State And Parameters Estimation Of Distributed Drive Electric Vehicle Based On Federal Kalman Filter

Vehicle dynamics control has always been a research hotspot in vehicle industry,and the resulting active safety technology is more and more widely used in vehicle.How to obtain driving state information of vehicle accurately and stably in real time is prerequisite for active safety control technology.Therefore,the research on estimation of vehicle driving state has attracted more and more attention of scholars.Because road and vehicle parameters information will have a greater impact on driving state estimation,and distributed drive electric vehicles with four-wheel independent drive and steering are an important development direction in the future.In this paper,a distributed drive electric vehicle is taken as the research object.In view of its unique dynamic control characteristics and advantages of multiple information sources,considering influence of various uncertain factors such as road and vehicle parameters.A vehicle state and parameters estimation algorithm based on Federal Kalman Filter is proposed to realize driving state,road friction coefficient and vehicle parameters of the distributed drive electric vehicle and provide a basis for vehicle dynamics control.The research on state and parameters estimation of distributed driving electric vehicles is carried out based on the Natural Science Foundation of China(51675257)"Study on dynamics control for four wheel independent drive and steering electrical vehicle considering driver characteristic" and the Project of Liaoning Province Innovative Talents(LR2016054)"New energy vehicle simulation and control".The main work is as follows:(1)Considering the degrees of freedom of distributed driving electric vehicles in longitudinal,lateral and yaw,the vehicle dynamics model and Dugoff tire model are established.The sensors information of whole vehicle is obtained through whole vehicle network.The vehicle driving state estimator is designed based on the Federal Kalman Filter theory using multi-information fusion method.Through off-line simulation verification,the estimation of longitudinal speed,lateral speed and sideslip angle of mass center is realized accurately during driving.(2)In order to improve adaptability of the estimation algorithm to different roads and further improve its accuracy and stability,considering the impact of road friction coefficient on vehicle driving state estimation,The road friction coefficient estimator is designed based on Federal Kalman Filter theory,and the two estimators are connected to form a dual Federal Kalman Filter estimator.Through off-line simulation verification,the joint estimation of vehicle driving state and road friction coefficient is realized accurately.(3)Because parameters of vehicle itself(such as mass,moment of inertia and position of mass center)constantly change during vehicle driving,which has a direct impact on vehicle driving state estimation,so it is particularly important to consider the impact of vehicle parameters.On the basis of the dual Federal Kalman Filter,the vehicle parameters estimator is designed based on the theory of Federal Kalman Filter.Through off-line simulation verification,the joint estimation of driving state,road and vehicle parameters of distributed drive electric vehicle is realized accurately.(4)Using the driving simulator hardware-in-the-loop experimental platform to verify the algorithm of state and parameters estimation of distributed drive electric vehicle based on the Federal Kalman Filter.The results show that the proposed estimation algorithm based on the Federal Kalman Filter can accurately and stably estimate the state and parameters of distributed driving electric vehicle.