• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

生态协同异构电动汽车队列的合并控制。

Ecological cooperative merging control of heterogeneous electric vehicle platoons.

机构信息

School of Automotive Engineering, Lanzhou Institute of Technology, Lanzhou, China.

出版信息

PLoS One. 2024 Nov 12;19(11):e0309930. doi: 10.1371/journal.pone.0309930. eCollection 2024.

DOI:10.1371/journal.pone.0309930
PMID:39531482
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11556728/
Abstract

Vehicle platooning improves energy savings via vehicle-to-vehicle (V2V) communication. Ecological cooperative adaptive cruise control (Eco-CACC) is implemented in platoons for merging task by using regrouped platoon models. The merging positions are selected in the middle and tail of an original platoon with a two-vehicle sub-platoon. The distributed nonlinear model predictive controller based on signal temporal logic (DNMPC-STL) approach is developed to model the Eco-CACC merging strategy. The performance of the Eco-CACC merging strategy is modeled by objective control for a predecessor-leader following (PLF) topology. The results demonstrate that merging positions located in the tail exhibit superior performance and can be used to improve stability, tracking performance, energy consumption efficiency and SOC of battery.

摘要

车辆编队通过车对车（V2V）通信提高了节能效果。在编队中实施生态协同自适应巡航控制（Eco-CACC），通过重新分组的编队模型实现合并任务。合并位置选择在原始编队的中间和尾部，采用两辆汽车的子编队。基于信号时序逻辑（STL）的分布式非线性模型预测控制器（DNMPC-STL）方法用于对 Eco-CACC 合并策略进行建模。通过前导-跟随者（PLF）拓扑的目标控制来对 Eco-CACC 合并策略的性能进行建模。结果表明，位于尾部的合并位置具有更好的性能，可以用于提高稳定性、跟踪性能、能量消耗效率和电池的 SOC。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/1efcf2c604ea/pone.0309930.g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/798b247dee67/pone.0309930.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/b11c9f409112/pone.0309930.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/8d2f8f6bd414/pone.0309930.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/9a93e42b9be3/pone.0309930.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/0cffc72e2f1f/pone.0309930.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/d9154a30d1e2/pone.0309930.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/82812af23ffc/pone.0309930.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/d62e74bd785d/pone.0309930.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/c5a547d89998/pone.0309930.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/dc172e22a5f1/pone.0309930.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/2d4de9034c1b/pone.0309930.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/d48753599faa/pone.0309930.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/6667f28d7c56/pone.0309930.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/800eaee934b6/pone.0309930.g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/4bbe2a5f54dd/pone.0309930.g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/ee11bc5d9a11/pone.0309930.g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/1efcf2c604ea/pone.0309930.g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/798b247dee67/pone.0309930.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/b11c9f409112/pone.0309930.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/8d2f8f6bd414/pone.0309930.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/9a93e42b9be3/pone.0309930.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/0cffc72e2f1f/pone.0309930.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/d9154a30d1e2/pone.0309930.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/82812af23ffc/pone.0309930.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/d62e74bd785d/pone.0309930.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/c5a547d89998/pone.0309930.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/dc172e22a5f1/pone.0309930.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/2d4de9034c1b/pone.0309930.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/d48753599faa/pone.0309930.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/6667f28d7c56/pone.0309930.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/800eaee934b6/pone.0309930.g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/4bbe2a5f54dd/pone.0309930.g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/ee11bc5d9a11/pone.0309930.g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/216f/11556728/1efcf2c604ea/pone.0309930.g017.jpg

相似文献

1
Ecological cooperative merging control of heterogeneous electric vehicle platoons.生态协同异构电动汽车队列的合并控制。
PLoS One. 2024 Nov 12;19(11):e0309930. doi: 10.1371/journal.pone.0309930. eCollection 2024.
2
Energy-Oriented Hybrid Cooperative Adaptive Cruise Control for Fuel Cell Electric Vehicle Platoons.面向能量的燃料电池电动车辆编队混合协作自适应巡航控制
Sensors (Basel). 2024 Aug 5;24(15):5065. doi: 10.3390/s24155065.
3
Decentralized Platoon Join-in-Middle Protocol Considering Communication Delay for Connected and Automated Vehicle.考虑通信延迟的车联网中自动驾驶车辆的分布式编队中途加入协议
Sensors (Basel). 2021 Oct 27;21(21):7126. doi: 10.3390/s21217126.
4
Robust Time-Delay Feedback Control of Vehicular CACC Systems with Uncertain Dynamics.具有不确定动力学的车辆协同自适应巡航控制系统的鲁棒时滞反馈控制
Sensors (Basel). 2020 Mar 23;20(6):1775. doi: 10.3390/s20061775.
5
A Safety Reinforced Cooperative Adaptive Cruise Control Strategy Accounting for Dynamic Vehicle-to-Vehicle Communication Failure.考虑动态车对车通信失败的安全增强型协同自适应巡航控制策略。
Sensors (Basel). 2021 Sep 14;21(18):6158. doi: 10.3390/s21186158.
6
Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies.基于混合轨迹规划和 CACC 策略的车队合并方法。
Sensors (Basel). 2021 Apr 8;21(8):2626. doi: 10.3390/s21082626.
7
Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication.联网车辆编队的控制架构：从传感器数据到使用车与万物通信的控制器设计
Sensors (Basel). 2023 Aug 31;23(17):7576. doi: 10.3390/s23177576.
8
An Adaptive Traffic-Flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons.车辆队列协同过渡机动的自适应交通流管理系统。
Sensors (Basel). 2023 Feb 23;23(5):2481. doi: 10.3390/s23052481.
9
Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions.基于强化学习的驾驶车队决策最小化能量损失方法。
Sensors (Basel). 2023 Apr 21;23(8):4176. doi: 10.3390/s23084176.
10
Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning.基于强化学习的协同编队合并控制的对比研究。
Sensors (Basel). 2023 Jan 15;23(2):990. doi: 10.3390/s23020990.

本文引用的文献

1
A Collaborative Merging Method for Connected and Automated Vehicle Platoons in a Freeway Merging Area with Considerations for Safety and Efficiency.高速公路合流区车联网中安全与效率协同的车辆合流方法
Sensors (Basel). 2023 Apr 30;23(9):4401. doi: 10.3390/s23094401.