Predictive cruise control for heavy trucks based on slope information under cloud control system

doi:10.23919/JSEE.2022.000081

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 812-826.doi: 10.23919/JSEE.2022.000081

收稿日期:2022-03-01 出版日期:2022-08-30 发布日期:2022-08-30

Predictive cruise control for heavy trucks based on slope information under cloud control system

Shuyan LI¹(), Keke WAN¹, Bolin GAO^2,*(), Rui LI¹(), Yue WANG²(), Keqiang LI²

¹ College of Engineering, China Agricultural University, Beijing 100083, China
² School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

Received:2022-03-01 Online:2022-08-30 Published:2022-08-30
Contact: Bolin GAO E-mail:lishuyan@cau.edu.cn;gaobolin@tsinghua.edu.cn;lirui@cau.edu.cn;wyue@mail.tsinghua.edu.cn
About author:|LI Shuyan was born in 1972. She is currently an associate professor at the College of Engineering, China Agricultural University. Her research interests are intelligent and connected vehicle, predictive cruise control, and intelligent testing of agricultural machinery and equipment. E-mail: lishuyan@cau.edu.cn||WAN Keke was born in 1998. He received his B.E. degree from Henan University of Engineering. He is pursuing his master’s degree in vehicle engineering at the College of Engineering, China Agricultural University, Beijing, China. He is engaged in research work in the School of Vehicle and Mobility, Tsinghua University. His research interests include cloud-based predictive cruise control, vehicle-road-cloud collaborative control and cloud control architecture. E-mail: wankeke@cau.edu.cn||GAO Bolin was born in 1986. He is now an associate research professor at the School of Vehicle and Mobility, Tsinghua University. His research interests include the theoretical research and engineering application of the dynamic design and control of intelligent and connected vehicles, especially about the collaborative perception and tracking method in cloud control system, intelligent predictive cruise control system on commercial trucks with cloud control mode, as well as the test and evaluation of intelligent vehicle driving system. E-mail: gaobolin@tsinghua.edu.cn||LI Rui was born in 1997. He is currently a master student at the College of Engineering, China Agricultural University, Beijing, China. He is engaged in research work in the School of Vehicle and Mobility, Tsinghua University. His research interest is adaptive cruise control. E-mail: lirui@cau.edu.cn||WANG Yue was born in 1988. He received his Ph.D. degree in vehicle engineering from Jilin University, Jilin, China, in 2020. He is currently an assistant research fellow with the School of Vehicle and Mobility, Tsinghua University, Beijing, China. His research interests include energy management, clean energy vehicles, intelligent vehicles, and eco-driving. E-mail: wyue@mail.tsinghua.edu.cn||LI Keqiang was born in 1963. He is an academician of the Chinese Academy of Engineering, and a professor at the School of Vehicle and Mobility, Tsinghua University. He is also the Director of the State Key Laboratory of Automotive Safety and Energy and a Senior Member of SAE-China. His research interests include connected and intelligent vehicles, vehicle dynamics and control. E-mail: likq@mail.tsinghua.edu.cn
Supported by:
This work was supported by the National Key Research and Development Program (2021YFB2501003), the Key Research and Development Program of Guangdong Province (2019B090912001) and the China Postdoctoral Science Foundation (2020M680531)

摘要/Abstract

Abstract:

With the advantage of fast calculation and map resources on cloud control system (CCS), cloud-based predictive cruise control (CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control (PCC) system, lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the real-time computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method (RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also, compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity. Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

Key words: predictive cruise control (PCC), cloud control system (CCS), layered architecture, road point segmentation method (RPSM), economic driving

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 812-826.

Shuyan LI, Keke WAN, Bolin GAO, Rui LI, Yue WANG, Keqiang LI. Predictive cruise control for heavy trucks based on slope information under cloud control system[J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 812-826.

图/表 23

参考文献 32

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Condition series	Value of relevant signals
Condition 1	CPCC switch=1 and data format=1 and network status=1 and GPS signal quality=1 and vehicle speed $ \in \left[ {{v_{\min }},{v_{\max }}} \right] $
Condition 2	CC switch=0
Condition 3	CPCC switch=0 or data format=0 or network status=0 or GPS signal quality=0 or vehicle speed $ \notin \left[ {{v_{\min }},{v_{\max }}} \right] $
Condition 4	Same as Condition 1
Condition 5	Same as Condition 2
Condition 6	CC switch=1

Item	Parameter	Symbol	Value
Engine	Engine speed range/(r/min)	$ {\omega _e} $	[700 2100]
	Engine torque range/Nm	$ {T_e} $	[?160 2549]
	Moment of inertia /kgm²	$ {J_e} $	[2.5 3.5]
Driveline	Final drive ratio	$ {i_0} $	3.7
	Transmission ratio	$ {i_g} $	[12.26,9.56,7.36,5.8, 4.52,3.53,2.71,2.12, 1.62,1.29,1,0.78]
	Final drive efficiency	$ {\eta _0} $	0.95
	Transmission efficiency	$ {\eta _g} $	[0.966,0.966,0.969,0.971, 0.975,0.977,0.985,0.988, 0.988,0.989,0.989,0.99]
Longitudinal force	Vehicle curb mass/kg	$ m $	49000
	Effective tire radius/m	$ {r_w} $	0.459
	Frontal area/m²	$ A $	[5.0 10.0]
	Air drag coefficient	$ {C_{\text{D}}} $	[0.5 1.0]
	Rolling resistance coefficient	$ f $	[0.010 0.018]

Weight factor	value
$ {w_1} $	1
$ {w_2} $	0.025
$ {w_3} $	0.01
$ {w_4} $	0.02
$ {w_5} $	1

Parameter	Symbol	Value
Predictive horizon/km	Y	3
Segmentation horizon/km	X	9
Iteration distance/m	$ r $	200
Discrete interval /(m/s)	${{\Delta } }v$	0.2
Reference velocity /(km/h)	${v_{{\rm{ref}}} }$	70
Engine min rpm /(r/min)	$ {\omega _{{\text{min}}}} $	1000
Engine max rpm /(r/min)	$ {\omega _{{\text{max}}}} $	1800
Min gear position	$ {G_{\min }} $	10
Max gear position	$ {G_{\max }} $	12
Maximum acceleration /(m/s²)	$ {a_{\max }} $	0.4
Minimum acceleration /(m/s²)	$ {a_{\min }} $	?0.4

Type	Distance /km	Time /s	Fuel consumption /kg	Fuel-saving rate/%
CC	34	1648.3	11.797	?
DP-V	34	1655.4	11.356	3.74
CPCC	34	1658.8	11.068	6.17

Predictive cruise control for heavy trucks based on slope information under cloud control system

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