Vehicle States And Parameters Estimation Methods And Scaled Vehicle Test Verification

Vehicle states and parameters are crucial to the active safety system.Accurate identification of these states and parameters can greatly improve vehicle handling stability,safety and fuel efficiency.Therefore,the identification of vehicle status and parameter identification is of great significance to vehicle safety performance and maneuverability stability.By verifying method with the scaled vehicle,the risk and cost of the real vehicle experiment can be solved.Firstly,the scaled vehicle test platform is designed,the central processing control module and the on-board terminal power conversion module are designed.Protocol analysis of the GPS system required for the test is completed,and the positioning system configuration is carried out to enable it to collect various state parameters required for the test.Based on the similarity theory,the dynamic similarity between scaled vehicle test platform and real vehicle is explored,and the dynamic matching between scaled vehicle test platform and real vehicle is realized.Parameters of the test platform of the scaled vehicle were collected through experiments.Parameters of the tire lateral stiffness of the scaled vehicle that could not be measured were identified,and the accuracy of the lateral lateral stiffness was verified.Accurate and real-time access to road adhesion information is the premise of the active safety control system.The surface roughness and wet condition have great influence on lateral stiffness.First,the lateral force and slip angles of front and rear axle are obtained from the two-degree-of-freedom vehicle model.Considering the load transfer,the vertical tire vertical force force is obtained.By the difference between the front and rear wheel sideslip angles,the sideslip angle which is more difficult to get is eliminated.Finally the normalized tire lateral stiffness was estimated by the recursive least square method,and the difference of the estimated results on different roads are compared.The proposed algorithm is verified through simulation and scaled vehicle road test.Finally,the estimation algorithm for vehicle mass and road slope is presented.The vehicle mass and road slope are estimated by a two layer structure estimation according to changing speed.The slowly changed vehicle mass is taken as the estimated output of the first layer,and the rapidly changed road slope is taken as the estimated output of the second layer.The estimation algorithm is based on longitudinal dynamics.Firstly,the vehicle mass and slowy changed road slope of the first layer is estimated through EKF based on tire driving moment and vehicle velocity,and then the estimated vehicle mass is substituted into the second-layer method for fast changed road slope estimation.The algorithm is verified through simulation and scaled vehicle test,and the simulation and test results show that the proposed identification algorithm can accurately and real-time estimate vehicle mass and road slope.