Cloud-based control for hierarchical vehicle platoon predictive cruise control

doi:10.3969/j.issn.1674-8484.2023.05.008

Abstract

Abstract:

A cloud-based platoon predictive cruise control method (CPPCC) was proposed for highway scenarios to improve the economy of platoon driving and simulation experiments with real road and vehicle data models were conducted. The method employed a hierarchical structure, with the upper layer being the cloud-based platoon speed planning layer and the lower layer being the platoon stabilization control layer. The speed planning layer in the cloud considered the receding dynamic planning (RDP) algorithm of the road slope to achieve the economic goal of platoon driving. The lower vehicle-side platoon stability control layer was equipped with a distributed model predictive controller (DMPC) to track the speed send from the cloud, while considering the platoon stability control. The results show that the proposed method saves 6.04% of energy with 0.24% improvement in the overall operating efficiency compared to the predecessor-leader following’s cruise control platoon (PLF-CC) method.

Key words: smart vehicles, cloud-based platoon predictive cruise control (CPPCC), receding dynamic programming (RDP), distributed model predictive controller (DMPC), predecessor-leader following’s cruise control platoon (PLF-CC)

CLC Number:

U461.8

WANG Zhou, CHU Duanfeng, GAO Bolin, MEI Run, ZHONG Wei. Cloud-based control for hierarchical vehicle platoon predictive cruise control[J]. Journal of Automotive Safety and Energy, 2023, 14(5): 591-599.

Figures/Tables 12

References 17

[1]	EC-European Commission. EU transport in figures-statistical pocketbook[DB/OL]. (2022-02-22). https//:transport.ec.europa.eu/media-corner/publications/statistical-pocketbook-2021_en.
[2]	Department of Transportation, Bureau of Transportation Statistics US. Freight facts and figures[DB/OL]. (2022-02-22). https//:www.bts.gov/product/freight-facts-and-figures.
[3]	Scania A B. Annual report for the year 2013[R]. 556184-8564, Södertälje, Sweden, Aug. 2013.
[4]	WU Qiong, ZHENG Jun. Performance modeling and analysis of the ADHOC MAC protocol for vehicular networks[J]. Wireless Networks, 2016, 22(3): 799-812. doi: 10.1007/s11276-015-1000-6 URL
[5]	WU Qiong, ZHENG Jun. Performance modeling and analysis of the ADHOC MAC protocol for VANETs[C]// Proc IEEE Int’l Conf Commu (ICC’15), London, UK, 2015: 3646-3652.
[6]	Maged M, Mahfouz D M, Shehata O M, et al. Behavioral assessment of an optimized multi-vehicle platoon formation control for efficient fuel consumption[C]// 2nd Novel Intell Leading Emerging Sci Conf (NILES), Giza, Egypt, 2020: 403-409.
[7]	GUO Ge, WANG Qiong. Fuel-efficient en route speed planning and tracking control of truck platoons[J]. IEEE Trans Intel Transp Syst, 2018, 20(8): 3091-3103. doi: 10.1109/TITS.6979 URL
[8]	YANG Yu, MA Fangwei, WANG Jiawei, et al. Cooperative ecological cruising using hierarchical control strategy with optimal sustainable performance for connected automated vehicles on varying road conditions[J]. J Cleaner Produ, 2020, 275: 123-156.
[9]	Turri V, Besselink B, Johansson K H. Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning[J]. IEEE Trans Contr Syst Tech, 2017, 25(1): 12-28. doi: 10.1109/TCST.2016.2542044 URL
[10]	Lakshmanan V K, Sciarretta A, Ganaoui-Mourlan O E. Cooperative eco-driving of electric vehicle platoons for energy efficiency and string stability: Science direct[J]. IFAC-Papers Online, 2021, 54(2): 133-139.
[11]	李克强, 李家文, 常雪阳, 等. 智能网联汽车云控系统原理及其典型应用[J]. 汽车安全与节能学报, 2020, 11(3): 261-275.
	LI Keqiang, LI Jiawen, CHANG Xueyang. Cloud control system for intelligent and connected vehicles and its application[J]. J Auto Safe Engi, 2020, 11(3): 261-275. (in Chinese)
[12]	Ozatay E, Onori S, Wollaeger J, et al. Cloud-based velocity profile optimization for everyday driving: A dynamic-programming-based solution[J]. IEEE Trans Intel Transp Syst, 2014, 15(6): 2491-2505. doi: 10.1109/TITS.2014.2319812 URL
[13]	LI Shuyan, WAN Keke, GAO Bolin, et al. Predictive cruise control for heavy trucks based on slope information under cloud control system[J]. J Syst Eng Elect, 2022, 33(4): 812-826.
[14]	LI Shuyan, LI Rui, GAO Bolin, et al., Predictive adaptive cruise control for heavy-duty vehicle based on cloud control system[C]. // 2022 IEEE 25th Int’l Conf Intell Transp Syst (ITSC), Macau, China, 2022: 2998-3003.
[15]	ZHAI Chunjie, LIU Yonggui, FEI Luo. A switched control strategy of heterogeneous vehicle platoon for multiple objectives with state constraints[J]. IEEE Trans Intel Transp Syst, 2019, 20(5): 1883-1896. doi: 10.1109/TITS.6979 URL
[16]	ZHOU Yang, WANG Meng, Ahn S. Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability[J]. Transp Res Part B: Methodol, 2019, 128(1): 69-86. doi: 10.1016/j.trb.2019.07.001 URL
[17]	Rakovic S V, Kerrigan E C, Kouramas K I, et al. Invariant approximations of the minimal robust positively invariant set[J]. IEEE Trans Autom Contr, 2005, 50(3): 406-410. doi: 10.1109/TAC.2005.843854 URL

发动机转速， n_e	750~3 250 r/min
发动机扭矩，T_e	-325~380 Nm
惯性矩， J_e	2.5~3.5 kg m²
传动比，i_g	[5.9,3.365,2.129.1.518,1,0.783]
最终驱动效率，η₀	0.915
最终传动比，i₀	4.33
车辆质量，m	6.50 t
有效轮胎半径，r_w	0.459 m
迎风面积，A	5.10 m²
空气阻力因数，C_D	0.650
滚动阻力因数，f	0.007 60

发动机转速， n_e	750~3 250 r/min
发动机扭矩，T_e	-325~380 Nm
惯性矩， J_e	2.5~3.5 kg m²
传动比，i_g	[5.9,3.365,2.129.1.518,1,0.783]
最终驱动效率，η₀	0.915
最终传动比，i₀	4.33
车辆质量，m	6.50 t
有效轮胎半径，r_w	0.459 m
迎风面积，A	5.10 m²
空气阻力因数，C_D	0.650
滚动阻力因数，f	0.007 60

成本函数项	值
油耗优化项，W_{cost_fuel}	100
参考速度偏差项， W_{cost_ref}	5
速度变化项， W_{cost_ΔV}	10
加速度变化项， W_{cost_Δa}	10

成本函数项	值
油耗优化项，W_{cost_fuel}	100
参考速度偏差项， W_{cost_ref}	5
速度变化项， W_{cost_ΔV}	10
加速度变化项， W_{cost_Δa}	10

车辆编号	速度误差/ (m·s^-1)		间距误差/ m
车辆编号	误差范围	均值	误差范围	均值
V2	[-0.230, 0.092]	0.000	[-0.240, 0.478]	0.259
V3	[-0.235, 0.097]	0.003	[-0.285, 0.481]	0.258