Research On The Algorithm Of The Road Friction Coefficient Estimation Based On The EMD Algorithm

With the development of the science and technology and people's material living standard, people have more and more requests on the safety of vehicles and stability of the road. Especially when the rate of traffic accidents was obviously on the rise, which are brought about by the changes in the tire performance along with changes in the weather conditions and roads conditions. Facts have improved that on the way of vehicle running, detection on tire safety operation and road adhesion can play an effective supporting role in driving. The detection can also ensure that the traffic environment develops safely and harmoniously. At the same time, it can reduce the number of traffics accidents and assure the safety of roads transport.Through adjusting the movement of vehicles by the vehicle control system simultaneously, Vehicle Stability Control can make vehicles move in accordance with the intention of the drivers and prevent instability. So the Vehicle Stability Control is active vehicle safety device which is also the hot spot studying in the field of international automotive active safety.As an important parameter for the stability of the vehicles and the road running perception, road adhesion coefficient is difficult to be measured because of the cost and the accuracy of the measurement. Based on the intelligent tire technology, this paper puts forward to a road adhesion parameter estimation method and makes use of it to study the adhesion parameter identification system for highway conditions.1. Based on the nonlinear 3-DOF vehicle dynamics model,estimation algorithm of vehicles movement state on the using of vehicle sensor information have been studied and the extended Kalman state observer has been designed. The longitudinal , .lateral acceleration and yaw rate of the state of the vehicle can be estimated online. Vehicle state estimation algorithm in Matlab/simulink has been achieved,And the vehicle state estimation algorithm has been experimental verificated passing the real vehicle test. The results proved that,the accuracy of the state of the vehicle movement based on the extended EMD algorithm. It establishes a firm basis for the estimation algorithm of road/tire coefficient in the next chapter.2. Through the magic formula tire,the unitire model and the Dugoff tire model comparison and analysis,Dugoff tire model has been determined toapplicate into the research of road/tire coefficient estimation algorithm,and the basic guarantee its accuracy requirements was satisfied by a group of empirical parameters. At the same time,a simulation verification of the tire Characteristics of the Dugoff tire model have been made using the software CarSim7.0.A four vehicle dynamics model has been constructed,Road/tire coefficient estimation algorithm has been established based on the EMD algorithm,and achieved in Matlab/Simulink environment.3. On the basis of the conclusion of the above study,a preliminary idea to combine vehicle state estimation algorithm and road/tire coefficient estimation algorithm based on the EMD algorithm into a complete estimation algorithm,therefore,using of a state vehicle signal to estimate road/tire coefficient,and the estimation algorithm has been achieved in the Matlab/simulink environment. Vehicle speed,yaw rate,road/tire coefficient of state estimation and parameters have been completed using CarSim7.0 in the high-adhesion,low attachment,docking,three off the road situation. The results show that simulation results are in good agreement,this fully explained the accuracy of the road/tire coefficient estimation algorithm based on the EMD algorithm.To sum up, based on intelligent tire system and EMD algorithm theory, this paper have a research on the vehicle's state estimation and the road adhesion coefficient estimation algorithm. Apart from that, this paper also has a real vehicle test on state estimation, sets out a simulation on the road adhesion estimation algorithm and gets a satisfied result, which provides a new way for the vehicle estimation state and road adhesion coefficient.