A data-driven method based on recurrent neural network method for online capacity estimation of lithium-ion batteries

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

lithium-ion batteries are a convenient choice for various energy storage systems (ESS) such as electric and hybrid vehicles. Nevertheless, the characterization of capacity degradation is critical to ensure the proper performance of lithium-ion batteries. This paper presents a data-driven technique based on a recurrent neural network called nonlinear autoregressive exogenous neural network (NARX) to estimate the capacity degradation of lithium-ion batteries. The voltage charging curves, extracted from twelve nickel manganese cobalt oxide (NMC) cells with different aging trends are used to develop a predictive model for capacity estimation. The results demonstrate that the proposed model is able to estimate capacity with high accuracy and low complexity.
Original languageEnglish
Title of host publication2020 IEEE Vehicle Power and Propulsion Conference (VPPC)
Place of PublicationGijon, Spain
PublisherIEEE
Pages1 to 7
Number of pages7
ISBN (Electronic)9781728189598
DOIs
Publication statusPublished - Nov 2020

Publication series

Name2020 IEEE Vehicle Power and Propulsion Conference, VPPC 2020 - Proceedings

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