Battery state-of-health estimation for mobile devices

Liang He; Eugene Kim; Kang G. Shin; Guozhu Meng; Tian He

doi:10.1145/3055004.3055018

Battery state-of-health estimation for mobile devices

Liang He, Eugene Kim, Kang G. Shin, Guozhu Meng, Tian He

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)
Publisher	Association for Computing Machinery, Inc
Pages	51-60
Number of pages	10
ISBN (Electronic)	9781450349659
DOIs	https://doi.org/10.1145/3055004.3055018
State	Published - Apr 18 2017
Event	8th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2017 - Pittsburgh, United States Duration: Apr 18 2017 → Apr 20 2017

Publication series

Name	Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)

Other

Other	8th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2017
Country/Territory	United States
City	Pittsburgh
Period	4/18/17 → 4/20/17

Bibliographical note

Funding Information:
The work reported in this paper was supported by NSF under Grants CNS-1329702 and CNS-1446117.

Publisher Copyright:
© 2017 ACM.

Keywords

Battery management
Mobile device
Relaxation
State of health

Publisher link

10.1145/3055004.3055018

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He, L., Kim, E., Shin, K. G., Meng, G., & He, T. (2017). Battery state-of-health estimation for mobile devices. In Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week) (pp. 51-60). (Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)). Association for Computing Machinery, Inc. https://doi.org/10.1145/3055004.3055018

Battery state-of-health estimation for mobile devices. / He, Liang; Kim, Eugene; Shin, Kang G. et al.
Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week). Association for Computing Machinery, Inc, 2017. p. 51-60 (Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

He, L, Kim, E, Shin, KG, Meng, G & He, T 2017, Battery state-of-health estimation for mobile devices. in Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week). Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week), Association for Computing Machinery, Inc, pp. 51-60, 8th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2017, Pittsburgh, United States, 4/18/17. https://doi.org/10.1145/3055004.3055018

He L, Kim E, Shin KG, Meng G, He T. Battery state-of-health estimation for mobile devices. In Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week). Association for Computing Machinery, Inc. 2017. p. 51-60. (Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)). doi: 10.1145/3055004.3055018

He, Liang ; Kim, Eugene ; Shin, Kang G. et al. / Battery state-of-health estimation for mobile devices. Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week). Association for Computing Machinery, Inc, 2017. pp. 51-60 (Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)).

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abstract = "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.",

keywords = "Battery management, Mobile device, Relaxation, State of health",

author = "Liang He and Eugene Kim and Shin, {Kang G.} and Guozhu Meng and Tian He",

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N2 - 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.

AB - 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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Battery state-of-health estimation for mobile devices

Abstract

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Keywords

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