Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

Tae Won Noh; Junghoon Ahn; Byoung Kuk Lee

doi:10.3390/en15041423

Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

Tae Won Noh
, Junghoon Ahn
, Byoung Kuk Lee

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

2 Scopus citations

Abstract

In this study, an online cell screening algorithm is proposed to estimate the maximum peak current considering the cell inconsistencies in battery packs for electric vehicles. Based on the equivalent circuit model, the maximum peak current is mathematically defined, and the inconsistency parameters affecting the maximum peak current are analyzed. The proposed algorithm compares the inconsistency parameters of each cell and subsequently selects a cell or a group of cells whose voltage can exceed the allowable voltage range. The maximum peak current is determined based on the selected cells, while ensuring that all the cells are charged and discharged within the allowable voltage range. The feasibility and superiority of the proposed algorithm are verified through an experiment conducted on a commercially manufactured battery pack for electric vehicles.

Original language	English
Article number	1423
Journal	Energies
Volume	15
Issue number	4
DOIs	https://doi.org/10.3390/en15041423
State	Published - 1 Feb 2022
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Battery management system
Cell inconsistency
Maximum peak current estimation

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10.3390/en15041423

Cite this

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title = "Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles",

abstract = "In this study, an online cell screening algorithm is proposed to estimate the maximum peak current considering the cell inconsistencies in battery packs for electric vehicles. Based on the equivalent circuit model, the maximum peak current is mathematically defined, and the inconsistency parameters affecting the maximum peak current are analyzed. The proposed algorithm compares the inconsistency parameters of each cell and subsequently selects a cell or a group of cells whose voltage can exceed the allowable voltage range. The maximum peak current is determined based on the selected cells, while ensuring that all the cells are charged and discharged within the allowable voltage range. The feasibility and superiority of the proposed algorithm are verified through an experiment conducted on a commercially manufactured battery pack for electric vehicles.",

keywords = "Battery management system, Cell inconsistency, Maximum peak current estimation",

author = "Noh, \{Tae Won\} and Junghoon Ahn and Lee, \{Byoung Kuk\}",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2022",

month = feb,

day = "1",

doi = "10.3390/en15041423",

language = "English",

volume = "15",

journal = "Energies",

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T1 - Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

AU - Noh, Tae Won

AU - Ahn, Junghoon

AU - Lee, Byoung Kuk

PY - 2022/2/1

Y1 - 2022/2/1

N2 - In this study, an online cell screening algorithm is proposed to estimate the maximum peak current considering the cell inconsistencies in battery packs for electric vehicles. Based on the equivalent circuit model, the maximum peak current is mathematically defined, and the inconsistency parameters affecting the maximum peak current are analyzed. The proposed algorithm compares the inconsistency parameters of each cell and subsequently selects a cell or a group of cells whose voltage can exceed the allowable voltage range. The maximum peak current is determined based on the selected cells, while ensuring that all the cells are charged and discharged within the allowable voltage range. The feasibility and superiority of the proposed algorithm are verified through an experiment conducted on a commercially manufactured battery pack for electric vehicles.

AB - In this study, an online cell screening algorithm is proposed to estimate the maximum peak current considering the cell inconsistencies in battery packs for electric vehicles. Based on the equivalent circuit model, the maximum peak current is mathematically defined, and the inconsistency parameters affecting the maximum peak current are analyzed. The proposed algorithm compares the inconsistency parameters of each cell and subsequently selects a cell or a group of cells whose voltage can exceed the allowable voltage range. The maximum peak current is determined based on the selected cells, while ensuring that all the cells are charged and discharged within the allowable voltage range. The feasibility and superiority of the proposed algorithm are verified through an experiment conducted on a commercially manufactured battery pack for electric vehicles.

KW - Battery management system

KW - Cell inconsistency

KW - Maximum peak current estimation

UR - https://www.scopus.com/pages/publications/85124886520

U2 - 10.3390/en15041423

DO - 10.3390/en15041423

M3 - Article

AN - SCOPUS:85124886520

SN - 1996-1073

VL - 15

JO - Energies

JF - Energies

IS - 4

M1 - 1423

ER -

Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

Abstract

UN SDGs

Keywords

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