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
Recommended Citation
Razavi-Far, Roozbeh; Chakrabarti, Shiladitya; and Saif, Mehrdad. (2017). Multi-step parallel-strategy for estimating the remaining useful life of batteries. Canadian Conference on Electrical and Computer Engineering.
https://scholar.uwindsor.ca/electricalengpub/149