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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received December 22, 2022
Revised April 7, 2023
Accepted April 17, 2023

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Junseop Shin¹ Yeonsoo Kim^2† Jong Min Lee^1†

¹School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea ²Department of Chemical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea

kimy3@kw.ac.kr, jongmin@snu.ac.kr

Korean Journal of Chemical Engineering, August 2023, 40(8), 1850-1862(13), 10.1007/s11814-023-1476-1

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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

Keywords

Lithium-ion Battery Cycle Life Prediction Feature Construction Machine Learning

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