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High Accuracy Speed and Angular Position Detection by Multiple Sensing
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The information from numerous sensors placed in modern vehicles is crucial for maximum performance. In the future, when commercial vehicles become fully autonomous, this information needs to be accurate in order to guarantee optimal performance as well as safety. Accuracy is specifically important in components that are essential for the vehicle operation, such as the gearbox. Sensor solutions in modern gearboxes require an improvement, especially when it comes to low speed detection. This is important in automatic transmission, which is the preferred choice in commercial vehicles, so that the gearbox control unit can shift gears in optimal way.

This thesis project investigates the possibility of high accuracy speed and angular position detection in gearbox by multiple sensing. This was done with the use of distributed sensors covering the region of a single period on a gear structure. Hall sensors with analogue output were implemented for this purpose. Also, GMR sensors were introduced in this application, only in single unit measurement configuration.

The results show that the accuracy of low speed detection is higher with multiple sensors. Particularly in the case of braking down to zero speed, the accuracy is significantly increased in comparison to digital speed detection. However, at higher speed the accuracy is nearly the same. Therefore, one possible solution could comprise the use of multiple sensing at low speed and digital detection at high speed. 

Place, publisher, year, edition, pages
2017.
Series
UPTEC E, ISSN 1654-7616 ; 17007
Keywords [en]
Sensors, Multiple Sensing, Hall, GMR, Gearbox
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-325852OAI: oai:DiVA.org:uu-325852DiVA, id: diva2:1117163
External cooperation
Scania CV AB
Educational program
Master Programme in Electrical Engineering
Supervisors
Examiners
Available from: 2017-06-28 Created: 2017-06-28 Last updated: 2017-06-28Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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