Improving lithium-ion battery pack diagnostics by optimizing the internal allocation of demand current for parameter identifiability

Michael J. Rothenberger, Jariullah Safi, Ji Liu, Joel Anstrom, Sean Brennan, Hosam K. Fathy

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This article optimizes the allocation of external current demand among parallel strings of cells in a lithium-ion battery pack to improve Fisher identifiability for these strings. The article is motivated by the fact that better battery parameter identifiability can enable the more accurate detection of unhealthy outlier cells. This is critical for pack diagnostics. The literature shows that it is possible to optimize the cycling of a single battery cell for identifiability, thereby improving the speed and accuracy with which its health-related parameters can be estimated. However, the applicability of this idea to online pack management is limited by the fact that overall pack current is typically dictated by the user, and difficult to optimize. We circumvent this challenge by optimizing the internal allocation of total pack current for identifiability. We perform this optimization for two pack designs: one that exploits switching control to allocate current passively among parallel strings of cells, and one that incorporates bidirectional DC-DC conversion for active charge shuttling among the strings. A novel evolutionary algorithm optimizes identifiability for each pack design, and a local outlier probability (LoOP) algorithm is then used for diagnostics. Simulation studies show significant improvements in diagnostic accuracy for an automotive protocol.

Original languageEnglish (US)
Article number081001
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume139
Issue number8
DOIs
StatePublished - Aug 1 2017

Fingerprint

electric batteries
strings
lithium
cells
Evolutionary algorithms
ions
direct current
Health
health
cycles
optimization
Lithium-ion batteries
simulation

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications

Cite this

@article{63b6d4a1e4354db4a0056231ef705344,
title = "Improving lithium-ion battery pack diagnostics by optimizing the internal allocation of demand current for parameter identifiability",
abstract = "This article optimizes the allocation of external current demand among parallel strings of cells in a lithium-ion battery pack to improve Fisher identifiability for these strings. The article is motivated by the fact that better battery parameter identifiability can enable the more accurate detection of unhealthy outlier cells. This is critical for pack diagnostics. The literature shows that it is possible to optimize the cycling of a single battery cell for identifiability, thereby improving the speed and accuracy with which its health-related parameters can be estimated. However, the applicability of this idea to online pack management is limited by the fact that overall pack current is typically dictated by the user, and difficult to optimize. We circumvent this challenge by optimizing the internal allocation of total pack current for identifiability. We perform this optimization for two pack designs: one that exploits switching control to allocate current passively among parallel strings of cells, and one that incorporates bidirectional DC-DC conversion for active charge shuttling among the strings. A novel evolutionary algorithm optimizes identifiability for each pack design, and a local outlier probability (LoOP) algorithm is then used for diagnostics. Simulation studies show significant improvements in diagnostic accuracy for an automotive protocol.",
author = "Rothenberger, {Michael J.} and Jariullah Safi and Ji Liu and Joel Anstrom and Sean Brennan and Fathy, {Hosam K.}",
year = "2017",
month = "8",
day = "1",
doi = "10.1115/1.4035743",
language = "English (US)",
volume = "139",
journal = "Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME",
issn = "0022-0434",
publisher = "American Society of Mechanical Engineers(ASME)",
number = "8",

}

Improving lithium-ion battery pack diagnostics by optimizing the internal allocation of demand current for parameter identifiability. / Rothenberger, Michael J.; Safi, Jariullah; Liu, Ji; Anstrom, Joel; Brennan, Sean; Fathy, Hosam K.

In: Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Vol. 139, No. 8, 081001, 01.08.2017.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Improving lithium-ion battery pack diagnostics by optimizing the internal allocation of demand current for parameter identifiability

AU - Rothenberger, Michael J.

AU - Safi, Jariullah

AU - Liu, Ji

AU - Anstrom, Joel

AU - Brennan, Sean

AU - Fathy, Hosam K.

PY - 2017/8/1

Y1 - 2017/8/1

N2 - This article optimizes the allocation of external current demand among parallel strings of cells in a lithium-ion battery pack to improve Fisher identifiability for these strings. The article is motivated by the fact that better battery parameter identifiability can enable the more accurate detection of unhealthy outlier cells. This is critical for pack diagnostics. The literature shows that it is possible to optimize the cycling of a single battery cell for identifiability, thereby improving the speed and accuracy with which its health-related parameters can be estimated. However, the applicability of this idea to online pack management is limited by the fact that overall pack current is typically dictated by the user, and difficult to optimize. We circumvent this challenge by optimizing the internal allocation of total pack current for identifiability. We perform this optimization for two pack designs: one that exploits switching control to allocate current passively among parallel strings of cells, and one that incorporates bidirectional DC-DC conversion for active charge shuttling among the strings. A novel evolutionary algorithm optimizes identifiability for each pack design, and a local outlier probability (LoOP) algorithm is then used for diagnostics. Simulation studies show significant improvements in diagnostic accuracy for an automotive protocol.

AB - This article optimizes the allocation of external current demand among parallel strings of cells in a lithium-ion battery pack to improve Fisher identifiability for these strings. The article is motivated by the fact that better battery parameter identifiability can enable the more accurate detection of unhealthy outlier cells. This is critical for pack diagnostics. The literature shows that it is possible to optimize the cycling of a single battery cell for identifiability, thereby improving the speed and accuracy with which its health-related parameters can be estimated. However, the applicability of this idea to online pack management is limited by the fact that overall pack current is typically dictated by the user, and difficult to optimize. We circumvent this challenge by optimizing the internal allocation of total pack current for identifiability. We perform this optimization for two pack designs: one that exploits switching control to allocate current passively among parallel strings of cells, and one that incorporates bidirectional DC-DC conversion for active charge shuttling among the strings. A novel evolutionary algorithm optimizes identifiability for each pack design, and a local outlier probability (LoOP) algorithm is then used for diagnostics. Simulation studies show significant improvements in diagnostic accuracy for an automotive protocol.

UR - http://www.scopus.com/inward/record.url?scp=85019433217&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019433217&partnerID=8YFLogxK

U2 - 10.1115/1.4035743

DO - 10.1115/1.4035743

M3 - Article

AN - SCOPUS:85019433217

VL - 139

JO - Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

JF - Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

SN - 0022-0434

IS - 8

M1 - 081001

ER -