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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

Author(s): Renxiong Liu*

Volume 15, Issue 5, 2022

Published on: 02 October, 2020

Article ID: e060422186535 Pages: 7

DOI: 10.2174/2666255813999201002152742

Price: $65

Abstract

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL).

Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting of multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm.

Results: Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error.

Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

Keywords: Lithium-ion battery, RUL prediction, MKELM, multiple kernel, DE algorithm, mean square error

Graphical Abstract

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