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

• CLOUD CONTROL SYSTEMS • Previous Articles Next Articles

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)

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

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.

Figures/Tables 23

Fig 1

Fig 2

Fig 3

Table 1

Fig 4

Table 2

Fig 5

Fig 6

Fig 7

Fig 8

Fig 9

Fig 10

Fig 11

Fig 12

Fig 13

Table 3

Table 4

Fig 14

Fig 15

Table 5

Fig 16

Fig 17

Fig 18

References 32

1	XIA Y Q Cloud control systems. IEEE/CAA Journal of Automatica Sinica, 2015, 2 (2): 134- 142. doi: 10.1109/JAS.2015.7081652
2	CHU W B, WUNIRI Q, DU X P, et al Cloud control system architectures, technologies and applications on intelligent and connected vehicles: a review. Chinese Journal of Mechanical Engineering, 2021, 34 (1): 1- 23. doi: 10.1186/s10033-020-00524-5
3	YANG S C, ZHANG Z J, CAO R, et al Implementation for a cloud battery management system based on the CHAIN framework. Energy and AI, 2021, 5, 100088. doi: 10.1016/j.egyai.2021.100088
4	XU X L, WU J X, YANG G, et al Low-power task scheduling algorithm for large-scale cloud data centers. Journal of Systems Engineering and Electronics, 2013, 24 (5): 870- 878. doi: 10.1109/JSEE.2013.00101
5	XU X L, ZHANG Q T, MOU Y Q, et al Server load prediction algorithm based on CM-MC for cloud systems. Journal of Systems Engineering and Electronics, 2018, 29 (5): 1069- 1078. doi: 10.21629/JSEE.2018.05.17
6	LI K Q, LI J W, CHANG X Y, et al Principles and typical applications of cloud control system for intelligent and connected vehicles. Journal of Automotive Safety and Energy, 2020, 11 (3): 261- 275.
7	HELLSTROM E Explicit use of road topography for model predictive cruise control in heavy trucks. Linkoping: Linkoping University, 2005.
8	LATTEMANN F, NEISS K, TERWEN S, et al The predictive cruise control–a system to reduce fuel consumption of heavy duty trucks. SAE Transactions, 2004, 113 (2): 139- 146.
9	LI K Q, CHANG X Y, LI J W Cloud control system for intelligent and connected vehicles and its application. Automotive Engineering, 2020, 42 (12): 1595- 1605.
10	XIA Y Q From networked control systems to cloud control systems. Proc. of the 31st Chinese Control Conference, 2012, 5878- 5883.
11	KHATTAB A, ABDELGAWAD A, YELMARTHI K Design and implementation of a cloud-based IoT scheme for precision agriculture. Proc. of the 28th International Conference on Microelectronics, 2016, 201- 204.
12	ET-TAIBI B, ABID M R, BOUMHIDI I, et al Smart agriculture as a cyber physical system: a real-world deployment. Proc. of the 4th International Conference on Intelligent Computing in Data Sciences, 2020, 1- 7.
13	HU L, MIAO Y M, WU G X, et al iRobot-Factory: an intelligent robot factory based on cognitive manufacturing and edge computing. Future Generation Computer Systems, 2019, 90, 569- 577. doi: 10.1016/j.future.2018.08.006
14	CHEN G G, WANG P, FENG B, et al The framework design of smart factory in discrete manufacturing industry based on cyber-physical system. International Journal of Computer Integrated Manufacturing, 2020, 33 (1): 79- 101. doi: 10.1080/0951192X.2019.1699254
15	XIA Y Q, YAN C, WANG X J, et al Intelligent transportation cyber-physical cloud control systems. Acta Automatica Sinica, 2019, 45 (1): 132- 142.
16	OZATAY E, ONORI S, WOLLAEGER J, et al Cloud-based velocity profile optimization for everyday driving: a dynamic-programming-based solution. IEEE Trans. on Intelligent Transportation Systems, 2014, 15 (6): 2491- 2505. doi: 10.1109/TITS.2014.2319812
17	HAMEDNIA A, MURGOVSKI N, FREDRIKSSON J Predictive velocity control in a hilly terrain over a long look-ahead horizon. IFAC-PapersOnLine, 2018, 51 (31): 485- 492. doi: 10.1016/j.ifacol.2018.10.107
18	SCHWARZKOPF A B, LEIPNIK R B Control of highway vehicles for minimum fuel consumption over varying terrain. Transportation Research, 1977, 11 (4): 279- 286. doi: 10.1016/0041-1647(77)90093-4
19	LIN Y C, HSU H C, KUO I C An eco-cruising control systems using nonlinear predictive control approach. Proc. of the International Automatic Control Conference, 2016, 56- 94.
20	WEIBMANN A, GORGES D, LIN X H Energy-optimal adaptive cruise control based on model predictive control. IFAC-PapersOnLine, 2017, 50 (1): 12563- 12568. doi: 10.1016/j.ifacol.2017.08.2196
21	GUO L L, GAO B Z, GAO Y, et al Optimal energy management for HEVs in eco-driving applications using bi-level MPC. IEEE Trans. on Intelligent Transportation Systems, 2016, 18 (8): 2153- 2162.
22	KUMAR R, IVANTYSYNOVA M An instantaneous optimization based power management strategy to reduce fuel consumption in hydraulic hybrids. International Journal of Fluid Power, 2011, 12 (2): 15- 25. doi: 10.1080/14399776.2011.10781027
23	HUANG Y J, YIN C L, ZHANG J W Design of an energy management strategy for parallel hybrid electric vehicles using a logic threshold and instantaneous optimization method. International Journal of Automotive Technology, 2009, 10 (4): 513- 521. doi: 10.1007/s12239-009-0059-4
24	HELLSTROM E, ASLUND J, NIELSEN L Design of an efficient algorithm for fuel-optimal look-ahead control. Control Engineering Practice, 2010, 18 (11): 1318- 1327. doi: 10.1016/j.conengprac.2009.12.008
25	HELLSTROM E, IVARSSON M, NIELSEN L Look-ahead control for heavy trucks to minimize trip time and fuel consumption. IFAC Proceedings Volumes, 2007, 40 (10): 439- 446. doi: 10.3182/20070820-3-US-2918.00060
26	CONG X Y, ZHANG Y G, WANG C P, et al Look-ahead gear-shifting strategy on ramps for heavy trucks with automated mechanical transmission. Advances in Mechanical Engineering, 2019, 11 (1): 1- 13.
27	HELLSTROM E, FROBERG A, NIELSEN L A real-time fuel-optimal cruise controller for heavy trucks using road topography information. Proc. of the SAE World Congress & Exhibition, 2006, 1- 8.
28	LIN Y C, NGUYEN H L T, BALAS V E, et al Adaptive prediction-based control for an ecological cruise control system on curved and hilly roads. Journal of Intelligent & Fuzzy Systems, 2020, 38 (5): 6129- 6144.
29	XU S B, LI S E, CHENG B, et al Instantaneous feedback control for a fuel-prioritized vehicle cruising system on highways with a varying slope. IEEE Trans. on Intelligent Transportation Systems, 2016, 18 (5): 1210- 1220.
30	QU L H Ecological cruising control strategy design of electric vehicle based on prediction of road slopes. Nanjing: Southeast University, 2019.
31	GILLESPIE T D Fundamentals of vehicle dynamics. New York: SAE International, 2021.
32	ZHANG J, JIN H Optimized calculation of the economic speed profile for slope driving: based on iterative dynamic programming. IEEE Trans. on Intelligent Transportation Systems, 2022, 23 (4): 3313- 3323. doi: 10.1109/TITS.2020.3035610

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 23

References 32

Related Articles 0

Recommended Articles

Metrics

Comments