for wireless static charging while maintaining good operational efficiency and order. Simulation experimental results based on real-world scenarios showed that the proposed model could increase the static wireless charging duration of CEBs by 85% without significantly affecting the overall operational efficiency of CEBs. In addition, through the applicability and sensitivity analysis, some insights wer given for the practical application of the model.

Key words： traffic engineering bus scheduling mixed integer linear programming connected electric bus arterial signal coordination static wireless charging

Received: 23 November 2024 Published: 28 July 2025

CLC:

U 491

Fund: 国家自然科学基金资助项目（72361137006）；国家留学基金管理委员会资助项目；浙江省“尖兵”“领雁”研发攻关计划资助项目（2022C01042）.

Corresponding Authors: Sheng JIN E-mail: 12112120@zju.edu.cn;jinsheng@zju.edu.cn

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Cite this article:

Chengcheng YANG,Kun GAO,Sheng JIN,Congcong BAI,Donglei RONG,Xi GAO,Xinyi SHEN. Collaborative method of bus trajectory control and station charging considering signal control. Journal of ZheJiang University (Engineering Science), 2025, 59(8): 1755-1766.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.08.023 OR https://www.zjujournals.com/eng/Y2025/V59/I8/1755

考虑信号控制的公交轨迹控制与站点充电协同方法

针对电动公交运行过程中存在的里程焦虑问题，提出考虑干线信号协调的网联电动公交（CEBs）轨迹控制与站点充电协同方法. 针对道路中的常规车辆（RVs）和CEBs，利用混合整数线性规划方法，构建考虑RVs运行效率与CEBs的交叉口不停车约束条件的干线信号协调控制模型；考虑乘客需求、车头时距保持规则、串车现象、交叉口不停车约束、速度边界及充电时长边界，基于规则方法构建轨迹控制与站点充电协同优化模型，使CEBs在保持良好运行效率与秩序的同时，能够灵活调整停站时长以进行静态无线充电. 基于实际场景的仿真实验结果表明，模型可以在几乎不影响CEBs总体运行效率的情况下，增加85%的静态无线充电时长. 通过适用性与敏感性分析，为模型的实际应用提供了建议.

关键词： 交通工程, 公交调度, 混合整数线性规划, 网联电动公交, 干线信号协调, 静态无线充电

Fig.1 Space-time diagram and phase sequence mode of RVs

Fig.2 Relationship between arrival interval of CEBs and green-phase interval

Fig.3 CEBs trajectory control logic at first stage for each operational section

Fig.4 CEBs trajectory control logic at third stage for each operational section

Fig.5 Simulation experimental environment

Fig.6 CEBs trajectories under STC and ST strategies

Fig.7 Charging time and travel time of CEBs under STC and ST strategies

Fig.8 Changes in operation indicators under different traffic flows of CEBs

Tab.1 Dwell time of CEBs under different passenger demand scenarios

Fig.9 Changes in increased charging time under different passenger demand scenarios and charging demand coefficients

Fig.10 Average charging time with different values of charging demand coefficient

Fig.11 CEBs operation time under different values of charging demand coefficients

Fig.12 Changes in CEBs operation indicators under different charging demand coefficients

Fig.13 Speed distribution of CEBs with charging demand coefficients of 0.2, 0.4, 0.6 and 0.8


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