Accurate Online Full Charge Capacity Modeling of Smartphone Batteries

TL;DR

This paper introduces a novel, hardware-free method for accurately estimating smartphone battery full charge capacity using voltage and charging rate data, leveraging crowdsourced information for large-scale analysis.

Contribution

It presents a new approach for FCC estimation that does not require low-level system access or additional hardware, utilizing crowdsourced data to improve battery health assessment.

Findings

55% of devices experienced FCC loss

Median capacity loss exceeded 20% in some models

Method enables better battery performance debugging

Abstract

Full charge capacity (FCC) refers to the amount of energy a battery can hold. It is the fundamental property of smartphone batteries that diminishes as the battery ages and is charged/discharged. We investigate the behavior of smartphone batteries while charging and demonstrate that the battery voltage and charging rate information can together characterize the FCC of a battery. We propose a new method for accurately estimating FCC without exposing low-level system details or introducing new hardware or system modules. We also propose and implement a collaborative FCC estimation technique that builds on crowdsourced battery data. The method finds the reference voltage curve and charging rate of a particular smartphone model from the data and then compares the curve and rate of an individual user with the model reference curve. After analyzing a large data set, we report that 55% of all devices and at least one device in 330 out of 357 unique device models lost some of their FCC. For some models, the median capacity loss exceeded 20% with the inter-quartile range being over 20 pp. The models enable debugging the performance of smartphone batteries, more accurate power modeling, and energy-aware system or application optimization.