Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (1): 70-76.DOI: 10.12068/j.issn.1005-3026.2019.01.014

• Mechanical Engineering • Previous Articles     Next Articles

Vehicle Driving State Estimation of the Adaptive Soft-Sensing Algorithm

HAO Liang1,2, GUO Li-xin1   

  1. 1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China; 2. Automobile & Traffic Engineering College, Liaoning University of Technology, Jinzhou 121001, China.
  • Received:2017-04-29 Revised:2017-04-29 Online:2019-01-15 Published:2019-01-28
  • Contact: HAO Liang
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Abstract: The low-cost measurement of vehicle driving states is realized by establishing an algorithm based on the traditional unscented Kalman filter(UKF) which can estimate vehicle driving state parameters and the sub-optimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time. Meanwhile three-degree-of-freedom(3-DOF) dynamic model of electrical vehicles and highway safety research institute(HSRI) tire model are established. Accordingly, electrical vehicle driving states can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and handwheel steering angles. Under the selected typical working condition, the adaptive unscented Kalman filter(AUKF) soft-sensing algorithm is compared with the UKF soft-sensing algorithm, and the hardware-in-the-loop(HIL) testing platform result indicates the AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of electrical vehicles’ driving state more precisely than the UKF soft-sensing algorithm.

Key words: AUKF soft-sensing algorithm, sub-optimal Sage-Husa noise estimator, three-degree-of-freedom dynamic model, highway safety research institute tire model, hardware-in-the-loop

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