Date of Award

Fall 2021

Publication Type

Thesis

Degree Name

M.A.Sc.

Department

Mechanical, Automotive, and Materials Engineering

First Advisor

Y. Kim

Second Advisor

J. Ahmed

Third Advisor

J. Johrendt

Keywords

DiM 250, Dynamic Driving Simulator, Ride and Handling, Semi-Active Suspension, Subjective Evaluation, Suspension

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The number of passenger cars currently equipped with semi-active suspensions has been steadily increasing in recent decades. These suspension systems provide an improvement in ride and handling when compared to passive suspensions. Currently, the approach to evaluating and tuning semi-active suspensions has been limited to objective methods or time-consuming alterations made on physical components. To alleviate the time and costs and improve the fidelity of such methods, a novel solution to subjectively evaluating vehicle semi-active suspensions is presented. The subjective evaluation method herein involves the use of a state-of-the-art dynamic driving simulator with drivers to subjectively evaluate and tune virtual semi-active suspensions.

To consider the results of the proposed evaluation method accurate, high-fidelity vehicle models supplied by an OEM are studied. These vehicle models have previously been validated with objective and subjective performance data by an OEM’s expert drivers. First, offline co-simulations between VI-grade’s CarRealTime vehicle simulation software and several versions of a Simulink semi-active suspension controller are completed to objectively evaluate ride and handling. The semi-active suspension controller is based on several well-known control strategies and incorporates the vehicle’s passive suspension settings as one of the suspension modes. This feature permits a comparison between the passive and semi-active suspensions in terms of ride and handling.

For the subjective evaluation, the vehicle and controller models are uploaded in a driver-in-the-loop environment. Expert drivers then execute a series of maneuvers and provide subjective feedback on the ride and handling of the different suspension modes. A questionnaire is implemented involving a list of subjective metrics tailored for ride and handling of semi-active suspensions. Furthermore, a correlation between changes in objective and subjective metrics is made to determine where correlation exists and to suggest predictive methods for future subjective ratings. A specific evaluation procedure is presented to ensure a bias among drivers is removed.

The results of the subjective evaluation method prove that the method is effective at capturing relatively small changes in ride and handling, in a timely manner. The subjective ratings from the drivers showed acceptable agreement and considered many ride and handling improvements as major differences according to SAE standards. The correlation study identified a list of strong correlations between objective and subjective metrics. These results can be used to predict subjective performance when implementing offline changes to suspensions.

Share

COinS