Sudden drop in the battery level?: understanding smartphone state of charge anomaly

ABSTRACT

Battery State of Charge (SOC) estimation is a fundamental component of today's smartphones that affects the internal processes and observable behavior of the devices. This article systematically investigates and analyzes the SOC estimation techniques in smartphones. First, we discover that the voltage curve of a given smartphone implicitly captures the usable capacity of the battery while charging the mobile device. Second, we observe that today's SOC estimation techniques do not model battery capacity loss sufficiently to accurately capture the usable capacity. Finally, we report findings based on battery analytics of 2077 devices that validate the relationship between battery voltage and the usable capacity of a device. The presented results enable the development of more accurate battery gauges and metering solutions thus resulting in better power-saving decisions, recommendations for the users, and most importantly more reliable systems.

References

1. iPhone 5 Battery Replacement Program. https://www.apple.com/support/iphone5-battery/.
2. Choi, S. S., and Lim, H. S. Factors that affect cycle-life and possible degradation mechanisms of a li-ion cell based on licoo2. Journal of Power Sources 111, 1 (2002), 130--136.
3. Erdinc, O., Vural, B., and Uzunoglu, M. A dynamic lithium-ion battery model considering the effects of temperature and capacity fading. In Clean Electrical Power, 2009 International Conference on (June 2009), pp. 383--386.
4. Liu, D., Pang, J., Zhou, J., Peng, Y., and Pecht, M. Prognostics for state of health estimation of lithium-ion batteries based on combination gaussian process functional regression. Microelectronics Reliability 53, 6 (2013), 832--839.
5. Lu, L., Han, X., Li, J., Hua, J., and Ouyang, M. A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources 226, 0 (2013), 272--288.
6. Oliner, A. J., Iyer, A. P., Stoica, I., Lagerspetz, E., and Tarkoma, S. Carat: Collaborative Energy Diagnosis for Mobile Devices. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (New York, NY, USA, 2013), ACM, pp. 10:1--10:14.
7. Pathak, A., Hu, Y. C., and Zhang, M. Bootstrapping energy debugging on smartphones: A first look at energy bugs in mobile devices. In HotNets (2011).
8. Ramadass, P., Haran, B., White, R., and Popov, B. N. Mathematical modeling of the capacity fade of li-ion cells. Journal of Power Sources 123, 2 (2003), 230--240.
9. Sasaki, T., Ukyo, Y., and Novák, P. Memory effect in a lithium-ion battery. Nat Mater 12, 6 (2013), 569--575.
10. Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R. P., Mao, Z. M., and Yang, L. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis (New York, NY, USA, 2010), ACM, pp. 105--114.

Index Terms

• Applied computing

  • Computers in other domains

    • Personal computers and PC applications

• Computer systems organization

  • Dependable and fault-tolerant systems and networks

• General and reference

  • Cross-computing tools and techniques

    • Performance

• Networks

  • Network performance evaluation