基于聚类与Markov链法的西安市某线路城市客车工况构建

doi:10.3969/j.issn.1674-8484.2022.02.015

摘要/Abstract

摘要：

为了对特定区域构建符合当地车辆行驶特征的行驶工况,基于聚类与Markov链法构建了西安市某线路城市客车的行驶工况,确定了聚类个数及特征参数组合,提出了构建工况长度的确定方法,从能耗角度定义汽车行驶时的单位里程比能耗作为工况选取标准,从50条候选工况中筛选出该线路的代表工况。结果表明：与聚类法工况和V-A矩阵法工况相比,基于聚类与Markov链法构建的行驶工况与样本数据偏差最小,平均偏差率为1.17%,百千米能耗相差最小,偏差率为0.069%,显示基于聚类与Markov链法构建的行驶工况精度更高,更能反映车辆的实际行驶状况工况。

关键词: 聚类法, Markov链法, 行驶工况, 能耗

Abstract:

A driving cycles of Xi’an city bus were constructed by clustering and Markov chain method to construct driving conditions that reflect the local vehicle driving characteristics for a specific area. The number of clusters and characteristic parameters in clustering were determined, and the method to determine the length of driving cycle by Markov chain was proposed. The specific energy consumption per mileage was defined as the criterion to select typical driving cycle among 50 candidate driving cycles. The results show that, compared with the clustering method and the V-A matrix method, the driving conditions constructed based on the clustering and Markov chain method have the smallest deviation from the sample data, the average deviation rate is 1.17%, and the difference in energy consumption per 100 kilometers is the smallest, the deviation rate is 0.069%. The driving cycle constructed by clustering and Markov chain method shows higher accuracy and can reflect the actual driving conditions better.

Key words: clustering method, Markov chain method, driving cycle, energy consumption

中图分类号:

U491.17

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.

LI Yaohua, SHAO Pandeng, ZHAI Dengwang, REN Tianyuan, SONG Weiping, LIU Yang, ZHAO Chenghui. Construction of driving cycle of city bus line in Xi’an based on clustering and Markov chain method[J]. Journal of Automotive Safety and Energy, 2022, 13(2): 341-349.

图/表 16

参考文献 32

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片段序号	v_max	v_mean	v_mr	…	λ_40-50
1	-0.816 77	-0.902 16	-0.978 00	…	-0.111 67
2	0.403 90	0.128 52	0.538 30	…	-0.111 67
3	0.394 41	-0.047 98	0.515 96	…	-0.111 67
…	…	…	…	…	…
2 828	0.298 77	0.561 44	0.194 51	…	-0.111 67
2 829	0.602 67	0.748 00	0.671 48	…	-0.111 67
2 830	0.625 93	1.496 58	0.986 78	…	-0.111 67

片段序号	v_max	v_mean	v_mr	…	λ_40-50
1	-0.816 77	-0.902 16	-0.978 00	…	-0.111 67
2	0.403 90	0.128 52	0.538 30	…	-0.111 67
3	0.394 41	-0.047 98	0.515 96	…	-0.111 67
…	…	…	…	…	…
2 828	0.298 77	0.561 44	0.194 51	…	-0.111 67
2 829	0.602 67	0.748 00	0.671 48	…	-0.111 67
2 830	0.625 93	1.496 58	0.986 78	…	-0.111 67

类数	特征参数	v_sd + v_max + D_max + a_max	…	t + Lr
3	28	688	…	14 92
	1 090	1 074		346
	1 712	1 068		992
4	4	578	…	1 330
	1 191	588		379
	51	1 189		1 013
	784	475		108
5	49	409	…	974
	599	482		5
	4	773		1 170
	572	253		164
	1 606	913		517

类数	特征参数	v_sd + v_max + D_max + a_max	…	t + Lr
3	28	688	…	14 92
	1 090	1 074		346
	1 712	1 068		992
4	4	578	…	1 330
	1 191	588		379
	51	1 189		1 013
	784	475		108
5	49	409	…	974
	599	482		5
	4	773		1 170
	572	253		164
	1 606	913		517

编号	Ws / [J·(kg·km)^-1]	编号	Ws / [J·(kg·km)^-1]
1	171.38	15	165.71
2	165.20	…	…
…	…	48	167.86
13	167.31	49	167.50
14	168.06	50	166.13