成都市燃料电池公交车示范运行行驶工况的构建

doi:10.3969/j.issn.1674-8484.2022.01.021

汽车安全与节能学报 ›› 2022, Vol. 13 ›› Issue (1): 202-208.DOI: 10.3969/j.issn.1674-8484.2022.01.021

• 汽车节能与环保 • 上一篇

成都市燃料电池公交车示范运行行驶工况的构建

金思含¹(), 彭忆强¹^,²^,³(), 武小花¹^,²^,³, 韩震¹

1.西华大学汽车与交通学院,成都 610039,中国
2.汽车测控与安全四川省重点实验室,成都 610039,中国
3.四川省新能源汽车智能控制与仿真测试技术工程研究中心,成都 610039,中国

收稿日期:2021-05-24 修回日期:2021-12-23 出版日期:2022-03-31 发布日期:2022-04-02
通讯作者: 彭忆强
作者简介:*彭忆强(1963—),男(汉族),四川,教授。E-mail:yqpeng@mail.xhu.edu.cn。
金思含(1998—),女(汉族),四川,硕士研究生。E-mail:2450256010@qq.com。
基金资助:
四川省科技厅重大科技项目(2019ZDZX0002);四川省区域创新合作项目(2020YFQ0037);四川省重点研发计划项目(2021YFG0071);成都科技项目(2019-RK00-00025-ZF,2019-YF08-00003-GX)

Construction of the driving cycle for fuel cell bus running in Chengdu demonstration area

JIN Sihan¹(), PENG Yiqiang¹^,²^,³(), WU Xiaohua¹^,²^,³, HAN Zhen¹

1. School of Automobile and Transportation, Xihua University, Chengdu 610039, China
2. Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Chengdu 610039, China
3. Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Chengdu 610039, China

Received:2021-05-24 Revised:2021-12-23 Online:2022-03-31 Published:2022-04-02
Contact: PENG Yiqiang

摘要/Abstract

摘要：

为构建成都市燃料电池公交车示范运行行驶工况,基于示范区内燃料电池公交车实际行驶数据,对预处理后的有效运行数据进行运动学片段划分,采用主成分分析法进行数据降维处理;利用肘部法则确定最佳聚类数目,使用K-means++算法对主成分进行聚类;以不同聚类所占比例和距中心点距离确定最优运动学片段。结果表明：构建出的行驶工况能够反映成都市燃料电池公交车示范区内交通行驶特征,且时长为1 577 s;成都市燃料电池公交车示范运行行驶工况与中国城市客车行驶工况在某些特征参数上存在一些差异。此工况的建立为研制燃料电池公交车智能能量管理系统奠定了基础。

关键词: 燃料电池公交车, 城市道路, 行驶工况, 主成分分析, K-means++聚类

Abstract:

In order to construct the driving cycle of the demonstration operation of fuel cell buses in Chengdu, based on the actual driving data of the fuel cell buses in the demonstration area, the pre-processed effective operation data was divided into kinematics segments, and the principal component analysis method was used for data dimensionality reduction processing. The elbow rule was applied to determine the optimal number of clusters, and the K-means++ algorithm was used to cluster the principal components. The optimal kinematics segments were determined based on the proportions of different clusters and the distance from the center point. The results showes that the constructed driving cycle reflects the driving characteristics of the traffic situation in the fuel cell bus demonstration zone in Chengdu and the driving cycle time is 1 577 s; there are some differences in certain characteristic parameters between the driving cycle of fuel cell buses in Chengdu demonstration area and that of Chinese city buses. The above working lays the foundation for the development of an intelligent energy management system for fuel cell buses.

Key words: fuel cell buses, urban roads, driving cycle, principal component analysis, K-means++ clustering

中图分类号:

U491

金思含, 彭忆强, 武小花, 韩震. 成都市燃料电池公交车示范运行行驶工况的构建[J]. 汽车安全与节能学报, 2022, 13(1): 202-208.

JIN Sihan, PENG Yiqiang, WU Xiaohua, HAN Zhen. Construction of the driving cycle for fuel cell bus running in Chengdu demonstration area[J]. Journal of Automotive Safety and Energy, 2022, 13(1): 202-208.

图/表 8

参考文献 21

[1]	科技部. “三纵三横” 持续支持新能源汽车科技创新[J]. 中国质量万里行, 2020(12):76-77.
	China Ministry of Science and Technology. “Three verticals and three horizontals” continue to support the technological innovation of new energy vehicles[J]. China Quality Travel, 2020(12):76-77. (in Chinese)
[2]	武小花, 邹佩佩, 傅家豪, 等. 燃料电池电动汽车动力系统能量管理策略研究进展[J]. 西华大学学报(自然科学版), 2020,39(4):89-96.
	WU Xiaohua, ZOU Peipei, FU Jiahao, et al. Research progress on energy management strategies of fuel cell electric vehicle power systems[J]. J Xihua Univ (Nat Sci Edit), 2020,39(4):89-96. (in Chinese)
[3]	Huertas J I, Diaz J, Cordero D. A new methodology to determine typical driving cycles for the design of vehicles power trains[J]. Int’l J Interactive Desi Manufact, 2018,12(1):319-326.
[4]	Achour H, Olabi A G. Driving cycle developments and their impacts on energy consumption of transportation[J]. J Cleaner Production, 2016,112:1778-1788
[5]	郭家琛, 姜衡, 雷世英, 等. 城市道路汽车行驶工况构建方法[J]. 交通运输工程学报, 2020,20(6):197-209.
	GUO Jiachen, JIANG Heng, LEI Shiying, et al. Vehicle driving cycle construction method of urban roads[J]. J Traf Transport Engi, 2020,20(6):197-209. (in Chinese)
[6]	王国林, 郭新, 张树培, 等. 基于短行程法的轻型乘用车行驶工况构建[J]. 重庆交通大学学报(自然科学版), 2019,38(2):139-143.
	WANG Guolin, GUO Xin, ZHANG Shupei, et al. Driving cycle development of light vehicles based on short trip method[J]. J Chongqing Jiaotong Univ (Nat Sci), 2019,38(2):139-143. (in Chinese)
[7]	郭晓云. 银川市车辆行驶工况构建及仿真研究[D]. 银川:宁夏大学, 2018.
	GUO Xiaoyun. Construction and simulation of vehicle driving conditions in Yinchuan City[D]. Yinchuan: Ningxia University, 2018. (in Chinese)
[8]	JING Zhecheng, WANG Guolin, ZHANG Shupei, et al. Building Tianjin driving cycle based on linear discriminant analysis[J]. Transport Res Part D: Transport Environ, 2017,53:78-87.
[9]	高建平, 任德轩, 郗建国. 基于全局K-means聚类算法的汽车行驶工况构建[J]. 河南理工大学学报 (自然科学版), 2019,38(1):112-118.
	GAO Jianping, REN Dexuan, XI Jianguo. Construction of vehicle driving cycle based on global K-means clustering algorithm[J]. J Henan Polytechnic Univ (Nat Sci), 2019,38(1):112-118. (in Chinese)
[10]	胡志远, 秦艳, 谭丕强, 等. 基于大样本的上海市乘用车行驶工况构建[J]. 同济大学学报 (自然科学版), 2015,43(10):1523-1527.
	HU Zhiyuan, QIN Yan, TAN Peiqiang, et al. Large-sample-based car-driving cycle in Shanghai City[J]. J Tongji Univ (Nat Sci), 2015,43(10):1523-1527. (in Chinese)
[11]	余曼, 赵炜华, 吴玲, 等. 基于K-均值聚类和支持向量机的电动汽车行驶工况研究[J]. 重庆交通大学学报(自然科学版), 2021,40(5):129-139.
	YU Man, ZHAO Weihua, WU Ling, et al. Working condition of electric vehicle based on K-mean clustering and support vector machine[J]. J Chongqing Jiaotong Univ (Nat Sci), 2021,40(5):129-139. (in Chinese)
[12]	黄万友, 程勇, 李闯. 基于车辆能耗状态的济南市道路行驶工况构建[J]. 西南交通大学学报, 2012, 47(6): 989-995+1026.
	HUANG Wangyou, CHEN Yong LI Chuang, Driving cycle construction of city road based on vehicles energy consumption in Jinan[J]. J Southwest Jiaotong Univ, 2012, 47(6): 989-995+1026. (in Chinese)
[13]	李傲伽, 张炳力, 程啸宇, 等. 一种氢燃料电池电电混合城市客车试验循环典型工况研究[J]. 客车技术与研究, 2020,42(6):1-4.
	LI Aojia, ZHANG Bingli, CHENG Xiaoyu, et al. Research on a typical condition of test cycle for hydrogen fuel cell electric-electric hybrid city buses[J]. Bus Coach Tech Res, 2020,42(6):1-4. (in Chinese)
[14]	Ganesh S G, Bharadwai B V, Ronald M C. A time series clustering based approach for construction of real-world drive cycles[J]. Transport Res Part D, 2021,97(9):102896.
[15]	徐婷, 崔世超, 刘明, 等. 高原山区乘用车行驶工况构建方法研究[J]. 公路交通科技, 2021,38(2):117-124.
	XU Ting, CUI Shichao, LIU Ming. Study on construction method of driving cycle of passenger vehicle in plateau mountainous area[J]. J Highway Transport Res Develop, 2021,38(2):117-124. (in Chinese)
[16]	曹骞, 李君, 刘宇. 基于大数据和马尔科夫链的行驶工况构建[J]. 东北大学学报(自然科学版), 2019,40(1):77-81.
	CAO Qian, LI Jun, LIU Yu. Construction of driving cycle based on big data and Markov chain[J]. J Northeastern Univ (Nat Sci), 2019,40(1):77-81. (in Chinese)
[17]	Frederique R, Catherine M, Mundy D. Driving cycle development methods based on Markov chains[J]. Transport Res Record, 2021,2675(3):212-221
[18]	国家市场监督管理总局中国国家标准化管理委员会. 中国汽车行驶工况 (GB/T 38146.2-2019) 第2部分:重型商用车辆[S]. 中国汽车技术研究中心有限公司, 天津, 2019.
	Standardization Administration. Driving conditions of automobiles in China (GB/T 38146.2-2019), Part 2: Heavy commercial vehicles[S]. China Automotive Technology Research Center, 2019. (in Chinese)
[19]	郑英鑫. 数据挖掘中基于肘部法则的聚类分析在中小学生出行路线优化设计的应用[J]. 电子世界, 2017,4(9):146.
	ZHENG Yingxin. Application of cluster analysis based on elbow rule in data mining in optimal design of travel routes for primary and middle school students[J]. Electronics World, 2004, 4(3): 182-184+188. (in Chinese)
[20]	马志雄, 李孟良, 朱西产, 等乘用车实际行驶工况开发方法的研究[J]. 武汉理工大学学报 (信息与管理工程版), 2004, 4(3): 182-184+188.
	MA Zhixiong, LI Mengliang, ZHU Xichan, et al. Study of the methodology for car driving cycle development[J]. J Wuhan Autom Polytechnic Univ, 2004, 4(3): 182-184+188. (in Chinese)
[21]	汪雯琦, 高广阔, 王子鉴, 等. 汽车运行工况的构建方法[J]. 公路交通科技, 2020,37(9):128-138.
	WANG Wenqi, GAO Guangkuo, WANG Zijian, et al. A method for constructing automobile operation condition[J]. J Highway Transport Res Develop, 2020,37(9):128-138. (in Chinese)

序号	Δt s	v_m km·h^-1	v_mr km·h^-1	v_max km·h^-1	a_max m·s^-2	a_min m·s^-2	S_v km·h^-1	S_a m·s^-2	a_am m·s^-2	a_dm m·s^-2	r_a %	r_d %	r_c %	r_i %
1	71	11.23	14.25	23.00	1.06	-1.42	6.30	1.75	1.54	-1.88	0.07	0.05	0.78	0.09
2	46	15.03	19.21	29.50	0.78	-1.22	11.89	3.30	1.79	-3.35	0.35	0.17	0.22	0.24
3	102	26.47	29.67	39.00	1.06	-2.47	14.20	3.94	1.48	-2.82	0.27	0.15	0.14	0.43
4	73	9.03	23.31	34.00	0.83	-1.86	14.42	4.00	1.59	-2.77	0.29	0.16	0.38	0.15
?	?	?	?	?	?	?	?	?	?	?	?	?	?	?
394	130	28.9	30.05	45.50	1.39	-1.36	14.79	4.1	2.29	-2.05	0.30	0.33	0.05	0.31

序号	Δt s	v_m km·h^-1	v_mr km·h^-1	v_max km·h^-1	a_max m·s^-2	a_min m·s^-2	S_v km·h^-1	S_a m·s^-2	a_am m·s^-2	a_dm m·s^-2	r_a %	r_d %	r_c %	r_i %
1	71	11.23	14.25	23.00	1.06	-1.42	6.30	1.75	1.54	-1.88	0.07	0.05	0.78	0.09
2	46	15.03	19.21	29.50	0.78	-1.22	11.89	3.30	1.79	-3.35	0.35	0.17	0.22	0.24
3	102	26.47	29.67	39.00	1.06	-2.47	14.20	3.94	1.48	-2.82	0.27	0.15	0.14	0.43
4	73	9.03	23.31	34.00	0.83	-1.86	14.42	4.00	1.59	-2.77	0.29	0.16	0.38	0.15
?	?	?	?	?	?	?	?	?	?	?	?	?	?	?
394	130	28.9	30.05	45.50	1.39	-1.36	14.79	4.1	2.29	-2.05	0.30	0.33	0.05	0.31

主成分序号	λi	φ	ψ	主成分序号	λi	φ	ψ
1	7.2544	0.4836	0.4836	8	0.2120	0.0141	0.9844
2	2.5745	0.1716	0.6553	9	0.0985	0.0066	0.9909
3	1.7735	0.1182	0.7735	10	0.0755	0.0050	0.9960
4	1.3544	0.0903	0.8638	11	0.0284	0.0019	0.9979
5	0.7932	0.0529	0.9167	12	0.0249	0.0017	0.9995
6	0.5341	0.0356	0.9523	13	0.0068	0.0005	1.0000
7	0.2696	0.0180	0.9702	14	0.0002	0.0000	1.0000

主成分序号	λi	φ	ψ	主成分序号	λi	φ	ψ
1	7.2544	0.4836	0.4836	8	0.2120	0.0141	0.9844
2	2.5745	0.1716	0.6553	9	0.0985	0.0066	0.9909
3	1.7735	0.1182	0.7735	10	0.0755	0.0050	0.9960
4	1.3544	0.0903	0.8638	11	0.0284	0.0019	0.9979
5	0.7932	0.0529	0.9167	12	0.0249	0.0017	0.9995
6	0.5341	0.0356	0.9523	13	0.0068	0.0005	1.0000
7	0.2696	0.0180	0.9702	14	0.0002	0.0000	1.0000

成分序号	t s	v_m km·h^-1	v_mr km·h^-1	v_max km·h^-1	a_max m·s^-2	a_min m·s^-2	S_v km·h^-1	S_a m·s^-2	a_am m·s^-2	a_dm m·s^-2	r_a %	r_d %	r_c %	r_i %
1	-0.026	0.132	0.131	0.131	0.065	-0.077	0.122	0.122	0.056	-0.019	0.057	0.069	-0.112	0.09
2	0.310	0.068	0.069	0.052	-0.100	0.111	-0.028	-0.028	-0.237	0.235	-0.135	-0.128	-0.010	0.194
3	-0.132	0.033	-0.083	-0.078	0.083	0.318	-0.130	-0.130	0.054	0.376	0.120	0.410	0.027	0.027
4	0.094	-0.023	0.045	0.014	0.329	0.105	0.036	0.036	0.454	0.148	-0.575	0.021	0.003	0.003

成都市燃料电池公交车示范运行行驶工况的构建

Construction of the driving cycle for fuel cell bus running in Chengdu demonstration area

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 21

相关文章 5

编辑推荐

Metrics

本文评价

期刊信息

在线期刊

作者中心

审稿中心

联系我们

	r_a %	r_d %	r_c %	r_i %	v_m km·h^-1	v_mr km·h^-1	a_max m·s^-2	a_min m·s^-2	a_am m·s^-2	a_dm m·s^-2	v_max km·h^-1
示范区工况	29.23	28.21	16.06	26.50	23.97	27.92	2.16	-2.10	0.61	-0.62	53.30
CHTC-B	29.16	25.88	22.60	22.37	15.08	19.43	1.26	-1.32	0.48	-0.51	45.60

[1]	李耀华, 邵攀登, 翟登旺, 任田园, 宋伟萍, 刘洋, 赵承辉. 基于聚类与 Markov 链法的西安市某线路城市客车工况构建[J]. 汽车安全与节能学报, 2022, 13(2): 341-349.
[2]	杨林，胡艳青，闫斌. 基于行驶工况的插电式混合动力汽车电能消耗最优控制[J]. JASE, 2017, 08(01): 87-96.
[3]	张小龙，刘鹏飞，任平，陈彬，宋健. 乘用车城市道路行驶工况制定方法试验研究[J]. JASE, 2016, 07(01): 66-71.
[4]	贾要勤, 曹慧敏. 电动汽车驱动用IPM 电机全工况控制策略整合[J]. 汽车安全与节能学报, 2014, 5(04): 385-390.
[5]	罗玉涛, 谭迪, 何小颤. 基于行驶工况的动力锂离子电池包的热动力仿真（英文）[J]. 汽车安全与节能学报, 2012, 3(1): 51-57.