Vehicle state and tire parameter estimation based on improved particle filter based on genetic algorithm

Independent tire dynamics parameters are often determined using expensive test equipment and large amounts of test data, after which these parameters are applied in the design phase to a vehicle model to simulate the vehicle’s handling dynamics behavior. The use of particle filtering techniques enables the estimation of tire parameters and can eventually lead to the estimation of parameters such as vehicle lateral forces. The advantage of genetic algorithm in selection inheritance is used to complement the degradation of particle weights and the lack of particle diversity in particle filter resampling to achieve high accuracy identification of tire parameters.