Extending battery system operation via adaptive reconfiguration

Liang He; Linghe Kong; Yu Gu; Cong Liu; Tian He; Kang G. Shin

doi:10.1145/3284556

Extending battery system operation via adaptive reconfiguration

Liang He, Linghe Kong, Yu Gu, Cong Liu, Tian He, Kang G. Shin

Center for Transportation Studies

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Large-scale battery packs are commonly used in applications such as electric vehicles (EVs) and smart grids. Traditionally, to provide stable voltage to the loads, voltage regulators are used to convert battery packs’ output voltage to those of the loads’ required levels, causing power loss especially when the difference between the supplied and required voltages is large or when the load is light. In this article, we address this issue via a reconfiguration framework for the battery system. By abstracting the battery system as a cell graph, we develop an adaptive reconfiguration algorithm to identify the desired system configurations based on real-time load requirements. Our design is evaluated via both prototype-based experiments, EV driving trace-based emulations, and large-scale simulations. The results demonstrate an extended system operation time of up to 5×, especially when facing severe cell imbalance.

Original language	English (US)
Article number	11
Journal	ACM Transactions on Sensor Networks
Volume	15
Issue number	1
DOIs	https://doi.org/10.1145/3284556
State	Published - Jan 2019

Bibliographical note

Funding Information:
An early version of this work has been published at IEEE RTSS’13 (CPS track) (He et al. 2013). The work reported in this article was supported in part by NSF under Grants CNS-1446117, CNS-1503590, CNS 1527727, CNS-1739577, CNS-1750263, and NSFC under 61629302. Authors’ addresses: L. He, Department of Computer Science and Engineering, University of Colorado Denver, Lawrence Street Center, 1380 Lawrence Street, Suite 800, Denver, Colorado 80204; L. Kong (corresponding author), Computer Science Department, Shanghai Jiaotong University, Dongchuan Rd., Shanghai, China, 200240; email: [email protected]; Y. Gu, Visa Inc., 12301 Research Blvd, Austin, TX, 78759; C. Liu, Department of Computer Science, University of Texas at Dallas, Richardson, Texas, 75080; T. He, Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE Minneapolis, MN 55455; K. G. Shin, Department of Electrical Engineering and Computer Science, University of Michigan, 2260 Hayward St., Ann Arbor, MI 48109-2121. 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]. © 2019 Association for Computing Machinery. 1550-4859/2019/01-ART11 $15.00 https://doi.org/10.1145/3284556

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

Adaptive reconfiguration
Cell imbalance
Rate capacity effect
Reconfigurable battery packs
Voltage regulation

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title = "Extending battery system operation via adaptive reconfiguration",

abstract = "Large-scale battery packs are commonly used in applications such as electric vehicles (EVs) and smart grids. Traditionally, to provide stable voltage to the loads, voltage regulators are used to convert battery packs{\textquoteright} output voltage to those of the loads{\textquoteright} required levels, causing power loss especially when the difference between the supplied and required voltages is large or when the load is light. In this article, we address this issue via a reconfiguration framework for the battery system. By abstracting the battery system as a cell graph, we develop an adaptive reconfiguration algorithm to identify the desired system configurations based on real-time load requirements. Our design is evaluated via both prototype-based experiments, EV driving trace-based emulations, and large-scale simulations. The results demonstrate an extended system operation time of up to 5×, especially when facing severe cell imbalance.",

keywords = "Adaptive reconfiguration, Cell imbalance, Rate capacity effect, Reconfigurable battery packs, Voltage regulation",

author = "Liang He and Linghe Kong and Yu Gu and Cong Liu and Tian He and Shin, {Kang G.}",

note = "Funding Information: An early version of this work has been published at IEEE RTSS{\textquoteright}13 (CPS track) (He et al. 2013). The work reported in this article was supported in part by NSF under Grants CNS-1446117, CNS-1503590, CNS 1527727, CNS-1739577, CNS-1750263, and NSFC under 61629302. Authors{\textquoteright} addresses: L. He, Department of Computer Science and Engineering, University of Colorado Denver, Lawrence Street Center, 1380 Lawrence Street, Suite 800, Denver, Colorado 80204; L. Kong (corresponding author), Computer Science Department, Shanghai Jiaotong University, Dongchuan Rd., Shanghai, China, 200240; email: [email protected]; Y. Gu, Visa Inc., 12301 Research Blvd, Austin, TX, 78759; C. Liu, Department of Computer Science, University of Texas at Dallas, Richardson, Texas, 75080; T. He, Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE Minneapolis, MN 55455; K. G. Shin, Department of Electrical Engineering and Computer Science, University of Michigan, 2260 Hayward St., Ann Arbor, MI 48109-2121. 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]. {\textcopyright} 2019 Association for Computing Machinery. 1550-4859/2019/01-ART11 $15.00 https://doi.org/10.1145/3284556 Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.",

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N2 - Large-scale battery packs are commonly used in applications such as electric vehicles (EVs) and smart grids. Traditionally, to provide stable voltage to the loads, voltage regulators are used to convert battery packs’ output voltage to those of the loads’ required levels, causing power loss especially when the difference between the supplied and required voltages is large or when the load is light. In this article, we address this issue via a reconfiguration framework for the battery system. By abstracting the battery system as a cell graph, we develop an adaptive reconfiguration algorithm to identify the desired system configurations based on real-time load requirements. Our design is evaluated via both prototype-based experiments, EV driving trace-based emulations, and large-scale simulations. The results demonstrate an extended system operation time of up to 5×, especially when facing severe cell imbalance.

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Extending battery system operation via adaptive reconfiguration

Abstract

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