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浙江大学学报(工学版)
浙江大学学报(工学版)  2025, Vol. 59 Issue (10): 2086-2095    DOI: 10.3785/j.issn.1008-973X.2025.10.009
交通工程、水利工程、土木工程     
考虑乘客舒适度的混合车辆队列最优能耗控制方法
孟芸(),苗鹏辉,闫茂德*(),左磊
长安大学 电子与控制工程学院，陕西 西安 710064
Optimal energy consumption control method for mixed vehicle platoon considering passenger comfort
Yun MENG(),Penghui MIAO,Maode YAN*(),Lei ZUO
School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
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摘要：

为了解决混合车辆队列协作控制中的能耗优化问题，同时保障乘客舒适度，提出实时优化的分布式模型预测控制与智能驾驶员模型结合的协作控制方法. 针对队列中的网联自动驾驶车辆，建立乘客舒适度约束，使用精确的油耗模型建立实时优化的分布式模型预测控制方法，在确保队列一致稳定的基础上降低实时能耗. 针对队列中的人工驾驶车辆，采用乘客舒适度和能耗性能良好的智能驾驶员跟驰模型描述跟驰行为，推理分析得到跟驰稳定性条件. 分别在恒速与变速领航车辆场景下开展仿真实验，验证所提控制方法在满足乘客舒适度约束条件下的跟踪性能. 以从初始状态到稳态的平均发动机功率为能耗优化指标，进行多组对比仿真实验，结果表明，相比对比算法，所提控制方法能够有效降低混合车辆队列的能耗.
关键词： 混合车辆队列;  能耗优化;  乘客舒适度;  分布式模型预测控制;  网联自动驾驶车辆;  人工驾驶车辆    
Abstract:

To address the energy consumption optimization problem in cooperative control of mixed vehicle platoon while ensuring passenger comfort, a collaborative control method combining real-time optimization with distributed model predictive control and an intelligent driver model was proposed. For connected autonomous vehicles in the platoon, passenger comfort constraints were established. Utilizing a precise fuel consumption model, a real-time optimized distributed model predictive control method was designed to reduce real-time energy consumption while ensuring the consistency and stability of the platoon. For human-driven vehicles in the platoon, an intelligent driver-following model that ensures passenger comfort and low energy consumption was adopted. The following stability condition was then derived. Simulation experiments were conducted in the scenarios of constant speed and variable speed leader vehicles to verify the tracking performance of the proposed control method under the constraints of passenger comfort. The average engine power from the initial state to the steady state was used as the energy consumption optimization index, and multiple sets of comparative simulation experiments were conducted. Simulation results show that, compared with the comparative algorithm, the proposed control method can effectively reduce the energy consumption of the mixed vehicle platoon.
Key words: mixed vehicle platoon    energy consumption optimization    passenger comfort    distributed model predictive control    connected autonomous vehicle    human-driven vehicle
收稿日期: 2024-09-04 出版日期: 2025-10-27
CLC:  TP 29  
基金资助: 国家自然科学基金资助项目（52372406）；国家重点研发计划资助项目（2020YFB1600400）；陕西省重点研发计划项目（2023-YBGY-212）.
通讯作者: 闫茂德     E-mail: mengyun@chd.edu.cn;mdyan@chd.edu.cn
作者简介: 孟芸（1987—），女，教授，博士，从事车辆队列、车路协同研究. orcid.org/0000-0002-2317-9379. E-mail：mengyun@chd.edu.cn
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引用本文:

孟芸,苗鹏辉,闫茂德,左磊. 考虑乘客舒适度的混合车辆队列最优能耗控制方法[J]. 浙江大学学报(工学版), 2025, 59(10): 2086-2095.

Yun MENG,Penghui MIAO,Maode YAN,Lei ZUO. Optimal energy consumption control method for mixed vehicle platoon considering passenger comfort. Journal of ZheJiang University (Engineering Science), 2025, 59(10): 2086-2095.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.10.009    或    https://www.zjujournals.com/eng/CN/Y2025/V59/I10/2086

图 1  网联自动驾驶车辆和人工驾驶车辆协作控制框架
图 2  网联自动驾驶车辆的通信拓扑
参数数值
$ {m_i}/{\text{kg}} $$ 1500+{U}[ - 200,{\text{ }}200] $
$ {\tau _i}/{\text{s}} $$ 0.50,0.38,0.42,0.60,0.45,0.50 $
$ \rho /({\text{kg}} \cdot {{\text{m}}^{ - 2}}) $$ 1.22 $
$ C_i^{\text{A}} $$ 0.90 - 0.01 \times i $
$ g $$ 9.80 $
$ {N_{\text{p}}} $$ 20 $
$ {a_{{\text{LB}}}}/({\text{m}} \cdot {{\text{s}}^{ - 2}}) $$ - 2 $
$ {a_{{\text{UB}}}}/({\text{m}} \cdot {{\text{s}}^{ - 2}}) $$ 2 $
$ {v^0}/({\text{m}} \cdot {{\text{s}}^{ - 1}}) $$ 20 $
$ {a_{\max }}/({\text{m}} \cdot {{\text{s}}^{ - 2}}) $$ 2 $
$ {b_{{\text{comf}}}}/({\text{m}} \cdot {{\text{s}}^{ - 2}}) $$ 2 $
$ {\alpha _{0,i}} $$ 0.54+0.01 \times i $
$ {\alpha _{1,i}} $$ 0.060 - 0.001 \times i $
$ {\alpha _{2,i}} $$ 0.000\;17 - 0.000\;01 \times i $
$ {r_{{\text{W}},i}}/{\text{m}} $$ 0.25+{U}[ - 0.5,{\text{ }}0.5] $
$ {\eta _{{\text{T}},i}} $$ 0.80+0.01 \times i $
$ {A_{\text{f}}}/{{\text{m}}^2} $$ 2.12 $
$ f_i^{\text{R}} $$ 0.015+0.001 \times i $
$ \Delta t $$ 0.1 $
$ {s^0}/{\text{m}} $$ 1.68 $
$ T $$ 0.8 $
$ {d_0}/{\text{m}} $$ 10 $
$ T'/{\text{s}} $$ 0 $
$ {{\boldsymbol{R}}_i} $$[ 0.01] $
$ {{\boldsymbol{F}}_i} $$ \text{diag}\;(5,\;2.5,\;1) $
$ {{\boldsymbol{G}}_i} $$ {(\left| {{\mathcal{O}_i}} \right|+1)^2} \cdot {{\boldsymbol{G}}_i} $
表 1  所提能耗控制方法的参数设置
图 3  恒速领航车辆场景的车辆状态
图 4  恒速领航车辆场景的车辆状态误差
图 5  恒速领航车辆场景的网联自动驾驶车辆控制输入
图 6  变速领航车辆场景的车辆状态
图 7  变速领航车辆场景的车辆状态误差
图 8  变速领航车辆场景的网联自动驾驶车辆控制输入
场景编号$ {p_i}(0)/{\text{m}} $$ {v_i}(0)/({\text{m}} \cdot {{\text{s}}^{ - 1}}) $$ {a_i}(t)/({\text{m}} \cdot {{\text{s}}^{ - 2}}) $
1$ - i \cdot {d_0} $$ 10+U{\rm{[ - 2, 2]}} $$ 0 $
2$ - i \cdot {d_0}+U{\rm{[ - 3, 3]}} $$ 10+U{\rm{[ - 2, 2]}} $$ 0 $
3$ - i \cdot {d_0}+U{\rm{[ - 3, 3]}} $$ 10+U{\rm{[ - 3, 3]}} $$ 0 $
表 2  能耗控制方法仿真对比实验的车辆状态分布
图 9  不同控制方法在3种场景中的能耗对比
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