A Research On Control Strategy For Driving Smoothness And Fuel Economy Of Plug-in Hybrid Electric Vehicle

Two main concerns of the current world are energy shortage and air pollution.Plug-in hybrid electric vehicle(PHEV),with good performance in oil-saving and emission-reducing,has already become a hot research topic in China and abroad.PHEV has two power sources and two energy sources,has main traits that characterize PHEV are multi-operation mode,complex torque coupling process,intricate frequency responses in power system and time-varying delay in control system.These traits result in transient effect in operation,affect the vehicle operation status and energy consumption.How to improving fuel economy and driving smoothness is a key problem to be resolved.It is an effective method to study a PHEV based on mathematical model.Mathematical model is crictical for optimization design and system analysis.A low-frequency model is built for the research purpose of control on driving smoothness and fuel efficiency,aimed at front-rear dual clutch single-axis parallel hybrid electric coach.By means of both forward and backward simulation,it is able to simulate transient characteristics of vehicle operation,and evaluate vehicle’s fuel efficiency.Parameters of critical components are calibrated in the transient model.The accuracy of model simulation is less than 5.7%,which meets the needs of the development of control strategy.Establishing a control algorithm to reduce the influence of torsional vibration on driving and power quality by analyzing the causes of torsional vibration and finding the law of amplitude change,which is the difficult problem to resolve for plug-in hybrid vehicles.In this thesis,the frequency band of vibration is analyzed based on the two-order high pass filter.A band stop filter for vehicle control is established,and the torque control is realized to improve the power quality.Besides,the delay caused by CAN communication in vehicle control system leads to instability in damping control.To tackle this problem,a prediction control strategy based on Lyapunov Therom and CAN bus delay distribution is introduced.The simulation results show a noticeable decrease in torsion amplitude,compared with present active PID control,shock duration decreased by 68.42%.The energy consumption algorithm of hybrid vehicles relies on the analysis of the driving cycle.The optimization algorithm based on driving cycle to improve fuel economy is a challenge in real-time control of on-board controller.This thesis proposes a route-based control to achieve global energy management optimization.Focusing on improving fuel efficiency,it is implemented to assist on-board controller in real-time control.ECMS control theory and minimum principle are the theoretical foundation of optimization analysis.Vehicle position track is taken as a reference in this optimization.It optimizes HEV energy management strategy.Another optimization algorithm is built for route recognition and energy management based on route control.Furthermore,by means of on-board communication technology,a real-time algorithm is established for on-board controller to take in key control parameters in global control optimization.The algorithm,which combines remote global optimization and local real-time on-board control,optimizes torque distribution,engine ON/OFF state and shift strategy.The algorithm is able to solve global optimization algorithm’s inadequacy to achieve real-time on-board control.In order to validate the proposed control method,a validation study on the designed control method is conducted to test its effectiveness in improving driving smoothness and fuel efficiency.A software control frame is developed for PHEV.Algorithms investigated in this thesis are integrated in controllers.Both bench test and road test are conducted for the designed strategy.The test result manifests the superiority of the strategy in convergence rate concerning driving smoothness.Under pure-electric mode,amplitude of motor rotating speed decreases by 50%,with faster convergence rate.Additionally,fuel efficiency under the control strategy improves by 11.60%,compared with regular control method.