Int. J. of Electric and Hybrid Vehicles   »   2017 Vol.9, No.2

 

 

Title: State estimation of four-wheel independent drive electric vehicle based on adaptive unscented Kalman filter

 

Authors: Bin Huang; Sen Wu; Xiang Fu; Jie Luo

 

Addresses:
Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
School of Automation, Wuhan University of Technology, Wuhan 430070, China

 

Abstract: In this paper, an algorithm using adaptive unscented Kalman filter (AUKF) to estimate four-wheel independent drive (4WID) electric vehicle key states is proposed. The algorithm estimates unknown noise by use of the modified Sage-Husa noise statistic estimator. Its recursive form is combined with unscented Kalman filter (UKF) algorithm for real-time estimation and correction of noise statistic property in filtering process so as to reduce the error in state estimation. The non-linear vehicle dynamics system which contained constant/time-variable noise and four degrees of freedom, including longitudinal, lateral, yaw and rolling motion is established. The estimator based on AUKF is compared with that based on UKF. The results of virtual experiments by using both Simulink and Carsim software and real vehicle experiments demonstrate that the AUKF-based algorithm can estimate quite accurately the key driving state parameters of 4WID electric vehicle.

 

Keywords: state estimation; 4WID; four-wheel independent drive; electric vehicle; AUKF; adaptive unscented Kalman filter; noise statistic property.

 

DOI: 10.1504/IJEHV.2017.10006173

 

Int. J. of Electric and Hybrid Vehicles, 2017 Vol.9, No.2, pp.151 - 168

 

Submission date: 21 Oct 2016
Date of acceptance: 05 Mar 2017
Available online: 11 Jul 2017

 

 

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