| Title |
Battery Health Estimation For Iot Devices Using V-Edge Dynamics |
| ID_Doc |
11681 |
| Authors |
Kumar A.; Hoque M.A.; Nurmi P.; Pecht M.G.; Tarkoma S.; Song J. |
| Year |
2020 |
| Published |
HotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications |
| DOI |
http://dx.doi.org/10.1145/3376897.3377858 |
| 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. © 2020 Association for Computing Machinery. |
| Author Keywords |
Battery Capacity; Battery Health; Internet of Things; Lithium Battery; Power Models |
