Vehicle tire parameter and state estimation under driving situation based on auxiliary particle filter method

Individual tire magic formula parameter is traditionally derived from expensive equipment in laboratory which needs a large number of experimental data. And then the parameter is transferred to vehicle model at a design stage to simulate the vehicle handling behavior. The main source of uncertainty in this type of models lies in the tire-road interaction due to high nonlinearity. Proper estimation of tire model parameters is important for obtaining reliable results. A vehicle dynamics system containing constant noise and non-linear model was established, and the Runge-Kuttta method was used to simulate the model. The parameters were estimated by using auxiliary particle filter through two rounds weighted processes, and the vehicle dynamic parameters such as tire lateral forces could be estimated by using the parameters estimated before. Meanwhile, the field test was done. The measurements under several standard handling maneuvers (step-steer, double-lane-change, etc.) were presented, and the results showed that the proposed algorithm improved the accuracy of standard particle filter.