research-article

Exploiting Multi-Cell Battery for Mobile Devices: Design, Management, and Performance

Authors:

Hojung ChaAuthors Info & Claims

SenSys '17: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

Article No.: 20, Pages 1 - 13

https://doi.org/10.1145/3131672.3131684

Published: 06 November 2017 Publication History

Abstract

Extending battery lifetime is an important issue for mobile devices. While extensive attempts have been made at the software level, optimization often risks hampering user experience. One fundamental method to increase battery lifetime is to improve the efficiency of the battery itself. We argue that the multi-cell battery system, which is widely used for enhancing battery efficiency in the electric vehicle (EV) field, can solve this issue. However, due to the hardware constraints and device usage characteristics, battery advancements in the EV field are not directly applicable to mobile devices. In this paper, we propose BattMan, a multi-cell battery management system for mobile devices, for the enhancement of battery efficiency. We develop an accurate battery cell model to estimate the expected battery lifetime considering the recovery effect, the rate capacity effect, and battery aging. We also propose a multi-cell scheduling algorithm to maximize the overall battery lifetime. We implemented BattMan on recent smartphones and evaluated its impact on battery lifetime. The experimental results show that a two-cell configuration of the proposed system increases battery lifetime by an average of between 14-19%, depending on cell aging, in real usage scenarios over a single-cell battery of the same overall capacity. We hope the proposed multi-cell battery scheme opens up a new direction towards battery lifetime improvement in mobile devices.

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Cited By

Liu WDai HXia YHuang D(2024)Hybrid State-of-Charge Estimation for Primary Battery Powered Sensor NodesIEEE Sensors Journal10.1109/JSEN.2024.337345224:9(15378-15392)Online publication date: 1-May-2024
https://doi.org/10.1109/JSEN.2024.3373452
Odu ADu Preez M(2023)Exploring the Information-seeking Behaviour of Students at the Federal University of Lafia, Nigeria, who Use Mobile Technologies to Access InformationMousaion: South African Journal of Information Studies10.25159/2663-659X/1269741:4Online publication date: 20-Dec-2023
https://doi.org/10.25159/2663-659X/12697
Kwak JLee SJeong DKumar AShin DKim IShin DLee KLee JShin IHui PAmiri Sani ANurmi PLiu Y(2023)MixMax: Leveraging Heterogeneous Batteries to Alleviate Low Battery Experience for Mobile UsersProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596843(247-260)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3581791.3596843
Show More Cited By

Index Terms

Exploiting Multi-Cell Battery for Mobile Devices: Design, Management, and Performance
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
2. Hardware
  1. Power and energy
    1. Energy generation and storage
      1. Batteries

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cover image ACM Conferences

SenSys '17: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

November 2017

490 pages

ISBN:9781450354592

DOI:10.1145/3131672

Editor:
Rasit Eskicioglu
University of Manitoba, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 November 2017

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Samsung Research Funding Center of Samsung Electronics

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SenSys '17

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SenSys '17: The 15th ACM Conference on Embedded Network Sensor Systems

November 6 - 8, 2017

Delft, Netherlands

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Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

Liu WDai HXia YHuang D(2024)Hybrid State-of-Charge Estimation for Primary Battery Powered Sensor NodesIEEE Sensors Journal10.1109/JSEN.2024.337345224:9(15378-15392)Online publication date: 1-May-2024
https://doi.org/10.1109/JSEN.2024.3373452
Odu ADu Preez M(2023)Exploring the Information-seeking Behaviour of Students at the Federal University of Lafia, Nigeria, who Use Mobile Technologies to Access InformationMousaion: South African Journal of Information Studies10.25159/2663-659X/1269741:4Online publication date: 20-Dec-2023
https://doi.org/10.25159/2663-659X/12697
Kwak JLee SJeong DKumar AShin DKim IShin DLee KLee JShin IHui PAmiri Sani ANurmi PLiu Y(2023)MixMax: Leveraging Heterogeneous Batteries to Alleviate Low Battery Experience for Mobile UsersProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596843(247-260)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3581791.3596843
Jiang JHu SLiu W(2023)Optimizing Battery Discharge with Supercapacitor: Design, Evaluation and Analysis2023 5th International Conference on Circuits and Systems (ICCS)10.1109/ICCS59502.2023.10367297(151-155)Online publication date: 27-Oct-2023
https://doi.org/10.1109/ICCS59502.2023.10367297
Kim JBaek SJeon SCha H(2022)DynLiB: Maximizing Energy Availability of Hybrid Li-Ion Battery SystemsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319752641:11(3850-3861)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2022.3197526
Kim JBaek SChoi YAhn JCha H(2020)Hydrone: Reconfigurable Energy Storage for UAV ApplicationsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.3013052(1-1)Online publication date: 2020
https://doi.org/10.1109/TCAD.2020.3013052
Jeon SKim JAhn JCha H(2020)Optimizing Discharge Efficiency of Reconfigurable Battery with Deep Reinforcement LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.3012230(1-1)Online publication date: 2020
https://doi.org/10.1109/TCAD.2020.3012230

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