Low frequency longitudinal vibration of the vehicle refers to the low-frequency(0-10 Hz)vibration of the vehicle in the front-rear direction,which has a bad effect on the drivability and comfort of the vehicle.The low frequency longitudinal vibration of the vehicle has a great relationship with the working conditions of the vehicle,and it has a strong vibration in the event of Tip-in/out and start-up and so on.The application of new hybrid electric vehicles has brought new problems to the low frequency longitudinal vibration of the vehicle.Different powertrain types,different power modes and working conditions all have effects on low frequency longitudinal vibration of the vehicle.Therefore,the study of low frequency longitudinal vibration of vehicles is significant to enhance vehicle performance.In this paper,a traditional front wheel drive vehicle with a mechanical gearbox and two types of modified hybrid electric vehicle(HEV)are taken as the research objects.Based on the Model Predictive Control and multiple-Model Predictive Control strategies,a multiple working conditions integrated control algorithm for every vehicle under research is designed for low frequency longitudinal vibration control.The system uncertainty,the actuator time delay,the multiple working conditions,the multiple power modes are all considered in the controller.Based on the dynamic analysis of the system and the requirement of the verification of the control functions,the mechanical vehicle powertrain and the chassis system is modeled to simulate the low frequency dynamics of the vehicle.By comparing different simulation results of various scale subsystem models,the suitable subsystem models are selected.Under some uncertain conditions simulation,the main problems in low frequency longitudinal vibration control are analyzed and summarized.For traditional vehicles,firstly the longitudinal vibration controller under tip-in/out working conditions is designed when clutch is engaged.Two MPC longitudinal controllers with and without considering the engine delay are compared.The simulation results show that the vibration control effect with considering the engine delay is greatly improved,and it also has good control effect under the influence of uncertain factors such as road slope and clearance.Then,the longitudinal vibration controller under start up and tip-in working conditions is designed when clutch is slipping.By considering both statuses of clutch,slipping and engaged,the uniform multi-model state space equations are built.Based on the model,an mMPC longitudinal vibration controller is designed.The simulation results show that the algorithm can dynamically adjust the clutch slip process based on the driver’s intention,and can effectively inhibit the vehicle low frequency vibration.Two types of HEV are selected for the study.One is called BAS(Belted Alternator Starter)HEV,which has an alternator starter connected in front of engine crankshaft by belt.The other is called TTRP(Through-the-road parallel)HEV,which has an engine driven axle and a motor driven axle.Based on the analysis of the influences of these two HEVs on the low frequency longitudinal vibration,including power modes and power distributions,the low frequency longitudinal vibration controller for each HEV is designed by using MPC or mMPC strategy.The results show that the design of the controllers can achieve good control effect under different power modes and power distributions.As the traditional MPC or mMPC algorithm using implicit QP solution method consumes too much computing time,it is difficult to use in application as vehicle powertrain dynamic control systems.In order to make the low frequency longitudinal vibration control algorithm based on MPC or mMPC strategy have better real-time operation ability,this paper studies the explicit solution for the longitudinal vibration controller based on the explicit model predictive control method.In order to further verify the real-time computing ability of the explicit low-frequency longitudinal vibration control algorithms,they are downloaded to the rapid control prototyping device and connected to the hardware in the loop equipment for real-time simulation.The simulation results show that the running time of the explicit algorithm in real-time environments is short enough and can be applied to vehicle powertrain dynamic control systems. |