Battery-related problems in mobile devices have been extensively investigated in both industry and literature. In particular, battery aging is a critical issue, since battery lifetime decreases as usage time increases. Battery aging primarily causes inconvenience to users by necessitating frequent recharging, and also affects the accuracy of power estimations for mobile devices. Evaluating battery aging and its effects has rarely been addressed in prior works. In this paper, we propose an online scheme to quantify the battery aging of mobile devices. Specifically, we estimate the degree of battery aging as a ratio metric based on patterns of charging time. For example, an estimate of 50% indicates that the battery capacity is only half of full capacity, meaning that the battery usage time is only approximately half that of the new battery's. Our scheme works autonomously on mobile devices and does not require any external equIPment. The extensive experiments demonstrated that the proposed scheme quantifies battery aging accurately.
|Title of host publication||2015 52nd ACM/EDAC/IEEE Design Automation Conference, DAC 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2015 Jul 24|
|Event||52nd ACM/EDAC/IEEE Design Automation Conference, DAC 2015 - San Francisco, United States|
Duration: 2015 Jun 8 → 2015 Jun 12
|Name||Proceedings - Design Automation Conference|
|Other||52nd ACM/EDAC/IEEE Design Automation Conference, DAC 2015|
|Period||15/6/8 → 15/6/12|
Bibliographical notePublisher Copyright:
© 2015 ACM.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Modelling and Simulation