期刊
2020 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC)
卷 -, 期 -, 页码 -出版社
IEEE
DOI: 10.1109/VPPC49601.2020.9330987
关键词
Lithium-ion batteries; Capacity estimation; data-driven technique; NARX model
资金
- European Union [824256]
- Flanders Make
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 arc 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据