Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method

Xiaopeng TANG; Ke YAO; Changfu ZOU; Boyang LIU; Furong GAO

doi:10.1016/j.egypro.2019.01.320

Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method

Xiaopeng TANG, Ke YAO, Changfu ZOU, Boyang LIU, Furong GAO^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

8 Citations (Scopus)

Abstract

Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.

Original language	English
Pages (from-to)	2456-2461
Number of pages	6
Journal	Energy Procedia
Volume	158
DOIs	https://doi.org/10.1016/j.egypro.2019.01.320
Publication status	Published - Feb 2019
Externally published	Yes
Event	10th International Conference on Applied Energy, ICAE 2018 - Hong Kong, China Duration: 22 Aug 2018 → 25 Aug 2018

Bibliographical note

Publisher Copyright:
© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.

Funding

This work is supported, in part, by National Natural Science Foundation of China project (61227005) and Guangdong scientific and technological project (2017B010120002).

Keywords

Aging trajectory prediction
Bayesian Monte Carlo
Lithium-ion batteries
Model migration
State-of-health

UN SDGs

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

Access to Document

10.1016/j.egypro.2019.01.320Licence: CC BY-NC-ND

Cite this

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title = "Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method",

abstract = "Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.",

keywords = "Aging trajectory prediction, Bayesian Monte Carlo, Lithium-ion batteries, Model migration, State-of-health",

author = "Xiaopeng TANG and Ke YAO and Changfu ZOU and Boyang LIU and Furong GAO",

note = "Publisher Copyright: {\textcopyright} 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.; 10th International Conference on Applied Energy, ICAE 2018 ; Conference date: 22-08-2018 Through 25-08-2018",

year = "2019",

month = feb,

doi = "10.1016/j.egypro.2019.01.320",

language = "English",

volume = "158",

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journal = "Energy Procedia",

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TY - JOUR

T1 - Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method

AU - TANG, Xiaopeng

AU - YAO, Ke

AU - ZOU, Changfu

AU - LIU, Boyang

AU - GAO, Furong

N1 - Publisher Copyright: © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.

PY - 2019/2

Y1 - 2019/2

N2 - Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.

AB - Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.

KW - Aging trajectory prediction

KW - Bayesian Monte Carlo

KW - Lithium-ion batteries

KW - Model migration

KW - State-of-health

UR - http://www.scopus.com/inward/record.url?scp=85063901584&partnerID=8YFLogxK

U2 - 10.1016/j.egypro.2019.01.320

DO - 10.1016/j.egypro.2019.01.320

M3 - Journal Article (refereed)

AN - SCOPUS:85063901584

SN - 1876-6102

VL - 158

SP - 2456

EP - 2461

JO - Energy Procedia

JF - Energy Procedia

T2 - 10th International Conference on Applied Energy, ICAE 2018

Y2 - 22 August 2018 through 25 August 2018

ER -

Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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