Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driv...Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.展开更多
For a single-motor parallel hybrid electric vehicle, during mode transitions （especially the transition from electric drive mode to engine/parallel drive mode, which requires the clutch engagement）, the drivability ...For a single-motor parallel hybrid electric vehicle, during mode transitions （especially the transition from electric drive mode to engine/parallel drive mode, which requires the clutch engagement）, the drivability of the vehicle will be signifi- cantly affected by a clutch torque induced disturbance, driveline oscillations and jerks which can occur without adequate controls. To improve vehicle drivability during mode transitions for a single-motor parallel hybrid electric vehicle, two controllers are proposed. The first controller is the engine-side controller for engine cranking/starting and speed synchronization. The second controller is the motor-side controller for achieving a smooth mode transition with reduced driveline oscillations and jerks under the clutch torque induced disturbance and system uncertainties. The controllers are all composed of a feed-forward control and a robust feedback control. The robust controllers are designed by using the mu synthesis method. In the design process, control- oriented system models that take account of various parameter uncertainties and un-modeled dynamics are used. The results of the simulation demonstrate the effectiveness of the proposed control algorithms.展开更多
In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles （PHEV） is presented. In PHEV the required torque is generated by a combination of internal-combustion engine （ICE） and an...In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles （PHEV） is presented. In PHEV the required torque is generated by a combination of internal-combustion engine （ICE） and an electric motor. The controller simulated using the SIMULINK/MATLAB package. The controller is designed based on the desired speed for driving and the state of speed error. In the other hand, performance of PHEV and ICE under different road cycle is given. The hardware setup is done for electric propulsion system; the system contains the induction motor, the three phase IGBT inverter with control circuit using microcontroller. The closed loop control system used a DC permanent generator whose output voltage is related to motor speed. Comparison between simulation and experimental results show accurate matching.展开更多
The performance of the power assist, global optimization solved by dynamic programming （DP） method, Chery and Insight control strategies are analyzed using the mild parallel hybrid electric vehicle （PHEV） model ba...The performance of the power assist, global optimization solved by dynamic programming （DP） method, Chery and Insight control strategies are analyzed using the mild parallel hybrid electric vehicle （PHEV） model based on Insight structure. The influence of the four control strategies to the load power of the electric motor system used on parallel hybrid electric vehicle is studied. It is found that 80 percent of the motor load power points are under 1/5 of the electric peak power. The motor load power of the power assist control strategy is distributed in the widest range during generating operation, and the motor load power of the global optimization control strategy has the smallest one.展开更多
文摘Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.
基金Project supported by the International S&T Cooperation Program of China(No.2010DFA72760)
文摘For a single-motor parallel hybrid electric vehicle, during mode transitions （especially the transition from electric drive mode to engine/parallel drive mode, which requires the clutch engagement）, the drivability of the vehicle will be signifi- cantly affected by a clutch torque induced disturbance, driveline oscillations and jerks which can occur without adequate controls. To improve vehicle drivability during mode transitions for a single-motor parallel hybrid electric vehicle, two controllers are proposed. The first controller is the engine-side controller for engine cranking/starting and speed synchronization. The second controller is the motor-side controller for achieving a smooth mode transition with reduced driveline oscillations and jerks under the clutch torque induced disturbance and system uncertainties. The controllers are all composed of a feed-forward control and a robust feedback control. The robust controllers are designed by using the mu synthesis method. In the design process, control- oriented system models that take account of various parameter uncertainties and un-modeled dynamics are used. The results of the simulation demonstrate the effectiveness of the proposed control algorithms.
文摘In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles （PHEV） is presented. In PHEV the required torque is generated by a combination of internal-combustion engine （ICE） and an electric motor. The controller simulated using the SIMULINK/MATLAB package. The controller is designed based on the desired speed for driving and the state of speed error. In the other hand, performance of PHEV and ICE under different road cycle is given. The hardware setup is done for electric propulsion system; the system contains the induction motor, the three phase IGBT inverter with control circuit using microcontroller. The closed loop control system used a DC permanent generator whose output voltage is related to motor speed. Comparison between simulation and experimental results show accurate matching.
文摘The performance of the power assist, global optimization solved by dynamic programming （DP） method, Chery and Insight control strategies are analyzed using the mild parallel hybrid electric vehicle （PHEV） model based on Insight structure. The influence of the four control strategies to the load power of the electric motor system used on parallel hybrid electric vehicle is studied. It is found that 80 percent of the motor load power points are under 1/5 of the electric peak power. The motor load power of the power assist control strategy is distributed in the widest range during generating operation, and the motor load power of the global optimization control strategy has the smallest one.
