Journal
2020 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/VPPC49601.2020.9330987
Keywords
Lithium-ion batteries; Capacity estimation; data-driven technique; NARX model
Funding
- European Union [824256]
- Flanders Make
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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.
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