Research On Vehicle Driving Parameters Recognition Algorithm Based On Multi-source Information Fusion

With the rapid development of smart cars,people have higher and higher requirements for vehicle stability control systems.Accurate estimation of vehicle driving state parameters is the key to achieving vehicle stability control,and it is also a hot spot in vehicle dynamics control research.Vehicle mass center slip angle,tire cornering stiffness,longitudinal speed,lateral speed,and yaw rate are of great significance to the realization of vehicle stability control.Among them,the center of mass slip angle and tire cornering stiffness can only be predicted and estimated by on-board sensor signals.However,although the longitudinal and lateral speed and yaw rate of the vehicle can be directly measured by the sensor,a single sensor is interfered by noise,and there are problems of error accumulation and poor stability.In order to accurately estimate the above-mentioned vehicle state parameters,this paper uses IMU and GPS sensors to design a fusion estimation algorithm based on kinematics and dynamics models.The specific research contents are as follows:First of all,the traditional kinematics estimation method relies on the information of a single sensor,which is greatly interfered by noise,which causes the accumulation of estimation parameter errors.In response to this problem,the article integrates IMU and GPS information under the framework of Kalman filtering,and combines the kinematics model of the vehicle to achieve accurate estimation of vehicle speed and position information.The MATLAB simulation verification proves the effectiveness of the algorithm.Then,the vehicle state parameter estimation method based on the dynamic model is studied.Aiming at the problem that the dynamic estimation method requires high vehicle model,the article uses the least square method to estimate the vehicle tire cornering stiffness,and then uses the longitudinal acceleration,lateral acceleration and steering wheel angle measured by the sensor as input,and takes the vehicle three Based on the degree of freedom dynamics model,the extended Kalman filter method is used to estimate the vehicle’s yaw rate,longitudinal velocity,and side slip angle of the center of mass.Through Simulink and Carsim co-simulation,the effectiveness of the estimation algorithm is verified.Finally,combining the advantages and disadvantages of kinematics and dynamics parameter estimation,a fusion estimation method of vehicle centroid side slip angle is proposed.The algorithm assigns the respective weight coefficients of the two methods according to the dynamic characteristic strength of the centroid side slip angle,so as to more accurately estimate the vehicle centroid side slip angle.The article uses the laboratory wire-controlled electric vehicle as a test platform to construct a multi-source sensor test system,which realizes the functions of data collection and vehicle parameter estimation,and verifies the effectiveness of the fusion algorithm.