Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm

Liang Hao, Lixin Guo and Shuwei Liu

Mathematical Problems in Engineering, 2018, vol. 2018, 1-9

Abstract:

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3106329

DOI: 10.1155/2018/3106329

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