EmRep: Energy management relying on state‐of‐charge extrema prediction
Abstract 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 o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2022-07-01
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| Series: | IET Computers & Digital Techniques |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cdt2.12033 |
| _version_ | 1797422352707878912 |
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| author | Lars Hanschke Christian Renner |
| author_facet | Lars Hanschke Christian Renner |
| author_sort | Lars Hanschke |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T07:31:04Z |
| format | Article |
| id | doaj.art-322ac331830c45f4a3406e49c17af86b |
| institution | Directory Open Access Journal |
| issn | 1751-8601 1751-861X |
| language | English |
| last_indexed | 2024-03-09T07:31:04Z |
| publishDate | 2022-07-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Computers & Digital Techniques |
| spelling | doaj.art-322ac331830c45f4a3406e49c17af86b2023-12-03T06:14:20ZengHindawi-IETIET Computers & Digital Techniques1751-86011751-861X2022-07-011649110510.1049/cdt2.12033EmRep: Energy management relying on state‐of‐charge extrema predictionLars Hanschke0Christian Renner1Research Group smartPORT Hamburg University of Technology Hamburg GermanyUniversity Koblenz ‐ Landau Koblenz GermanyAbstract 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.https://doi.org/10.1049/cdt2.12033energy harvestingenergy storagewireless sensor networksenergy management systemstelecommunication power management |
| spellingShingle | Lars Hanschke Christian Renner EmRep: Energy management relying on state‐of‐charge extrema prediction IET Computers & Digital Techniques energy harvesting energy storage wireless sensor networks energy management systems telecommunication power management |
| title | EmRep: Energy management relying on state‐of‐charge extrema prediction |
| title_full | EmRep: Energy management relying on state‐of‐charge extrema prediction |
| title_fullStr | EmRep: Energy management relying on state‐of‐charge extrema prediction |
| title_full_unstemmed | EmRep: Energy management relying on state‐of‐charge extrema prediction |
| title_short | EmRep: Energy management relying on state‐of‐charge extrema prediction |
| title_sort | emrep energy management relying on state of charge extrema prediction |
| topic | energy harvesting energy storage wireless sensor networks energy management systems telecommunication power management |
| url | https://doi.org/10.1049/cdt2.12033 |
| work_keys_str_mv | AT larshanschke emrepenergymanagementrelyingonstateofchargeextremaprediction AT christianrenner emrepenergymanagementrelyingonstateofchargeextremaprediction |