The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construct...The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construction vehicles. The effect of power distribution and direction on shift characteristics of the parallel hybrid vehicle with operating loads is evaluated, which must be considered for optimal shift control. A power distribution factor is defined to accurately describe the power distribution and direction in various parallel hybrid systems. This paper proposes a Levenberg-Marquardt algorithm optimized neural network shift scheduling strategy. The methodology contains two objective functions, it is a dynamic combination of a dynamic shift schedule for optimal vehicle acceleration, and an energy-efficient shift schedule for optimal powertrain efficiency. The study is performed on a test bench under typical operating conditions of a wheel loader. The experimental results show that the proposed strategies offer effective and competitive shift performance.展开更多
To improve maneuverability and stability of articulated vehicles, we design an active steering controller, including tractor and trailer controllers, based on linear quadratic regulator(LQR) theory. First, a three-deg...To improve maneuverability and stability of articulated vehicles, we design an active steering controller, including tractor and trailer controllers, based on linear quadratic regulator(LQR) theory. First, a three-degree-of-freedom(3-DOF) model of the tractor-trailer with steered trailer axles is built. The simulated annealing particle swarm optimization(SAPSO) algorithm is applied to identify the key parameters of the model under specified vehicle speed and steering wheel angle. Thus, the key parameters of the simplified model can be obtained according to the vehicle conditions using an online look-up table and interpolation. Simulation results show that vehicle parameter outputs of the simplified model and Truck Sim agree well, thus providing the ideal reference yaw rate for the controller. Then the active steering controller of the tractor and trailer based on LQR is designed to follow the desired yaw rate and minimize their side-slip angle of the center of gravity(CG) at the same time. Finally, simulation tests at both low speed and high speed are conducted based on the Truck Sim-Simulink program. The results show significant effects on the active steering controller on improving maneuverability at low speed and lateral stability at high speed for the articulated vehicle. The control strategy is applicable for steering not only along gentle curves but also along sharp curves.展开更多
This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of ...This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of the suspension system is integrated with the longitudinal dynamics of the vehicle. Furthermore, a MPC framework with finite time horizon is formulated to calculate the optimal velocity profile that compromises fuel economy, mobility and ride comfort for every individual vehicle with the safety and physical constraints considered. In the MPC framework, the target velocity is calculated using signal phase and timing(SPAT)information to reduce the number of stoppage at red lights, and the vertical acceleration is calculated parallel to the calculation of the fuel consumption. The MPC optimal problem is solved with fast-MPC approach which enhances the computational efficiency via exploiting the structure of the control system and approximate methods. Simulation studies are conducted over different SPATs and connectivity penetration rates and the results validate the advantages of the proposed control architecture.展开更多
基金Project(51805200)supported by the National Natural Science Foundation of ChinaProject(20170520096JH)supported by the Science and Technology Development Plan of Jilin Province,ChinaProject(2016YFC0802900)supported by the National Key R&D Program of China
文摘The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construction vehicles. The effect of power distribution and direction on shift characteristics of the parallel hybrid vehicle with operating loads is evaluated, which must be considered for optimal shift control. A power distribution factor is defined to accurately describe the power distribution and direction in various parallel hybrid systems. This paper proposes a Levenberg-Marquardt algorithm optimized neural network shift scheduling strategy. The methodology contains two objective functions, it is a dynamic combination of a dynamic shift schedule for optimal vehicle acceleration, and an energy-efficient shift schedule for optimal powertrain efficiency. The study is performed on a test bench under typical operating conditions of a wheel loader. The experimental results show that the proposed strategies offer effective and competitive shift performance.
基金supported by the Program for Changjiang ScholarsInnovative Research Team in University,China(No.IRT0626)
文摘To improve maneuverability and stability of articulated vehicles, we design an active steering controller, including tractor and trailer controllers, based on linear quadratic regulator(LQR) theory. First, a three-degree-of-freedom(3-DOF) model of the tractor-trailer with steered trailer axles is built. The simulated annealing particle swarm optimization(SAPSO) algorithm is applied to identify the key parameters of the model under specified vehicle speed and steering wheel angle. Thus, the key parameters of the simplified model can be obtained according to the vehicle conditions using an online look-up table and interpolation. Simulation results show that vehicle parameter outputs of the simplified model and Truck Sim agree well, thus providing the ideal reference yaw rate for the controller. Then the active steering controller of the tractor and trailer based on LQR is designed to follow the desired yaw rate and minimize their side-slip angle of the center of gravity(CG) at the same time. Finally, simulation tests at both low speed and high speed are conducted based on the Truck Sim-Simulink program. The results show significant effects on the active steering controller on improving maneuverability at low speed and lateral stability at high speed for the articulated vehicle. The control strategy is applicable for steering not only along gentle curves but also along sharp curves.
基金supported by National Hi-Tech Research and Development Program of China(Grant Nos.2015BAG17B04&2013BAG08B01)U.S.National Science Foundation(Grant No.1544910)U.S.Department of Energy GATE Program and China Scholarship Council
文摘This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of the suspension system is integrated with the longitudinal dynamics of the vehicle. Furthermore, a MPC framework with finite time horizon is formulated to calculate the optimal velocity profile that compromises fuel economy, mobility and ride comfort for every individual vehicle with the safety and physical constraints considered. In the MPC framework, the target velocity is calculated using signal phase and timing(SPAT)information to reduce the number of stoppage at red lights, and the vertical acceleration is calculated parallel to the calculation of the fuel consumption. The MPC optimal problem is solved with fast-MPC approach which enhances the computational efficiency via exploiting the structure of the control system and approximate methods. Simulation studies are conducted over different SPATs and connectivity penetration rates and the results validate the advantages of the proposed control architecture.
