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Feature construction for on-board early prediction of electric vehicle battery cycle life

  • Process Systems Engineering, Process Safety
  • Published:
Korean Journal of Chemical Engineering Aims and scope Submit manuscript

Abstract

As the worldwide environmental crisis worsens, electric vehicles (EVs) are establishing themselves as ecofriendly alternatives to conventional fossil fuel vehicles. Lithium-ion batteries (LIBs) are a typical source of energy for EVs, but it is important to predict their life in order to ensure safe and optimal operation. However, because LIBs degrade in a nonlinear fashion and their state of health varies depending on operating conditions, achieving fast and accurate cycle life prediction has been a challenge. More importantly, on-board estimation is necessary because even the identical battery cells manufactured by the same company vary in their cycle lifetimes and operational characteristics, which we cannot specify in advance. In this paper, we propose a set of novel features that enable on-board battery cycle life prediction while maintaining high memory efficiency and low calculation complexity. The features’ performances were evaluated using a variety of machine learning models, ranging from simple linear elastic nets to nonlinear neural networks.

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Abbreviations

BMS:

battery management system

CC:

constant current

CNN:

convolutional neural network

DNN (NN):

deep neural network

Enet:

elastic net

EV:

electric vehicle

GB:

gradient boosting

GPR:

gaussian process regression

KRR:

kernel ridge regression

LFP:

lithium iron phosphate

LIB:

lithium-ion battery

LSTM:

long short term memory

MAPE:

mean absolute percentage error

ML:

machine learning

NB:

naïve bayes

PCA:

principal component analysis

PI:

permutation importance

PSO:

particle swarm optimization

Qd:

discharge capacity

RFR:

random forest regression

RMSE:

root mean square error

RNN:

recurrent neural networks

RUL:

remaining useful life

SDAE:

stacked denoising autoencoder

SEI:

solid electrolyte interphase

SOH:

state of health

SVR:

support vector regression

TPWPME:

two phase wiener process with measurement errors

XGB:

extreme gradient boosting

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Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) (NRF-2016R1A5A1009592). The Institute of Engineering Research at Seoul National University provided research facilities for this work.

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Authors and Affiliations

  1. School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, Korea

    Junseop Shin & Jong Min Lee

  2. Department of Chemical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul, 01897, Korea

    Yeonsoo Kim

Authors
  1. Junseop Shin
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  2. Yeonsoo Kim
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  3. Jong Min Lee
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Correspondence to Yeonsoo Kim or Jong Min Lee.

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Shin, J., Kim, Y. & Lee, J.M. Feature construction for on-board early prediction of electric vehicle battery cycle life. Korean J. Chem. Eng. 40, 1850–1862 (2023). https://doi.org/10.1007/s11814-023-1476-1

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  • DOI: https://doi.org/10.1007/s11814-023-1476-1

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