基于快速随机模型预测控制的网联混合车队生态驾驶策略研究

doi:10.19562/j.chinasae.qcgc.2024.09.006

摘要/Abstract

摘要：

为解决网联汽车由于驾驶员误差存在导致的速度轨迹偏移问题，本文提出一种实时的考虑驾驶员误差的网联混合车队生态驾驶策略。首先通过实车试验采集不同驾驶员的驾驶员误差数据，建立基于马尔可夫链的驾驶员误差模型，用于预测未来一段时间的驾驶员误差。然后以最小化整个车队的燃油消耗为优化目标，将车队速度轨迹优化问题描述为一个最优控制问题，采用快速随机模型预测控制（fast stochastic model predictive control， FSMPC）算法求解车队中网联汽车的最优速度轨迹。仿真和智能网联微缩车试验结果表明，相比于传统的基于快速模型预测控制（fast model predictive control， FMPC）的生态驾驶策略，本文所提出的生态驾驶策略能够有效减小车辆的速度轨迹偏移，并降低整个车队的燃油消耗，且满足实时性要求。

关键词: 网联汽车, 驾驶员误差, 快速随机模型预测控制, 混合车队

Abstract:

To address the problem of speed trajectory deviation of connected vehicles （CVs） caused by human driver error， a real-time eco-driving strategy for connected mixed platoons considering human driver error is proposed in this paper. Firstly， real vehicle tests are conducted to collect human driver error data of different drivers to establish the human driver error model based on Markov chain so as to predict the human driver error for a period of time in the future. Then， with the optimization objective of minimizing the fuel consumption of the entire platoon， the platoon speed trajectory optimization problem is formulated as an optimal control problem. Fast stochastic model predictive control （FSMPC） is employed to calculate the optimal speed trajectories of the connected vehicle in the mixed platoon. Both the simulation and intelligent and connected micro-car test results indicate that， compared to the traditional eco-driving strategy based on fast model predictive control （FMPC）， the proposed eco-driving strategy can effectively reduce the speed trajectory deviation and fuel consumption of the whole platoon as well as meet the real-time requirements.

Key words: connected vehicle, human driver error, fast stochastic model predictive control, mixed platoon

钱立军,陈健,赵丰,陈欣宇,宣亮. 基于快速随机模型预测控制的网联混合车队生态驾驶策略研究[J]. 汽车工程, 2024, 46(9): 1587-1599.

Lijun Qian,Jian Chen,Feng Zhao,Xinyu Chen,Liang Xuan. Research on Fast Stochastic Model Predictive Control-Based Eco-Driving Strategy for Connected Mixed Platoons[J]. Automotive Engineering, 2024, 46(9): 1587-1599.

图/表 20

图1

图2

表1

表2

图3

图4

图5

图6

图7

图8

表3

表4

图9

图10

表5

图11

表6

图12

图13

表7

参考文献 18

1	FAFOUTELLIS P， MANTOUKA E G， VLAHOGIANNI E I. Eco-driving and its impacts on fuel efficiency： an overview of technologies and data-driven methods［J］. Sustainability， 2021， 13（1）： 1-17.
2	NAEEM H M Y， BUTT Y A， AHMED Q， et al. Optimal-control-based eco-driving solution for connected battery electric vehicle on a signalized route［J］. Automotive Innovation， 2023， 6： 586-596.
3	LIORIS J， PEDARSANI R， TASCIKARAOGLU F Y， et al. Doubling throughput in urban roads by platooning［J］. IFAC - Papers Online， 2016， 49（3）： 49-54.
4	GONCALVES T R， VARMA V S， ELAYOUBI S E. Vehicle platooning schemes considering V2V communications： a joint communication/control approach［C］. 2020 IEEE Wireless Communications and Networking Conference （WCNC）， 2020： 1-6.
5	杨澜，赵祥模，吴国垣，等. 智能网联汽车协同生态驾驶策略综述［J］. 交通运输工程学报， 2020， 20（5）： 58-72.
	YANG L， ZHAO X M， WU G Y， et al. Review of cooperative eco-driving strategies based on connected and automated vehicles ［J］. Journal of Traffic and Transportation Engineering， 2020， 20（5）： 58-72.
6	MA F W， YANG Y， WANG J W， et al. Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections［J］. Transportation Research Part D： Transport and Environment， 2021， 92：1-17.
7	ZHAI C J， CHEN C Q， ZHENG X L， et al. Ecological cooperative adaptive cruise control for heterogenous vehicle platoons subject to time delays and input saturations ［J］. IEEE Transactions on Intelligent Transportation Systems， 2023， 24（3）： 2862-2873.
8	HALDER K， MONTANARO U， DIXIT S， et al. Distributed H-infinity controller design and robustness analysis for vehicle platooning under random packet drop ［J］. IEEE Transactions on Intelligent Transportation Systems， 2022， 23（5）： 4373-4386.
9	ZHAO W M， DONG N， SIMON S， et al. A platoon based cooperative eco-driving model for mixed automated and human⁃driven vehicle at a signalised intersection ［J］. Transportation Research Part C： Emerging Technologies， 2018， 95： 802-821.
10	CHEN C Y， WANG J W， XU Q， et al. Mixed platoon control of automated and human-driven vehicles at a signalized intersection： dynamical analysis and optimal control ［J］. Transportation Research Part C： Emerging Technologies， 2021， 127： 1-19.
11	ZHANG H Y， DU L L. Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system， online learning and distributed optimization part I： modeling and solution algorithm design ［J］. Transportation Research Part B： Methodological， 2023， 172： 174-198.
12	ZHANG H Y， DU L L. Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system， online learning and distributed optimization part II： theoretical analysis ［J］. Transportation Research Part B： Methodological， 2023， 172： 199-216.
13	HAO P， WU G Y， BORIBOONSOMSIN K， et al. Eco-approach and departure （EAD） application for actuated signals in real-world traffic ［J］. IEEE Transactions on Intelligent Transportation Systems， 2021， 22（12）： 7837-7849.
14	QI X W， WANG P， WU G Y， et al. Connected cooperative ecodriving system considering human driver error ［J］. IEEE Transactions on Intelligent Transportation Systems， 2018， 19（8）： 2721-2733.
15	HELBING D， TILCH B. Generalized force model of traffic dynamics ［J］. Physical Review E， 1998， 58（1）： 133-138.
16	AKCELIK R. Efficiency and drag in the power-based model of fuel consumption ［J］. Transportation Research Part B： Methodological， 1989， 23（5）： 376-385.
17	WANG Y， BOYD S. Fast model predictive control using online optimization ［J］. IEEE Transactions on Control Systems Technology， 2010， 18（2）： 267-278.
18	HOMCHAUDHURI B， VAHIDI A， PISU P. Fast model predictive control-based fuel efficient control strategy for a group of connected vehicles in urban road conditions ［J］. IEEE Transactions on Control Systems Technology， 2017， 25（2）： 760-767.

参数	数值
采样时间/s	0.5
预测时域/s	3
路径数量	400
权重系数ω₁	20
权重系数ω₂	5 000
权重系数ω₃	500

参数	数值
整备质量/kg	1 680
迎风面积/m²	2.25
空气阻力系数	0.3
滚动阻力系数	0.01
最高车速/（m·s^-1）	13.89
最低车速/（m·s^-1）	0
最大加速度/（m·s^-2）	3
最大减速度/（m·s^-2）	-6

车辆序号	FMPC	FSMPC
车辆1	5.82	5.43
车辆2	6.45	6.10
车辆3	7.04	6.76
车辆4	7.56	7.33
平均值	6.72	6.40

参数	数值
道路总长度/m	19
信号灯周期/s	70
信号灯红灯时间/s	45
信号灯绿灯时间/s	25
车辆最高车速/（m·s^-1）	0.26
车辆最低车速/（m·s^-1）	0
车辆最大加速度/（m·s^-2）	0.06
车辆最大减速度/（m·s^-2）	-0.12

车辆序号	FMPC	FSMPC
车辆1	2.372 8	2.369 4
车辆2	2.384 1	2.380 7
车辆3	2.455 6	2.391 2
平均值	2.404 2	2.380 4