Estimation Of Road Adhesion Coefficient Of Electric Vehicle Based On Data Fusion

In recent years,the energy crisis and environmental problems caused by traditional fuel vehicles appear increasingly,and the active safety of vehicles has attracted much attention.In this context,distributed drive electric vehicle is considered to be one of the effective solutions to these problems.Distributed drive electric vehicles have compact structure,short transmission chain and high transmission efficiency.The driving torque and braking torque of the driving wheel can be independently adjusted and measured.Compared with the traditional fuel vehicle,it has great advantages in driving,braking,steering and controllability,and the potential of improving the active safety performance of the vehicle is much greater than that of the traditional fuel vehicle.As an important part of the vehicle safety system,the electronic stability system ESC and automatic emergency braking system AEB play an important role in obtaining the adhesion coefficient of the road surface.The estimation of the adhesion coefficient of the road surface can be regarded as one of the core issues whether the vehicle active safety system can effectively protect the passenger safety.Therefore,it is of great practical significance to study the road adhesion coefficient of distributed drive electric vehicles.Based on the vehicle dynamic response and Dugoff tire model,an adhesion coefficient estimator was built based on the data fusion theory.According to the characteristics of vehicle driving conditions,the road adhesion coefficient estimation method under straight driving conditions and steering conditions was proposed.The details are as follows:Firstly,the dynamics of distributed drive electric vehicle was analyzed,and the seven-degree-of-freedom dynamics model and Dugoff tire model were constructed in the Matlab-Simulink environment.The characteristics of permanent magnet synchronous motor(PMSM)were analyzed,and the driving system model of four wheel hub motor was established.On this basis,the vehicle steering condition simulation test is set up to analyze and verify the effectiveness of the model.Combining the dynamic response of the electric vehicle on the road and on the road with the Dugoff tire model formula,Based on the maximum a posteriorestimation(MAP)principle and observation information,the statistical characteristics of the measured Noise were estimated online and embedded into the Cubature Kalman filter CKF to construct the adaptive volume Kalman filter NACKF pavement adhesion coefficient estimator.The experimental results show that the estimation accuracy and tracking adaptability of the proposed NACKF pavement adhesion coefficient estimator are better than CKF and Unscented Kalman filter.