Aging Study of In‐Use Lithium‐Ion Battery Packs to Predict End of Life Using Black Box Model

Daniela Chrenko; Manuel Fernandez Montejano; Sudnya Vaidya; Romain Tabusse

doi:10.3390/app12136557

Aging Study of In‐Use Lithium‐Ion Battery Packs to Predict End of Life Using Black Box Model

Daniela Chrenko
, Manuel Fernandez Montejano
, Sudnya Vaidya
, Romain Tabusse

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

8 Scopus citations

Abstract

In order to study the state of health (SOH) of unbalanced battery packs in real life, a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are studied, leading to the identification of five key parameters to indicate aging, as well as parameters influencing aging. Based on the measurement results, a simple black box model using evolutionary genetic algorithm is presented, which is used as end‐of‐life prediction model of the battery pack, successfully providing an approximate estimation of aging. This approach might thus be used for the supervision of battery systems during real‐life use.

Original language	English
Article number	6557
Journal	Applied Sciences (Switzerland)
Volume	12
Issue number	13
DOIs	https://doi.org/10.3390/app12136557
State	Published - Jul 1 2022
Externally published	Yes

Funding

Acknowledgments: This work has been supported by the EIPHI Graduate School (contract ANR‐ 17‐EURE‐0002) and the Region Bourgogne Franche‐Comté.

Keywords

aging
analysis
battery
black box
evolutionary algorithms
internal resistance
prediction

Access to Document

10.3390/app12136557

Cite this

@article{8a62b58c4acc43d2808e758c292b5d88,

title = "Aging Study of In‐Use Lithium‐Ion Battery Packs to Predict End of Life Using Black Box Model",

abstract = "In order to study the state of health (SOH) of unbalanced battery packs in real life, a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are studied, leading to the identification of five key parameters to indicate aging, as well as parameters influencing aging. Based on the measurement results, a simple black box model using evolutionary genetic algorithm is presented, which is used as end‐of‐life prediction model of the battery pack, successfully providing an approximate estimation of aging. This approach might thus be used for the supervision of battery systems during real‐life use.",

keywords = "aging, analysis, battery, black box, evolutionary algorithms, internal resistance, prediction",

author = "Daniela Chrenko and Montejano, \{Manuel Fernandez\} and Sudnya Vaidya and Romain Tabusse",

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

year = "2022",

month = jul,

day = "1",

doi = "10.3390/app12136557",

language = "English",

volume = "12",

journal = "Applied Sciences (Switzerland)",

issn = "2076-3417",

publisher = "MDPI",

number = "13",

}

TY - JOUR

T1 - Aging Study of In‐Use Lithium‐Ion Battery Packs to Predict End of Life Using Black Box Model

AU - Chrenko, Daniela

AU - Montejano, Manuel Fernandez

AU - Vaidya, Sudnya

AU - Tabusse, Romain

PY - 2022/7/1

Y1 - 2022/7/1

N2 - In order to study the state of health (SOH) of unbalanced battery packs in real life, a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are studied, leading to the identification of five key parameters to indicate aging, as well as parameters influencing aging. Based on the measurement results, a simple black box model using evolutionary genetic algorithm is presented, which is used as end‐of‐life prediction model of the battery pack, successfully providing an approximate estimation of aging. This approach might thus be used for the supervision of battery systems during real‐life use.

AB - In order to study the state of health (SOH) of unbalanced battery packs in real life, a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are studied, leading to the identification of five key parameters to indicate aging, as well as parameters influencing aging. Based on the measurement results, a simple black box model using evolutionary genetic algorithm is presented, which is used as end‐of‐life prediction model of the battery pack, successfully providing an approximate estimation of aging. This approach might thus be used for the supervision of battery systems during real‐life use.

KW - aging

KW - analysis

KW - battery

KW - black box

KW - evolutionary algorithms

KW - internal resistance

KW - prediction

UR - https://www.scopus.com/pages/publications/85133507383

U2 - 10.3390/app12136557

DO - 10.3390/app12136557

M3 - Article

AN - SCOPUS:85133507383

SN - 2076-3417

VL - 12

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

IS - 13

M1 - 6557

ER -

Aging Study of In‐Use Lithium‐Ion Battery Packs to Predict End of Life Using Black Box Model

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this