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

doi:10.3969/j.issn.1674-8484.2022.01.021

Abstract

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

CLC Number:

U491

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.

Figures/Tables 8

References 21

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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