Research of the relationship between Li-ion battery charge performance and SOH based on MIGA-GPR method

Sun Peikun; Wang Zhenpo

doi:10.1016/j.egypro.2016.06.086

Research of the relationship between Li-ion battery charge performance and SOH based on MIGA-GPR method

Sun Peikun^*, Wang Zhenpo

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

17 Citations (Scopus)

Abstract

Owing to the problem that it is difficult to measure State of Health (SOH) of Li-ion battery online, a method to estimate SOH of Li-ion battery using the charge performance under different SOH is put forward. By Grey Incidence Analysis (GIA), the correlation degree between the charge performance and SOH is measured. Based on Multi-island Genetic Algorithm (MIGA) and Gauss Process Regression (GPR), with charge performance as input, SOH is estimates and variance of estimation is also calculated. The result shows that the above-mentioned method can evaluate SOH well.

Original language	English
Pages (from-to)	608-613
Number of pages	6
Journal	Energy Procedia
Volume	88
DOIs	https://doi.org/10.1016/j.egypro.2016.06.086
Publication status	Published - 1 Jun 2016
Event	Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 - Fuzhou, China Duration: 15 Nov 2015 → 17 Nov 2015

Keywords

Charge performance
Gauss process regression
Grey incidence analysis
Multi-island genetic algorithm
SOH

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10.1016/j.egypro.2016.06.086

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@article{358cab7082bb47f3b0a160f664e77782,

title = "Research of the relationship between Li-ion battery charge performance and SOH based on MIGA-GPR method",

abstract = "Owing to the problem that it is difficult to measure State of Health (SOH) of Li-ion battery online, a method to estimate SOH of Li-ion battery using the charge performance under different SOH is put forward. By Grey Incidence Analysis (GIA), the correlation degree between the charge performance and SOH is measured. Based on Multi-island Genetic Algorithm (MIGA) and Gauss Process Regression (GPR), with charge performance as input, SOH is estimates and variance of estimation is also calculated. The result shows that the above-mentioned method can evaluate SOH well.",

keywords = "Charge performance, Gauss process regression, Grey incidence analysis, Multi-island genetic algorithm, SOH",

author = "Sun Peikun and Wang Zhenpo",

note = "Publisher Copyright: {\textcopyright} 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.; Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 ; Conference date: 15-11-2015 Through 17-11-2015",

year = "2016",

month = jun,

day = "1",

doi = "10.1016/j.egypro.2016.06.086",

language = "English",

volume = "88",

pages = "608--613",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - Research of the relationship between Li-ion battery charge performance and SOH based on MIGA-GPR method

AU - Peikun, Sun

AU - Zhenpo, Wang

PY - 2016/6/1

Y1 - 2016/6/1

N2 - Owing to the problem that it is difficult to measure State of Health (SOH) of Li-ion battery online, a method to estimate SOH of Li-ion battery using the charge performance under different SOH is put forward. By Grey Incidence Analysis (GIA), the correlation degree between the charge performance and SOH is measured. Based on Multi-island Genetic Algorithm (MIGA) and Gauss Process Regression (GPR), with charge performance as input, SOH is estimates and variance of estimation is also calculated. The result shows that the above-mentioned method can evaluate SOH well.

AB - Owing to the problem that it is difficult to measure State of Health (SOH) of Li-ion battery online, a method to estimate SOH of Li-ion battery using the charge performance under different SOH is put forward. By Grey Incidence Analysis (GIA), the correlation degree between the charge performance and SOH is measured. Based on Multi-island Genetic Algorithm (MIGA) and Gauss Process Regression (GPR), with charge performance as input, SOH is estimates and variance of estimation is also calculated. The result shows that the above-mentioned method can evaluate SOH well.

KW - Charge performance

KW - Gauss process regression

KW - Grey incidence analysis

KW - Multi-island genetic algorithm

KW - SOH

UR - http://www.scopus.com/inward/record.url?scp=85007545172&partnerID=8YFLogxK

U2 - 10.1016/j.egypro.2016.06.086

DO - 10.1016/j.egypro.2016.06.086

M3 - Conference article

AN - SCOPUS:85007545172

SN - 1876-6102

VL - 88

SP - 608

EP - 613

JO - Energy Procedia

JF - Energy Procedia

T2 - Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015

Y2 - 15 November 2015 through 17 November 2015

ER -

Research of the relationship between Li-ion battery charge performance and SOH based on MIGA-GPR method

Abstract

Keywords

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