LAPSE:2023.29317
Published Article
LAPSE:2023.29317
An eDrive-Based Estimation Method of the Laundry Unbalance and Laundry Inertia for Washing Machine Applications
April 13, 2023
Abstract
The estimation of the laundry unbalance and laundry inertia is fundamental in washing machine applications. On the one hand, the estimation and management of the laundry unbalance play a pivotal role in reducing mechanical stress and noise during the spinning phase. On the other hand, the laundry inertia’s estimation, performed at the beginning of the washing cycle, allows for the determination of the proper amounts of water and detergent, the water temperature, and the tumbling time. In this way, good washing performance is obtained, avoiding the waste of energy and resources. Moreover, at the end of the washing cycle, the laundry inertia’s accurate estimation is needed to properly manage the spinning phase. With the aim of optimizing the washing performance, this paper proposes a novel method to estimate the laundry unbalance and laundry inertia. The proposed approach does not require additional sensors, since it uses the already implemented motor control scheme, enhanced by a dedicated position-tracking observer. Experimental results have been carried out on a commercial horizontal-axis direct-drive washer, demonstrating the validity of the proposed solution.
Keywords
laundry inertia, laundry unbalance, washing machine
Suggested Citation
Martinello D, Rubino S, Bojoi R. An eDrive-Based Estimation Method of the Laundry Unbalance and Laundry Inertia for Washing Machine Applications. (2023). LAPSE:2023.29317
Author Affiliations
Martinello D: Haier Europe Appliance Research & Technology, Haier Deutschland GmbH, Neumeyerstraße 30, 90411 Nürnberg, Germany [ORCID]
Rubino S: Dipartimento Energia “G. Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy [ORCID]
Bojoi R: Dipartimento Energia “G. Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Journal Name
Energies
Volume
14
Issue
3
First Page
637
Year
2021
Publication Date
2021-01-27
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14030637, Publication Type: Journal Article
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LAPSE:2023.29317
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https://doi.org/10.3390/en14030637
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Apr 13, 2023
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