Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm

الملخص EN

Vehicle running state adaptive unscented Kalman filter soft-sensing algorithm is put forward in this paper based on traditional UKF which can estimate vehicle running state parameters and suboptimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time.

Meanwhile 3-DOF dynamic model of vehicle and HSRI tire model are established.

So vehicle running state can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and handwheel steering angle.

Under the typical working condition, AUKF soft-sensing algorithm is verified with substantial vehicle tests.

Comparing with UKF soft-sensing algorithm, the result indicates AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of vehicle running state more precisely than UKF soft-sensing algorithm.