Battery state-of-health estimation for mobile devices

Citation data:

Proceedings of the 8th International Conference on Cyber-Physical Systems - ICCPS '17, Page: 51-60

Publication Year:
2017
Citations 1
Citation Indexes 1
DOI:
10.1145/3055004.3055018
Author(s):
Liang He, Eugene Kim, Kang G. Shin, Guozhu Meng, Tian He
Publisher(s):
Association for Computing Machinery (ACM)
Tags:
Computer Science
conference paper description
Insufficient support of electric current sensing on commodity mobile devices leads to inaccurate estimation of their Battery's state-of-health (SoH), which, in turn, shuts them off unexpectedly and accelerates their Battery fading. In this paper, we design V-BASH, a new Battery SoH estimation method based only on their voltages and is compatible to commodity mobile devices. V-BASH is inspired by the physical phenomenon that the relaxing Battery voltages correlate to Battery SoH. Moreover, it is enabled on mobile devices with a common usage pattern of most users frequently taking a long time to charge their devices. The design of V-BASH is guided by 2; 781 empirically collected relaxing voltage traces with 19 mobile device batteries. We evaluate V-BASH using both laboratory experiments and field tests on mobile devices, showing a <6% error in SoH estimation.

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