Battery Health Estimation for IoT Devices using V-Edge Dynamics

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http://hdl.handle.net/10138/321931

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Kumar , A , Hoque , M A , Nurmi , P , Pecht , M G , Tarkoma , S & Song , J 2020 , Battery Health Estimation for IoT Devices using V-Edge Dynamics . in Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications : HotMobile'2020 . ACM , New York , pp. 56-61 , HotMobile '20: The 21st International Workshop on Mobile Computing Systems and Applications , Austin , United States , 03/03/2020 . https://doi.org/10.1145/3376897.3377858

Title: Battery Health Estimation for IoT Devices using V-Edge Dynamics
Author: Kumar, Arjun; Hoque, Mohammad Ashraful; Nurmi, Petteri; Pecht, Michael G.; Tarkoma, Sasu; Song, Junehwa
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
Publisher: ACM
Date: 2020-03-04
Language: eng
Number of pages: 6
Belongs to series: Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications HotMobile'2020
ISBN: 978-1-4503-7116-2
URI: http://hdl.handle.net/10138/321931
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 have constant discharge during their operation.
Subject: Battery Capacity
Battery Health
CAPACITY
CHARGE
Internet of Things
Lithium Battery
Power Models
STATE
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
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