TY - GEN
T1 - Semi-online power estimation for smartphone hardware components
AU - Rattagan, Ekarat
AU - Chu, Edward T.H.
AU - Lin, Ying-Dar
AU - Lai, Yuan Cheng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/10
Y1 - 2015/8/10
N2 - With low cost, ease of use, and scalability, online power estimation, which uses data obtained from battery monitoring unit (BMU) to estimate power consumption, could be a potential power estimation method for commercial smartphones. However, existing online power estimation methods exhibit high errors compared with the use of external power monitors. This is because they do not tackle three main factors which effect the efficacy of online power estimations: (1) the battery capacity degradation, (2) the asynchronous power consumption behavior, and (3) the effect of state of charge (SOC) difference. In this paper, we present a semi-online power estimation method which adopted the charging data to determine the actual battery capacity, applied the discrepancy of battery voltage for asynchronous power detection, and analyzed the optimal SOC for the hardware training. We validate the proposed method by conducting a series of experiments on a commercial smartphone and comparing its results with the existing online power estimation methods. Our results indicate that, the semi-online method can reduce the error rates of the average power estimates by 86.66%. Moreover, the experiment reveals that the battery capacity degradation has the major effect on the efficacy of online power estimations.
AB - With low cost, ease of use, and scalability, online power estimation, which uses data obtained from battery monitoring unit (BMU) to estimate power consumption, could be a potential power estimation method for commercial smartphones. However, existing online power estimation methods exhibit high errors compared with the use of external power monitors. This is because they do not tackle three main factors which effect the efficacy of online power estimations: (1) the battery capacity degradation, (2) the asynchronous power consumption behavior, and (3) the effect of state of charge (SOC) difference. In this paper, we present a semi-online power estimation method which adopted the charging data to determine the actual battery capacity, applied the discrepancy of battery voltage for asynchronous power detection, and analyzed the optimal SOC for the hardware training. We validate the proposed method by conducting a series of experiments on a commercial smartphone and comparing its results with the existing online power estimation methods. Our results indicate that, the semi-online method can reduce the error rates of the average power estimates by 86.66%. Moreover, the experiment reveals that the battery capacity degradation has the major effect on the efficacy of online power estimations.
KW - Batteries
KW - Error analysis
KW - Estimation
KW - Hardware
KW - Power demand
KW - System-on-chip
KW - Training
UR - http://www.scopus.com/inward/record.url?scp=84959502418&partnerID=8YFLogxK
U2 - 10.1109/SIES.2015.7185058
DO - 10.1109/SIES.2015.7185058
M3 - Conference contribution
AN - SCOPUS:84959502418
T3 - 2015 10th IEEE International Symposium on Industrial Embedded Systems, SIES 2015 - Proceedings
SP - 174
EP - 177
BT - 2015 10th IEEE International Symposium on Industrial Embedded Systems, SIES 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Symposium on Industrial Embedded Systems, SIES 2015
Y2 - 8 June 2015 through 10 June 2015
ER -