A study on the vehicle state estimation for a distributed-drive EV based on LO-EKF algorithms

Cheng Lin*, Gang Wang, Wanke Cao, Fengjun Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A study on vehicle state estimation is conducted for a distributed-drive electric vehicle in this paper. Firstly Luenberger observer (LO) is adopted to observe the road slope, which has significant effects on vehicle state estimation. Then extended Kalman filter (EKF) algorithm is used with the data information obtained from ESP sensor as observed value, the dynamics state variables of distributed-drive electric vehicle are estimated. Finally a Carsim-Matlab co-simulation is performed. The results show that the proposed vehicle state estimation algorithm based on LO and EKF is feasible and can well estimate the relevant dynamics state variables of vehicle with a rather high convergence speed.

Original languageEnglish
Pages (from-to)1316-1320
Number of pages5
JournalQiche Gongcheng/Automotive Engineering
Volume36
Issue number11
Publication statusPublished - 25 Nov 2014

Keywords

  • Distributed-drive electric vehicle
  • Extended Kalman filter
  • Luenberger observer
  • Road slope observation
  • Vehicle state estimation

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