Evaluation Model for Eco-Driving Performance of Pure Electric Bus Drivers

doi:10.12141/j.issn.1000-565X.240470

Abstract

Abstract:

Improving driver behavior is an important way to reduce vehicle energy consumption. Currently, many scholars have conducted extensive research on eco-driving behavior and proposed various eco-driving suggestions. However, there is a lack of evaluation methods specifically aimed at assessing the driving performance of pure electric bus drivers. In order to reduce the operating costs and energy consumption of electric buses, this study co-llected naturalistic driving data from electric buses. Firstly, the original data was pre-processed with unified sampling frequency, data cleaning, and parameter supplementation. Secondly, by selecting driver behavior characteristic parameters and vehicle operation parameters, the impact of bus driver driving behavior on energy consumption was analyzed. Based on the identified behavioral parameters that influence energy use, and taking each trip from the departure station to the terminal station as a unit, seven energy-related driving events were proposed: average start-up acceleration time, number of rapid accelerator pedal presses, duration of sustained high pedal opening, number of sudden accelerations, braking proportion during deceleration, duration of low-speed driving, and duration of economic speed driving. Afterwards, a multiple regression model for eco-driving level evaluation was established by analyzing the Pearson correlation coefficients between various driving event parameters and energy consumption per 100 kilometers, and the driving level of the driver during the journey was scored. Finally, based on the evaluation model, an eco-driving assistance feedback platform was built to help fleet managers better understand the eco-driving level of drivers. The results show that the proposed eco-driving level evaluation model for electric buses based on driving events has an accuracy of 93.52% and an average error of 6.48% in evaluating the eco-driving behavior. The model has a good effect on calculating eco-driving scores. The eco-driving assistance feedback platform can help fleet managers understand the operation status of buses and the eco-driving level of drivers, and help drivers understand their own driving situation.

Key words: pure electric bus, eco-driving, driving behavior, evaluation model

CLC Number:

ZHANG Yali, SHEN Yubo, YUAN Wei, ZHANG Kang, ZHANG Huiming. Evaluation Model for Eco-Driving Performance of Pure Electric Bus Drivers[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(8): 29-41.

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References 26

[1]	MINTSIS E， VLAHOGIANNI E I， MITSAKIS E. Dynamic eco-driving near signalized intersections：syste-matic review and future research directions［J］．Journal of Transportation Engineering，Part A：Systems，2020，146（4）：04020018/1-15.
[2]	BERRY I M ．The effects of driving style and vehicle performance on the real-world fuel consumption of US light-duty vehicles［D］．Cambridge：Massachusetts Institute of Technology，2010.
[3]	HOLDSTOCK T， SORNIOTTI A， EVERITT M，et al ．Energy consumption analysis of a novel four-speed dual motor drivetrain for electric vehicles［C］‍∥Proceeding of the 2012 IEEE Vehicle Power and Propulsion Confe-rence．Seoul：IEEE，2012：295-300.
[4]	KAMBLY K， BRADLEY T H ．Geographical and temporal differences in electric vehicle range due to cabin conditioning energy consumption［J］．Journal of Power Sources，2015，275：468-475.
[5]	AL-WREIKAT Y， SERRANO C， SODRE J R ．Effects of ambient temperature and trip characteristics on the e-nergy consumption of an electric vehicle［J］．Energy，2022，238：122028/1-9.
[6]	LAJUNEN A ．Energy consumption and cost-benefit analysis of hybrid and electric city buses［J］．Transportation Research Part C：Emerging Technologies，2014，38：1-15.
[7]	GONDER J， EARLEYWINE M， SPARKS W ．Analy-zing vehicle fuel saving opportunities through intelligent driver feedback［J］．SAE International Journal of Pa-ssenger Cars-Electronic and Electrical Systems，2012，5（2）：450-461.
[8]	ZHANG Y， FU R， GUO Y，et al ．Eco-driving stra-tegy for connected electric buses at the signalized intersection with a station［J］．Transportation Research Part D：Transport and Environment，2024，128：104076/1-21.
[9]	WANG J， RAKHA H A ．Fuel consumption model for conventional diesel buses［J］．Applied Energy，2016，170：394-402.
[10]	QIU C， WANG G ．New evaluation methodology of regenerative braking contribution to energy efficiency improvement of electric vehicles［J］．Energy Conversion and Management，2016，119：389-398.
[11]	张红妮．基于驾驶行为的纯电动公交车能耗规律与节能对策［D］．西安：长安大学，2020.
[12]	郭淼，赵晓华，姚莹，等．基于驾驶行为和交通运行状态的事故风险研究［J］．华南理工大学学报（自然科学版），2022，50（9）：29-38.
	GUO Miao， ZHAO Xiaohua， YAO Ying，et al ．Study on accident risk based on driving behavior and traffic operating status［J］．Journal of South China University of Technology （Natural Science Edition），2022，50（9）：29-38.
[13]	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，2022，112：103464/1-17.
[14]	ULLAH I， LIU K， YAMAMOTO T，et al ．A comparative performance of machine learning algorithm to predict electric vehicles energy consumption：a path towards sustainability［J］．Energy & Environment，2022，33（8）：1583-1612.
[15]	ABDELATY H， AL-OBAIDI A， MOHAMED M，et al ．Machine learning prediction models for battery-electric bus energy consumption in transit［J］．Transportation Research Part D：Transport and Environment，2021，96：102868/1-27.
[16]	LEE C H， WU C H ．A novel big data modeling method for improving driving range estimation of EVs［J］．IEEE Access，2015，3：1980-1993.
[17]	程颖，张佳乐，张少君，等．大型货运车辆生态驾驶及节油潜力评估［J］．交通运输系统工程与信息，2020，20（6）：253-258.
	CHENG Ying， ZHANG Jiale， ZHANG Shaojun，et al ．Evaluation of eco-dring behavior and fuel-saving potential of large freight vehicle［J］．Journal of Transportation Systems Engineering an Information Technology，2020，20（6）：253-258.
[18]	朱江苏，王继磊，吕文芝，等．基于多元回归的司机驾驶行为对油耗的影响［J］．山东交通学院学报，2020，28（3）：8-13.
	ZHU Jiangsu， WANG Jilei， Wenzhi LÜ，et al ．E-ffect of driver’‍s driving behavior on fuel consumption based on multiple regression［J］．Journal of Shandong Jiaotong University，2020，28（3）：8-13.
[19]	吴海东，臧力卓，肖百卉，等．基于新能源公交大数据的驾驶行为经济性评估［J］．中国公路学报，2022，35（3）：177-190.
	WU Haidong， ZANG Lizhuo， XIAO Baihui，et al ．Evaluation of driving behavior economy based on big data of new energy bus［J］．China Journal of Highway Transportation，2022，35（3）：8-13.
[20]	陈晨．城市道路驾驶员生态驾驶行为评估方法研究［D］．北京：北京工业大学，2017.
[21]	涂昊然．基于驾驶行为的纯电动汽车能耗分析方法研究［D］．北京：北京理工大学，2021.
[22]	张雅丽，沈钰博，王伊．纯电动公交车驾驶行为对能耗的影响研究［J］．交通工程，2024，24（7）：1-7，16.
	ZHANG Yali， SHEN Yubo， WANG Yi ．Research on the impact of driving behavior on energy consumption of electric buses［J］．Journal of Transportation Enginee-ring，2024，24（7）：1-7，16.
[23]	付锐，张雅丽，袁伟．生态驾驶研究现状及展望［J］．中国公路学报，2019，32（3）：1-12.
	FU Rui， ZHANG Yali， YUAN Wei ．Progress and prospect in research on eco-driving［J］．China Journal of Highway Transportation，2019，32（3）：1-12.
[24]	严利鑫，贾乐，刘清梅，等．基于知识图谱的生态驾驶行为研究现状及热点分析［J］．公路交通科技，2022，39（4）：150-159.
	YAN Lixin， JIA Le， LIU Qingmei，et al ．Analysis on study status and hotspots of eco-driving behavior based on knowledge graph［J］．Journal of Highway and Transportation Research and Development，2022，39（4）：150-159.
[25]	BEUSEN B， BROEKX S， DENYS T，et al ．Using on-board logging devices to study the longer-term impact of an eco-driving course［J］．Transportation Research Part D：Transport and Environment，2009，14（7）：514-520.
[26]	GONDER J， EARLEYWINE M， SPARKS W ．Final report on the fuel saving effectiveness of various driver feedback approaches［R］．National Rene-wable Energy Lab．NREL）. Golden，Colorado：［s.n.］，2011.

编号	参数		参数
1	记录日期	11	挡位
2	车牌号	12	电子刹车
3	线路编号	13	手刹
4	上下行	14	电机转速
5	上一站点编号	15	电机扭矩
6	下一站点编号	16	总电压
7	进站	17	总电流
8	CAN速度	18	SOC值
9	加速踏板开度	19	CAN里程
10	制动踏板开度

类别	参数	数值
KMO检验	KMO值	0.919
Bartlett球形检验	近似卡方	285.445
	自由度	21
	p	<0.001

成分	旋转前
成分	特征根	方差解释率/%	累积方差解释率/%	特征根	方差解释率/%	累积方差解释率/%
1	5.584	79.774	79.774	346.284	49.469	49.469
2	0.713	10.182	89.955	167.949	23.993	73.462
3	0.319	4.555	94.511	147.342	21.049	94.511
4	0.204	2.916	97.427	—	—	—
5	0.126	1.795	99.222	—	—	—
6	0.043	0.616	99.838	—	—	—
7	0.011	0.162	100.000	—	—	—

参数		数值
参数	成分1	成分2	成分3
平均起步加速时间/s	0.602	-0.020	1.345
猛踩加速踏板次数	0.235	0.010	0.023
减速时制动占比/%	-0.293	0.047	-0.072
持续高踏板位置时长/s	0.637	-0.449	0.290
急加速次数	0.256	-0.011	0.036
低速行驶时长/s	0.298	0.136	0.290
经济车速行驶时长/s	0.522	-1.081	0.075

环境		参数
软件	操作系统	Windows 10 64位
	数据分析工具	Matlab & Pycharm
	开发软件	Matlab AppDesigner
硬件	处理器	Intel（R） Core（TM） i5-9300H CPU @ 2.40 GHz
硬件	内存	DDR4 8G