Predictive cruise control(PCC)is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance.With the continuous dev...Predictive cruise control(PCC)is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance.With the continuous development of cloud control platforms(CCPs)and telematics boxes(T-boxes),cloud-based predictive cruise control(CPCC)systems are considered an effective solution to the problems of map update difficulties and insufficient computing power on the vehicle side.In this study,a vehicle-cloud hierarchical control architecture for PCC is designed based on a CCP and T-box.This architecture utilizes waypoint structures for hierarchical and dynamic cooperative inter-triggering,enabling rolling optimization of the system and commending parsing at the vehicle end.This approach significantly improves the anti-interference capability and resolution efficiency of the system.On the CCP side,a predictive fuel-saving speed-planning(PFSP)algorithm that considers the throttle input,speed variations,and time efficiency based on the waypoint structure is proposed.It features a forward optimization search without requiring weight adjustments,demonstrating robust applicability to various road conditions and vehicles equiped with constant cruise(CC)system.On the vehicle-side T-box,based on the reference control sequence with the global navigation satellite system position,the recommended speed is analyzed and controlled using the acute angle principle.Through analyzing the differences of the PFSP algorithm compared to dynamic programming(DP)and Model predictive control(MPC)algorithms under uphill and downhill conditions,the results show that the PFSP achieves good energy-saving performance compared to CC without exhibiting significant speed fluctuations,demonstrating strong adaptability to the CC system.Finally,by building an experimental platform and running field tests over a total of 2000 km,we verified the effectiveness and stability of the CPCC system and proved the fuel-saving performance of the proposed PFSP algorithm.The results showed that the CPCC system equipped with the PFSP algorithm achieved an average fuel-saving rate of 2.05%-4.39%compared to CC.展开更多
The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomousl...The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.展开更多
The Cloud-Based Predictive Cruise Control(CPCC)system obtain road and vehicle data from the Cloud Control Platform(CCP)and efficiently computes the optimal speed and trajectory for intelligent connected vehicles(ICVs)...The Cloud-Based Predictive Cruise Control(CPCC)system obtain road and vehicle data from the Cloud Control Platform(CCP)and efficiently computes the optimal speed and trajectory for intelligent connected vehicles(ICVs).It holds significant potential for conserving vehicle energy and minimizing waiting times.However,existing research on CPCC often overlooks the practical feasibility from theory to application and lacks effective validation regarding the consistency and rationality between theoretical architecture and model application.In this paper,a co-simulation method for CPCC is proposed.Firstly,the overall architecture of the CPCC system is proposed,outlining the real-time acquisition of road information and the speed planning strategy for ICVs.Then,models and algorithms for road and vehicles within the CPCC system are introduced.Finally,a CPCC co-simulation platform is established to validate the collaborative feasibility of the CPCC architecture,models,and algorithms.Simulation results show that compared to scenarios without CPCC,the CPCC-controlled ICVs experience a 2%reduction in cruising time and a 14.7%decrease in fuel consumption.Additionally,ICV co-control simulation results indicate a 4.05%decrease in average queue length at intersections,a 4.58%reduction in average vehicle delay time,an 8.99%decrease in average number of stops,a 12.89%reduction in average vehicle energy consumption,a 7.81%decrease in CO emissions,an 8.08%reduction in NOx emissions,and a 7.46%decrease in VOC emissions.展开更多
基金Supported by National Key Research and Development Program of China(Grant No.2021YFB2501000).
文摘Predictive cruise control(PCC)is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance.With the continuous development of cloud control platforms(CCPs)and telematics boxes(T-boxes),cloud-based predictive cruise control(CPCC)systems are considered an effective solution to the problems of map update difficulties and insufficient computing power on the vehicle side.In this study,a vehicle-cloud hierarchical control architecture for PCC is designed based on a CCP and T-box.This architecture utilizes waypoint structures for hierarchical and dynamic cooperative inter-triggering,enabling rolling optimization of the system and commending parsing at the vehicle end.This approach significantly improves the anti-interference capability and resolution efficiency of the system.On the CCP side,a predictive fuel-saving speed-planning(PFSP)algorithm that considers the throttle input,speed variations,and time efficiency based on the waypoint structure is proposed.It features a forward optimization search without requiring weight adjustments,demonstrating robust applicability to various road conditions and vehicles equiped with constant cruise(CC)system.On the vehicle-side T-box,based on the reference control sequence with the global navigation satellite system position,the recommended speed is analyzed and controlled using the acute angle principle.Through analyzing the differences of the PFSP algorithm compared to dynamic programming(DP)and Model predictive control(MPC)algorithms under uphill and downhill conditions,the results show that the PFSP achieves good energy-saving performance compared to CC without exhibiting significant speed fluctuations,demonstrating strong adaptability to the CC system.Finally,by building an experimental platform and running field tests over a total of 2000 km,we verified the effectiveness and stability of the CPCC system and proved the fuel-saving performance of the proposed PFSP algorithm.The results showed that the CPCC system equipped with the PFSP algorithm achieved an average fuel-saving rate of 2.05%-4.39%compared to CC.
基金Supported by Beijing Nova Program of Science and Technology(Grant No.Z191100001119087)Beijing Municipal Science&Technology Commission(Grant No.Z181100004618005 and Grant No.Z18111000460000)。
文摘The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.
基金supported by the National Key R&D Program,China(Grant No.2021YFB2501000).
文摘The Cloud-Based Predictive Cruise Control(CPCC)system obtain road and vehicle data from the Cloud Control Platform(CCP)and efficiently computes the optimal speed and trajectory for intelligent connected vehicles(ICVs).It holds significant potential for conserving vehicle energy and minimizing waiting times.However,existing research on CPCC often overlooks the practical feasibility from theory to application and lacks effective validation regarding the consistency and rationality between theoretical architecture and model application.In this paper,a co-simulation method for CPCC is proposed.Firstly,the overall architecture of the CPCC system is proposed,outlining the real-time acquisition of road information and the speed planning strategy for ICVs.Then,models and algorithms for road and vehicles within the CPCC system are introduced.Finally,a CPCC co-simulation platform is established to validate the collaborative feasibility of the CPCC architecture,models,and algorithms.Simulation results show that compared to scenarios without CPCC,the CPCC-controlled ICVs experience a 2%reduction in cruising time and a 14.7%decrease in fuel consumption.Additionally,ICV co-control simulation results indicate a 4.05%decrease in average queue length at intersections,a 4.58%reduction in average vehicle delay time,an 8.99%decrease in average number of stops,a 12.89%reduction in average vehicle energy consumption,a 7.81%decrease in CO emissions,an 8.08%reduction in NOx emissions,and a 7.46%decrease in VOC emissions.
