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Analysis and optimisation of objective vehicle dynamics testing in winter conditions
Volvo Cars.ORCID-id: 0000-0002-6699-1965
SEGULA Technologies.
Volvo Cars.
KTH, Skolan för teknikvetenskap (SCI), Farkost och flyg, Fordonsdynamik.ORCID-id: 0000-0002-2265-9004
Visa övriga samt affilieringar
2017 (Engelska)Ingår i: Vehicle System Dynamics, ISSN 0042-3114, E-ISSN 1744-5159, Vol. 55, nr 7, s. 945-969Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Objective testing of vehicle handling in winter conditions has not been implemented yet because of its low repeatability and its low signal-to-noise ratio. Enabling this testing, by identifying robust manoeuvres and metrics, was the aim of this study. This has been achieved by using both experimental data, gathered with steering-robot tests on ice, and simulation models of different complexities. Simple bicycle models with brush and MF-tyre models were built, both optimally parameterised against the experimental data. The brush model presented a better balance in complexity performance. This model was also implemented in a Kalman filter to reduce measurement noise; however, a simpler low-pass filter showed almost similar results at lower cost. A more advanced full vehicle model was built in VI-CarRealTime, based on kinematics and compliance data, damper measurements, and real tyre measurements in winter conditions. This model offered better results and was therefore chosen to optimise the initial manoeuvres through test design and simulations. A sensitivity analysis (ANOVA) of the experimental data allowed one to classify the robustness of the metrics. Finally, to validate the results, the proposed and the initial manoeuvres were tested back to back in a new winter campaign.

Ort, förlag, år, upplaga, sidor
Taylor & Francis, 2017. Vol. 55, nr 7, s. 945-969
Nyckelord [en]
Objective testing, vehicle handling, vehicle modelling, winter conditions, winter test, signal processing, Kalman filter
Nationell ämneskategori
Farkostteknik
Forskningsämne
Farkostteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-201022DOI: 10.1080/00423114.2016.1278248ISI: 000399659300001Scopus ID: 2-s2.0-85009775094OAI: oai:DiVA.org:kth-201022DiVA, id: diva2:1072352
Projekt
iCOMSA
Forskningsfinansiär
VINNOVA, 2012-04609
Anmärkning

QC 20170208

Tillgänglig från: 2017-02-07 Skapad: 2017-02-07 Senast uppdaterad: 2017-06-07Bibliografiskt granskad
Ingår i avhandling
1. Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
Öppna denna publikation i ny flik eller fönster >>Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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. 

Ort, förlag, år, upplaga, sidor
Stockholm: Kungliga tekniska högskolan, 2017. s. 72
Serie
TRITA-AVE, ISSN 1651-7660 ; 2017:12
Nyckelord
Steering feel, vehicle handling, driver preference, objective metrics, subjective assessments, regression analysis, artificial neural network
Nationell ämneskategori
Farkostteknik
Forskningsämne
Farkostteknik
Identifikatorer
urn:nbn:se:kth:diva-202348 (URN)978-91-7729-302-6 (ISBN)
Disputation
2017-03-17, D3, Lindstedsvägen 5, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Projekt
iCOMSA
Forskningsfinansiär
VINNOVA, 2012-04609
Anmärkning

QC 20170223

Tillgänglig från: 2017-02-23 Skapad: 2017-02-21 Senast uppdaterad: 2017-02-23Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

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Förlagets fulltextScopushttp://www.tandfonline.com/doi/full/10.1080/00423114.2016.1278248

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