State Estimation of Four-wheel Independent Drive Electric Vehicle Based on Adaptive Unscented Kalman Filter

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.

• Record URL:
  https://doi.org/10.1504/IJEHV.2017.085348
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17514088
• Supplemental Notes:
  • Copyright © 2017 Inderscience Enterprises Ltd.
• Authors:
  • Huang, Bin
  • Wu, Sen
  • Fu, Xiang
  • Luo, Jie
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 151-168
• Serial:
  • International Journal of Electric and Hybrid Vehicles
  • Volume: 9
  • Issue Number: 2
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1751-4088
  • EISSN: 1751-4096
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=ijehv

Subject/Index Terms

• TRT Terms: Algorithms; Electric vehicles; Four wheel drive; Kalman filtering; Real time information; Rolling; Steady state; Traffic noise; Yaw
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01644939
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 1 2017 10:29AM