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Analysing vehicle dynamics objective and subjective testing in winter conditions
Volvo Cars.ORCID iD: 0000-0002-6699-1965
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Vehicle Dynamics.ORCID iD: 0000-0002-2265-9004
Volvo Cars.
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Vehicle Dynamics.ORCID iD: 0000-0001-8928-0368
2016 (English)In: The Dynamics of Vehicles on Roads and Tracks: Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics, IAVSD 2015, Taylor & Francis Group, 2016, 759-768 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a test procedure developed to gather good quality data from objective and subjective testing on winter conditions. As the final goal of this test is to analyse the correlation between objective metrics and subjective assessments on winter for steering and handling, this procedure has to ensure a minimum change of the surface properties, which has a major influence on vehicle performance, during the whole test campaign. Therefore, the method presented keeps the total test time very low and allows similar vehicle configurations to be test- ed, objectively and subjectively, very close in time. Moreover, continuous maintenance work on the ice is performed. Reference vehicles are also used to monitor the changes on vehicle per- formance caused by weather conditions, which are inevitable. The method showed to be very effective. Initial results on objective metrics and subjective assessments are also presented. 

Place, publisher, year, edition, pages
Taylor & Francis Group, 2016. 759-768 p.
Keyword [en]
Subjective testing, System theory, Vehicles, Continuous maintenance, Objective metrics, Subjective assessments, Test campaign, Test procedures, Vehicle configuration, Vehicle dynamics, Winter conditions, Vehicle performance
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
URN: urn:nbn:se:kth:diva-181060DOI: 10.1201/b21185-81ISI: 000385792300079ISBN: 9781138028852 (print)ISBN: 978-1-4987-7702-5 (print)OAI: oai:DiVA.org:kth-181060DiVA: diva2:898267
Conference
24th Symposium of the International Association for Vehicle System Dynamics, IAVSD 2015, Graz, Austria, 17 August 2015 - 21 August 2015
Projects
iCOMSA
Funder
VINNOVA, 2012-04609TrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20160404

Available from: 2016-01-27 Created: 2016-01-27 Last updated: 2017-05-03Bibliographically approved
In thesis
1. Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
Open this publication in new window or tab >>Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Vehicle dynamics development is strongly based on subjective assessments (SA) of vehicle prototypes, which is expensive and time consuming. Consequently, in the age of computer- aided engineering (CAE), there is a drive towards reducing this dependency on physical test- ing. However, computers are known for their remarkable processing capacity, not for their feelings. Therefore, before SA can be computed, it is required to properly understand the cor- relation between SA and objective metrics (OM), which can be calculated by simulations, and to understand how this knowledge can enable a more efficient and effective development process.

The approach to this research was firstly to identify key OM and SA in vehicle dynamics, based on the multicollinearity of OM and of SA, and on interviews with expert drivers. Sec- ondly, linear regressions and artificial neural network (ANN) were used to identify the ranges of preferred OM that lead to good SA-ratings. This result is the base for objective require- ments, a must in effective vehicle dynamics development and verification.

The main result of this doctoral thesis is the development of a method capable of predicting SA from combinations of key OM. Firstly, this method generates a classification map of ve- hicles solely based on their OM, which allows for a qualitative prediction of the steering feel of a new vehicle based on its position, and that of its neighbours, in the map. This prediction is enhanced with descriptive word-clouds, which summarizes in a few words the comments of expert test drivers to each vehicle in the map. Then, a second superimposed ANN displays the evolution of SA-ratings in the map, and therefore, allows one to forecast the SA-rating for the new vehicle. Moreover, this method has been used to analyse the effect of the tolerances of OM requirements, as well as to verify the previously identified preferred range of OM.

This thesis focused on OM-SA correlations in summer conditions, but it also aimed to in- crease the effectiveness of vehicle dynamics development in general. For winter conditions, where objective testing is not yet mature, this research initiates the definition and identifica- tion of robust objective manoeuvres and OM. Experimental data were used together with CAE optimisations and ANOVA-analysis to optimise the manoeuvres, which were verified in a second experiment. To improve the quality and efficiency of SA, Volvo’s Moving Base Driving Simulator (MBDS) was validated for vehicle dynamics SA-ratings. Furthermore, a tablet-app to aid vehicle dynamics SA was developed and validated.

Combined this research encompasses a comprehensive method for a more effective and ob- jective development process for vehicle dynamics. This has been done by increasing the un- derstanding of OM, SA and their relations, which enables more effective SA (key SA, MBDS, SA-app), facilitates objective requirements and therefore CAE development, identi- fies key OM and their preferred ranges, and which allow to predict SA solely based on OM. 

Place, publisher, year, edition, pages
Stockholm: Kungliga tekniska högskolan, 2017. 72 p.
Series
TRITA-AVE, ISSN 1651-7660 ; 2017:12
Keyword
Steering feel, vehicle handling, driver preference, objective metrics, subjective assessments, regression analysis, artificial neural network
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-202348 (URN)978-91-7729-302-6 (ISBN)
Public defence
2017-03-17, D3, Lindstedsvägen 5, Stockholm, 10:00 (English)
Opponent
Supervisors
Projects
iCOMSA
Funder
VINNOVA, 2012-04609
Note

QC 20170223

Available from: 2017-02-23 Created: 2017-02-21 Last updated: 2017-02-23Bibliographically approved

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