吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 1329-1335.doi: 10.13229/j.cnki.jdxbgxb201705001

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Estimation of vehicle state and parameter based on strong tracking CDKF

LI Jing1, ZHANG Jia-xu1, 2, ZHANG Yan-hua1, CHEN Li-jun3   

  1. 1.State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
    2.Research and Development Center, China FAW Group Corporation, Changchun 130011, China;
    3.Big Data and Network Management Center, Jilin University, Changchun 130022,China
  • Received:2016-08-16 Online:2017-09-20 Published:2017-09-20

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Abstract: A 3-DOF nonlinear dynamic model of vehicle is established as nominal model of vehicle state and parameter estimation. Strong tracking Central Difference Kalman Filter (CDKF) is adopted to simultaneously estimate the longitudinal velocity, side slip angle and yaw rate of the vehicle and the equivalent lateral stiffness of the axle. In order to verify the performance of the strong tracking CDFK algorithm, typical cases using model-in-the-loop simulation systems are carried out. Simulation results show that the algorithm is effective and can get high accuracy.

Key words: vehicle engineering, vehicle dynamics, time-varying model parameters, strong tracking central difference Kalman filter, states and parameters estimation

CLC Number: 

  • U461
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