Multi-step-ahead prediction techniques for Lithium-ion batteries condition prognosis

Document Type

Conference Proceeding

Publication Date

2-6-2017

Publication Title

2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

First Page

4675

Last Page

4680

Abstract

This paper focuses on the use of different multi-step prediction techniques for long-term prognosis of the Lithium-ion batteries condition. Various inductive algorithms including adaptive neuro-fuzzy inference systems, random forests, and group method of data handling are used along with three strategies for multi-step prediction and prognosis. These prediction strategies including iterative, direct, and DirRec schemes make use of the historical and current data in different manners to forecast the future values of the capacity over a long horizon for estimation of the remaining useful life (RUL) of the Li-ion batteries. These multi-step predictors are trained by means of constant current Li-ion battery datasets. The attained results present the effectiveness of these techniques for the long-term prognosis of the RUL of the batteries. Besides, a statistical analysis of the attained results indicates that the RF predictor outperforms other techniques.

DOI

10.1109/SMC.2016.7844969

ISBN

9781509018970

Link to Full Text

Share

COinS