This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powe...This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powertrain system is as efficient as possible.With connected vehice technique,ACC system shows considerable potential of high energy eficiency.Combining a classical ACC algorithm,a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon.The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle.The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.展开更多
This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus.Large scale vehicles in a traffic flow is targeted instead of individual vehicles,and it is...This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus.Large scale vehicles in a traffic flow is targeted instead of individual vehicles,and it is assumed that the propulsion power of vehicles is hybrid electric powertrain.The control scheme is designed in the following two stages.In the first stage,in order to achieve speed consensus,the acceleration control law is designed by applying the MFG(mean-field game)theory.In the second stage,optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint.The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.展开更多
In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the conne...In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.展开更多
This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connecte...This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connected environment with real-time vehicle-to-everything information,including geographic information,vehicle-to-infrastructure information and vehicle-to-vehicle information.The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time.The proposed strategy includes multiple rules and model predictive control(MPC).The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode.To improve fuel economy,the optimal energy management strategy is primarily considered,and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints,a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon.Therefore,this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed.To validate the proposed optimization strategy,a powertrain control simulation platform in a traffic-in-the-loop environment is constructed,and case study results performed on the constructed platform are reported and discussed.展开更多
As a trend in the innovation of automotive engineering,connectivity provides new opportunities and challenging issues for vehicular powertrain control due to big potential in the use of the connected information for i...As a trend in the innovation of automotive engineering,connectivity provides new opportunities and challenging issues for vehicular powertrain control due to big potential in the use of the connected information for improving energy efficiency and reducing CO_(2) emission.In real world driving situation,a bottleneck for achieving optimal energy efficiency via control of the power sources in the powertrain is the uncertainty in power demand,since the power demand is delivered by the driver according to the driving environment which is always with stochasticity and un-detectable event in the environment.The connectivity enables us to predict the power demand in advance by using the real-time information of vehicle-to-vehicle,vehicle-to-infrastructure.展开更多
In order to improve the driving dynamics and riding comfort of pure electric vehicles,taking a two-speed I-AMT(Inverse-Automatic Mechanical Transmission)with rear friction clutch as the research object,a gear shift st...In order to improve the driving dynamics and riding comfort of pure electric vehicles,taking a two-speed I-AMT(Inverse-Automatic Mechanical Transmission)with rear friction clutch as the research object,a gear shift strategy,which consists of the open-loop control of the clutch position control and the closed-loop control of the drive motor speed control,is proposed.Considering the inherent time-delay and external disturbances within the motor speed adjustment system,a two DOF(degree-of-freedom)Smith predictor with feedforward input is designed to track the target speed of the drive motor.The feedforward input is used to eliminate the influence of clutch sliding friction on the motor speed control,while the feedback speed tracking controller is applied to realize the speed tracking performance with the existence of time-delay and the external disturbance.In order to verify the effectiveness of the gear shift control strategy and the accuracy of the two DOF Smith controller with feedforward control,simulation results comparison is firstly carried out to illustrate the effectiveness of the control scheme.Then,a light pure electric vehicle equipped with I-AMT was used for downshift experiments under large throttle,which is the most difficult working scenario to control the transmission.The experimental results show that the two DOF Smith controller can eliminate the influence of time-delay on the closed-loop control,and the proposed whole gear shift control strategy can limit the clutch slippage time within 1.5 s,resulting in a smaller shift jerk,thus guarantee the driving dynamics and riding comfort simultaneously.展开更多
基金supported by the National Natural Science Foundation(NNSF)of China(No.61973053).
文摘This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powertrain system is as efficient as possible.With connected vehice technique,ACC system shows considerable potential of high energy eficiency.Combining a classical ACC algorithm,a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon.The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle.The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.
文摘This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus.Large scale vehicles in a traffic flow is targeted instead of individual vehicles,and it is assumed that the propulsion power of vehicles is hybrid electric powertrain.The control scheme is designed in the following two stages.In the first stage,in order to achieve speed consensus,the acceleration control law is designed by applying the MFG(mean-field game)theory.In the second stage,optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint.The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.
文摘In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.
基金supported by the National Natural Science Foundation of China(No.61973053).The authors would like to thank the Toyota Motor Corporation for the technical support on this research work..
文摘This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connected environment with real-time vehicle-to-everything information,including geographic information,vehicle-to-infrastructure information and vehicle-to-vehicle information.The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time.The proposed strategy includes multiple rules and model predictive control(MPC).The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode.To improve fuel economy,the optimal energy management strategy is primarily considered,and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints,a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon.Therefore,this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed.To validate the proposed optimization strategy,a powertrain control simulation platform in a traffic-in-the-loop environment is constructed,and case study results performed on the constructed platform are reported and discussed.
文摘As a trend in the innovation of automotive engineering,connectivity provides new opportunities and challenging issues for vehicular powertrain control due to big potential in the use of the connected information for improving energy efficiency and reducing CO_(2) emission.In real world driving situation,a bottleneck for achieving optimal energy efficiency via control of the power sources in the powertrain is the uncertainty in power demand,since the power demand is delivered by the driver according to the driving environment which is always with stochasticity and un-detectable event in the environment.The connectivity enables us to predict the power demand in advance by using the real-time information of vehicle-to-vehicle,vehicle-to-infrastructure.
基金the National Natural Science Foundation of China under Grant 62003244the Perspective Study Funding of Nanchang Automotive Institute of Intelligence and New Energy+1 种基金Tongji University under Grant TPD-TC202110-10,in part by the Jilin Provincial Science&Technology Department under Grant 20200301011RQthe Fundamental Research Funds for the Central Universities under Grant 22120210160.
文摘In order to improve the driving dynamics and riding comfort of pure electric vehicles,taking a two-speed I-AMT(Inverse-Automatic Mechanical Transmission)with rear friction clutch as the research object,a gear shift strategy,which consists of the open-loop control of the clutch position control and the closed-loop control of the drive motor speed control,is proposed.Considering the inherent time-delay and external disturbances within the motor speed adjustment system,a two DOF(degree-of-freedom)Smith predictor with feedforward input is designed to track the target speed of the drive motor.The feedforward input is used to eliminate the influence of clutch sliding friction on the motor speed control,while the feedback speed tracking controller is applied to realize the speed tracking performance with the existence of time-delay and the external disturbance.In order to verify the effectiveness of the gear shift control strategy and the accuracy of the two DOF Smith controller with feedforward control,simulation results comparison is firstly carried out to illustrate the effectiveness of the control scheme.Then,a light pure electric vehicle equipped with I-AMT was used for downshift experiments under large throttle,which is the most difficult working scenario to control the transmission.The experimental results show that the two DOF Smith controller can eliminate the influence of time-delay on the closed-loop control,and the proposed whole gear shift control strategy can limit the clutch slippage time within 1.5 s,resulting in a smaller shift jerk,thus guarantee the driving dynamics and riding comfort simultaneously.
