Title

Multi-step parallel-strategy for estimating the remaining useful life of batteries

Document Type

Conference Proceeding

Publication Date

6-12-2017

Publication Title

Canadian Conference on Electrical and Computer Engineering

Abstract

This paper aims to study the use of a multistep parallel-strategy (MSPS) for long-term estimation of the remaining useful life of the Li-ion batteries. Various extreme learning machines (ELMs) including standard ELM, kernel ELMs and online sequential ELM (OS-ELM) are used along with the parallel strategy for multi-step prognosis. These multistep predictors are trained by means of constant current Li-ion battery datasets. The attained results present the effectiveness of these MSPS techniques in the long-term prediction of the remaining useful life of the Li-ion batteries. Besides, the OS-ELM predictor outperforms other techniques in terms of the root mean square error (RMSE) and the RUL estimation error.

DOI

10.1109/CCECE.2017.7946748

ISSN

08407789

ISBN

9781509055388

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