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Battery Health Estimation for IoT Devices using V-Edge Dynamics

Published: 03 March 2020 Publication History

Abstract

Deployments of battery-powered IoT devices have become ubiquitous, monitoring everything from environmental conditions in smart cities to wildlife movements in remote areas. How to manage the life-cycle of sensors in such large-scale deployments is currently an open issue. Indeed, most deployments let sensors operate until they fail and fix or replace the sensors post-hoc. In this paper, we contribute by developing a new approach for facilitating the life-cycle management of large-scale sensor deployments through online estimation of battery health. Our approach relies on so-called V-edge dynamics which capture and characterize instantaneous voltage drops. Experiments carried out on a dataset of battery discharge measurements demonstrate that our approach is capable of estimating battery health with up to $80%$ accuracy, depending on the characteristics of the devices and the processing load they undergo. Our method is particularly well-suited for the sensor devices, operating dedicated tasks, that they have constant discharge during their operation.

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Cited By

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  • (2024)Estimating SoC, SoH, or RuL of Rechargeable Batteries via IoT: A ReviewIEEE Internet of Things Journal10.1109/JIOT.2023.334236711:5(7559-7582)Online publication date: 1-Mar-2024
  • (2023)Anomaly Detection in Battery Charging Systems: A Deep Sequence Model Approach2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00109(587-594)Online publication date: 21-Dec-2023
  • (2023)Effect of Slots in Rectangular Geometry Patch Antennas for Energy Harvesting in 2.4 GHz BandCSEI: International Conference on Computer Science, Electronics and Industrial Engineering (CSEI)10.1007/978-3-031-30592-4_22(318-332)Online publication date: 1-May-2023
  • Show More Cited By

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cover image ACM Conferences
HotMobile '20: Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications
March 2020
116 pages
ISBN:9781450371162
DOI:10.1145/3376897
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 March 2020

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Author Tags

  1. battery capacity
  2. battery health
  3. internet of things
  4. lithium battery
  5. power models

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Cited By

View all
  • (2024)Estimating SoC, SoH, or RuL of Rechargeable Batteries via IoT: A ReviewIEEE Internet of Things Journal10.1109/JIOT.2023.334236711:5(7559-7582)Online publication date: 1-Mar-2024
  • (2023)Anomaly Detection in Battery Charging Systems: A Deep Sequence Model Approach2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00109(587-594)Online publication date: 21-Dec-2023
  • (2023)Effect of Slots in Rectangular Geometry Patch Antennas for Energy Harvesting in 2.4 GHz BandCSEI: International Conference on Computer Science, Electronics and Industrial Engineering (CSEI)10.1007/978-3-031-30592-4_22(318-332)Online publication date: 1-May-2023
  • (2022)Prediction of Li-Ion Battery Discharge Patterns in IoT Devices Under Random Use Via Machine Learning AlgorithmsThe Computer Journal10.1093/comjnl/bxac08966:6(1541-1548)Online publication date: 2-Jul-2022

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