Urban electric bus operation management: Review and outlook

doi:10.3969/j.issn.1674-8484.2022.03.001

Abstract

Abstract:

With the accelerating electrification of public transit systems, the number of electric buses (EBs) in China has increased substantially. The operation management of EBs is of great significance for reducing the carbon emissions of the public transit system. Herein, we firstly introduce the development history of EBs in China and Western countries, and summarize the existing issues in the EB operation management in practice. Then a framework of EB operation management is proposed, and current studies are summarized from the perspectives of energy consumption estimation, vehicle scheduling, energy-efficient driving, resource allocation and benefit evaluation. We point out the techniques and methods down the road on EB operation management. Finally, the challenges are systematically summarized. We conclude that the future works that need to be focused on include: electric bus network planning, battery life estimation based on actual data, intelligent scheduling, and adoption of new power supply and charging modes.

Key words: electric bus, operation management, energy consumption estimation, vehicle scheduling, resource allocation, benefit evaluation

CLC Number:

U491.2

QU Xiaobo, LIU Yajun, CHEN Yuwei, BIE Yiming. Urban electric bus operation management: Review and outlook[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 407-420.

Figures/Tables 6

References 117

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年份	电动公交车数量万辆	公共汽电车总数量万辆	电动化比例 %
2016	9.49	60.86	15.6
2017	17.13	65.12	26.3
2018	25.45	67.34	37.8
2019	32.45	69.33	46.8
2020	37.90	70.44	53.8
2021	41.95	70.94	59.1

年份	电动公交车数量万辆	公共汽电车总数量万辆	电动化比例 %
2016	9.49	60.86	15.6
2017	17.13	65.12	26.3
2018	25.45	67.34	37.8
2019	32.45	69.33	46.8
2020	37.90	70.44	53.8
2021	41.95	70.94	59.1

类别	理论基础	优点	缺点
经验法	无	方法简单	估计精度较差
动力学方法	纵向动力学、比功率模型	估计精度中等;较好地反映车辆运动状态;适用于车辆运行控制	数据采集困难;忽略环境温度对电池热损失和车载辅助系统的影响
数据驱动方法	多元回归	估计精度中等;可量化各个影响因素的影响程度;外推性好	多采用线性形式,结构简单
数据驱动方法	深度学习	估计精度较高;不需要预先明确数据之间的物理关系	对训练数据样本量要求高

类别	理论基础	优点	缺点
经验法	无	方法简单	估计精度较差
动力学方法	纵向动力学、比功率模型	估计精度中等;较好地反映车辆运动状态;适用于车辆运行控制	数据采集困难;忽略环境温度对电池热损失和车载辅助系统的影响
数据驱动方法	多元回归	估计精度中等;可量化各个影响因素的影响程度;外推性好	多采用线性形式,结构简单
数据驱动方法	深度学习	估计精度较高;不需要预先明确数据之间的物理关系	对训练数据样本量要求高