Identifying customer usage profiles of two-wheeled vehicles
type: CreativeWork
description: This cumulative PhD thesis shows the development of methods for identifying customer usage profiles of two-wheeled vehicles utilising the vehicle’s onboard sensors. It comprises three papers that have been published to relevant journals. At present, regarding the automotive industry, customer usage profiles are mostly unknown in durability engineering and the vehicle development process. The detailed knowledge about this crowd-sourced data would improve vehicle design targets and enable a virtual load acquisition. Therefore, it is desirable to identify customer usage and customer loads for every vehicle. The first paper presents a model-based customer load acquisition system that calculates the occurring wheel forces. Therefore, the current road slope and the vehicle mass are estimated using a Kalman filter. The resulting wheel forces are subsequently counted with the rainflow method. The validation was achieved by the comparison of measurements with wheel-load transducers. The second publication presents a three-part road classification system: first, a curve estimator was developed for identifying and classifying road curves; second, the road slope was utilised for counting the hilliness of a given road; and third, a modular road profile estimator was developed for classifying the road roughness according to ISO 8608. The approach uses the vehicle’s transfer functions to estimate the road excitation from the resultant vehicle motions. The third publication experimentally validates the road roughness classification method by comparing the results to laser- scanned road profiles. The comparison shows that even rough roads are detected correctly within a short time span. In addition, an impact detection strategy was developed using a supervised machine learning approach. A study of the six most popular classification algorithms was achieved for detecting mild and severe special events. The combination of the road roughness classification method and the impact detecti…
abstract: This cumulative PhD thesis shows the development of methods for identifying customer usage profiles of two-wheeled vehicles utilising the vehicle’s onboard sensors. It comprises three papers that have been published to relevant journals. At present, regarding the automotive industry, customer usage profiles are mostly unknown in durability engineering and the vehicle development process. The detailed knowledge about this crowd-sourced data would improve vehicle design targets and enable a virtual load acquisition. Therefore, it is desirable to identify customer usage and customer loads for every vehicle. The first paper presents a model-based customer load acquisition system that calculates the occurring wheel forces. Therefore, the current road slope and the vehicle mass are estimated using a Kalman filter. The resulting wheel forces are subsequently counted with the rainflow method. The validation was achieved by the comparison of measurements with wheel-load transducers. The second publication presents a three-part road classification system: first, a curve estimator was developed for identifying and classifying road curves; second, the road slope was utilised for counting the hilliness of a given road; and third, a modular road profile estimator was developed for classifying the road roughness according to ISO 8608. The approach uses the vehicle’s transfer functions to estimate the road excitation from the resultant vehicle motions. The third publication experimentally validates the road roughness classification method by comparing the results to laser- scanned road profiles. The comparison shows that even rough roads are detected correctly within a short time span. In addition, an impact detection strategy was developed using a supervised machine learning approach. A study of the six most popular classification algorithms was achieved for detecting mild and severe special events. The combination of the road roughness classification method and the impact detecti…
author: Gorges, Christian
language: en
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2018
date modified: 2026-07-27T21:28:31Z
provider: Technische Universität Berlin
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Technische Universität Berlin

PropertyValue
property ID: DOI
value: 10.14279/depositonce-7679