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  4. EmRep: energy management relying on state‐of‐charge extrema prediction
 
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EmRep: energy management relying on state‐of‐charge extrema prediction

Citation Link: https://doi.org/10.15480/882.4399
Publikationstyp
Journal Article
Date Issued
2022-07
Sprache
English
Author(s)
Hanschke, Lars  
Renner, Bernd-Christian  
Institut
Autonome Cyber-Physische Systeme E-24  
TORE-DOI
10.15480/882.4399
TORE-URI
http://hdl.handle.net/11420/12240
Journal
IET computers & digital techniques  
Volume
16
Issue
4
Start Page
91
End Page
105
Citation
IET Computers & Digital Techniques 16 (4): 91-105 (2022-07)
Publisher DOI
10.1049/cdt2.12033
Scopus ID
2-s2.0-85127453376
Publisher
IET
The persistent rise of Energy Harvesting Wireless Sensor Networks entails increasing demands on the efficiency and configurability of energy management. New applications often profit from or even require user-defined time-varying utilities, for example, the health assessment of bridges is only possible at rushhour. However, monitoring times do not necessarily overlap with energy harvest periods. This misalignment is often corrected by over-provisioning the energy storage. Favourable small-footprint and cheap energy storage, however, fill up quickly and waste surplus energy. Hence, EmRep is presented, which decouples the energy management of high-intake from low-intake harvest periods. Based on the State-of-Charge extrema prediction, the authors enhance energy management and reduce saturation of energy storage by design. Considering multiple user-defined utility profiles, the benefits of EmRep in combination with a variety of prediction algorithms, time resolutions, and energy storage sizes are showcased. EmRep is tailored to platforms with small energy storage, in which it is found that it doubles effective utility, and also increases performance by 10% with large-sized storage.
DDC Class
004: Informatik
530: Physik
Funding(s)
Publikationsfonds 2021  
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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