SEMI: Semi-Online Power Estimates for Smartphone Hardware Components

Ekarat Rattagan, Edward T.H. Chu, Ying Dar Lin, Yuan Cheng Lai

Research output: Contribution to journalArticlepeer-review

Abstract

Using the data obtained from a battery monitoring unit (BMU) are a low-cost, easy-to-use way to estimate the power consumption of smartphones. Current online power estimation methods produce significant errors compared to using external power monitors because online methods do not address the three most important factors which affect the efficacy of online power consumption estimates. These are: (1) battery capacity degradation with aging, (2) asynchronous power consumption, and (3) the effect of state-of-charge (SoC) difference, the remaining power level of the battery. This paper presents a semi-online power estimate method which uses charging data to determine actual battery capacity, applies the discrepancy between battery voltage at different workloads for asynchronous power detection, and analyzes the range of SoC (0-100 percent) which causes minimal power estimate errors. The proposed method is validated by conducting a series of experiments on a smartphone and comparing the results with the existing online power estimation methods. Experimental results on the power consumption estimates of real applications indicate that the semi-online method reduced the error rate of power estimates obtained from existing online methods by 27-94 percent. Moreover, this work also shows that battery capacity degradation is the major factor affecting the efficacy of online power estimations.

Original languageEnglish
Article number7814306
Pages (from-to)54-62
Number of pages9
JournalIEEE Transactions on Sustainable Computing
Volume1
Issue number2
DOIs
StatePublished - 1 Jul 2016

Keywords

  • asynchronous power consumption behavior
  • battery capacity
  • Power consumption estimation
  • state-of-charge

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