NARX Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycles Measurements

Khalfi, Jaouad; Boumaaz, Najib; Soulmani, Abdallah; Laafar, Sara; Maali, Asmaa; Laadissi, El Mehdi

doi:10.1007/978-981-19-6223-3_57

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although literature elaborates on this model by incorporating temperature and lifecycle effects, it lacks integration of the state of charge effect. Meanwhile, ANN have gained widespread application as intelligent mathematical tools for data-driven modeling [21]. Their suitability for handling nonlinear and intricate frameworks makes ANN a compelling choice.…”

Section: Introductionmentioning

confidence: 99%

Enhancing battery system identification: nonlinear autoregressive modeling for Li-ion batteries

Mossaddek,

Laadissi,

Ennawaoui

et al. 2024

IJECE

View full text Add to dashboard Cite

Precisely characterizing Li-ion batteries is essential for optimizing their performance, enhancing safety, and prolonging their lifespan across various applications, such as electric vehicles and renewable energy systems. This article introduces an innovative nonlinear methodology for system identification of a Li-ion battery, employing a nonlinear autoregressive with exogenous inputs (NARX) model. The proposed approach integrates the benefits of nonlinear modeling with the adaptability of the NARX structure, facilitating a more comprehensive representation of the intricate electrochemical processes within the battery. Experimental data collected from a Li-ion battery operating under diverse scenarios are employed to validate the effectiveness of the proposed methodology. The identified NARX model exhibits superior accuracy in predicting the battery's behavior compared to traditional linear models. This study underscores the importance of accounting for nonlinearities in battery modeling, providing insights into the intricate relationships between state-of-charge, voltage, and current under dynamic conditions.

show abstract

Section: Introductionmentioning

confidence: 99%