Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions
With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, desi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/13/12/522 |
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| author | Jiayan Wen Lun Li Qiqi Wu Kene Li Jingjing Lu |
| author_facet | Jiayan Wen Lun Li Qiqi Wu Kene Li Jingjing Lu |
| author_sort | Jiayan Wen |
| collection | DOAJ |
| description | With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NO<sub>x</sub> emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods. |
| format | Article |
| id | doaj-art-5f3e9a04924640f7b59dc524b479eaa2 |
| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-5f3e9a04924640f7b59dc524b479eaa22025-08-20T02:01:05ZengMDPI AGActuators2076-08252024-12-01131252210.3390/act13120522Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered ConditionsJiayan Wen0Lun Li1Qiqi Wu2Kene Li3Jingjing Lu4School of Automation, Guangxi University of Science and Technology, Liuzhou 545616, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou 545616, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou 545616, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou 545616, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou 545616, ChinaWith the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NO<sub>x</sub> emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.https://www.mdpi.com/2076-0825/13/12/522CACCdistributed model predictive controlfuel consumption optimizationNO<sub>x</sub> emissionsadaptive particle swarm optimizationevent-triggered conditions |
| spellingShingle | Jiayan Wen Lun Li Qiqi Wu Kene Li Jingjing Lu Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions Actuators CACC distributed model predictive control fuel consumption optimization NO<sub>x</sub> emissions adaptive particle swarm optimization event-triggered conditions |
| title | Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions |
| title_full | Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions |
| title_fullStr | Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions |
| title_full_unstemmed | Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions |
| title_short | Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions |
| title_sort | multi objective cooperative adaptive cruise control platooning of intelligent connected commercial vehicles in event triggered conditions |
| topic | CACC distributed model predictive control fuel consumption optimization NO<sub>x</sub> emissions adaptive particle swarm optimization event-triggered conditions |
| url | https://www.mdpi.com/2076-0825/13/12/522 |
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