Inlämning av Examensarbete / Submission of Thesis

Zuohai Yan; Shuqi Zhao , pp. 40. ING/School of Engineering, 2012.

The work

Författare / Author: Zuohai Yan, Shuqi Zhao
Titel / Title: Road Condition Predicting with Kalman Filter for Magneto-Rheological Damper in Suspension System
Abstrakt Abstract:

This thesis develops a new way to predict the road roughness with Kalman filter. It suggests applying the Kalman filter to predict road condition in suspension system. According to the literature review and to the knowledge of authors, no similar applications of Kalman filter in predicting the road roughness are found at the time of the writing thesis. Most of the prediction nowadays is around the road prediction with GPS. It concentrates on avoiding the road bumps by the operator. This research is brand new in this field. What the authors focus on is to predict the road condition and to pass this information to the control system. By this way, the passenger comfort is improved.

This research is practical in transportation industry. Nowadays the passenger comfort is crucial. This road condition predictor can help the vehicle to improve the passenger comfort. Furthermore, this predictor can be adjusted to different road conditions.

A suspension system is important to improve passenger comfort. Magneto-Rheological(MR) damper, which is a controllable damper, can improve the performance of the suspension system. This thesis presents a menthod to predict the road condition for MR damper. Firstly, three suspension systems, passive, active and semi-active suspension systems, are evaluated by their costs and performances. The semi-active suspension system has good performance with low cost. This suspension system shows better performance with proper control strategy.

Additionally, two different levels of road roughness are simulated by Harmonic superposition method in time domain. One of the road roughness scenarios is chosen to test the prediction method. The road roughness is predicted by a Kalman filter. The result shows that the Kalman filter can estimate the road condition with a high accuracy. The prediction frequency is high in this method. The control strategy can adjust its coefficient based on the high prediction frequency. Thus, the performance of the suspension system is enhanced and the passenger comfort is also improved.

Ämnesord / Subject: Signalbehandling - Signal Processing

Nyckelord / Keywords: Suspention System, Prediction, Kalman Filter

Publication info

Dokument id / Document id: houn-8y8dga
Program:/ Programme Magisterprogram i Elektroteknik / Master of Science in Electrical Engineering
Registreringsdatum / Date of registration: 09/17/2012
Uppsatstyp / Type of thesis: Masterarbete/Master's Thesis (120 credits)

Context

Handledare / Supervisor: Feng Wang
feng.wang@bth.se
Examinator / Examiner: Sven Johansson
Organisation / Organisation: Blekinge Institute of Technology
Institution / School: ING/School of Engineering

+46 455 38 50 00
Anmärkningar / Comments:

0764534242

Files & Access

Bifogad uppsats fil(er) / Files attached: bth2012yanzhao.pdf (940 kB, öppnas i nytt fönster)