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 language | English |
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Title of host publication | 2020 IEEE Vehicle Power and Propulsion Conference (VPPC) |
Place of Publication | Gijon, Spain |
Publisher | IEEE |
Pages | 1 to 7 |
Number of pages | 7 |
DOIs | |
Publication status | Published - 2020 |