Next Article in Journal
A Two-Stage Approach to Note-Level Transcription of a Specific Piano
Previous Article in Journal
A Review of Distributed Fibre Optic Sensors for Geo-Hydrological Applications
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(9), 898; doi:10.3390/app7090898

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

Product Development Methods and Mechatronics, Technical University of Berlin, 10623 Berlin, Germany
*
Author to whom correspondence should be addressed.
Received: 21 July 2017 / Revised: 24 August 2017 / Accepted: 29 August 2017 / Published: 1 September 2017
(This article belongs to the Section Mechanical Engineering)
View Full-Text   |   Download PDF [12936 KB, uploaded 20 September 2017]   |  

Abstract

The effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today’s typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence. View Full-Text
Keywords: unscented Kalman filter; moving horizon estimation; vehicle state estimation; distributed drive electric vehicle unscented Kalman filter; moving horizon estimation; vehicle state estimation; distributed drive electric vehicle
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Göhlich, D.; Fu, C. Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles. Appl. Sci. 2017, 7, 898.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top