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Research Papers

Improving Lithium-Ion Battery Pack Diagnostics by Optimizing the Internal Allocation of Demand Current for Parameter Identifiability

[+] Author and Article Information
Michael J. Rothenberger, Jariullah Safi, Ji Liu, Sean Brennan

Department of Mechanical
and Nuclear Engineering,
Pennsylvania State University,
University Park, PA 16802

Joel Anstrom

Larson Pennsylvania Transportation Institute,
Pennsylvania State University,
University Park, PA 16802

Hosam K. Fathy

Department of Mechanical
and Nuclear Engineering,
Pennsylvania State University,
University Park, PA 16802
e-mail: hkf2@engr.psu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 12, 2016; final manuscript received January 3, 2017; published online May 15, 2017. Assoc. Editor: Beshah Ayalew.

J. Dyn. Sys., Meas., Control 139(8), 081001 (May 15, 2017) (13 pages) Paper No: DS-16-1248; doi: 10.1115/1.4035743 History: Received May 12, 2016; Revised January 03, 2017

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.

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References

Figures

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Fig. 1

Second-order equivalent-circuit battery model

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Fig. 2

OCV versus state of charge curve for an 18650 LiFePO4 cell [22]

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Fig. 3

Token swapping algorithm with “tabular” design constraining row and column sums

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Fig. 4

Full EV model using an external drive demand supplied with PI control

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Fig. 5

Driving component of the benchmark input cycle consisting of two FTP cycles back to back

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Fig. 6

Electrically reconfigurable pack models, where (a) incorporates MOSFET switches and (b) incorporates bidirectional DC–DC converters

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Fig. 7

Positive token swapping optimal results for (a) cell fraction of external current and (b) cell SOC trajectories

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Fig. 8

Negative token swapping optimal results for (a) cell fraction of external current and (b) cell SOC trajectories

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Fig. 9

Univariate and multivariate parameter outlier study receiver operator curves, where the varying subplots are for the (a) capacity univariate outlier, (b) time constant univariate outlier, (c) capacitance univariate outlier, (d) ohmic resistance outlier, and (e) multivariate outlier case

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Fig. 10

EV model driver subsystem

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Fig. 11

EV model control scheme subsystem

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Fig. 12

EV model motor/generator efficiency subsystem within control scheme subsystem

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Fig. 13

EV model battery pack subsystem

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Fig. 14

EV model vehicle dynamics subsystem

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