Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks

Mohd Herwan, Sulaiman and Zuriani, Mustaffa and Amir Izzani, Mohamed and Ahmad Salihin, Samsudin and Muhammad Ikram, Mohd Rashid (2024) Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks. Energy, 311 (133417). pp. 1-10. ISSN 0360-5442 (Print), 1873-6785 (Online). (In Press / Online First) (In Press / Online First)

Preview	Pdf Battery state of charge estimation for electric vehicle_ABST.pdf Download (251kB) | Preview
	Pdf Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks.pdf Restricted to Repository staff only Download (3MB) | Request a copy

Abstract

Accurate estimation of the state of charge (SoC) in electric vehicle (EV) batteries is essential for effective battery management and optimal performance. This study investigates the application of Kolmogorov-Arnold Networks (KAN) for SoC estimation, comparing its performance against Artificial Neural Networks (ANN) and a hybrid Barnacles Mating Optimizer-deep learning model (BMO-DL). The dataset, derived from simulations involving a lithium polymer cell model (ePLB C020) in an electric car similar to Nissan Leaf EV, encompasses 68,741 instances, divided into training and testing sets. Three KAN models were developed and evaluated based on root mean square error (RMSE), mean absolute error (MAE), maximum error (MAX), and coefficient of determination (R2). Residual analysis indicates that KAN-Model 1 performs the best, with residuals closely clustered around zero and no significant patterns, suggesting reliable and unbiased predictions. KAN-Model 2 also performs well but exhibits some nonlinear trends in the residuals. ANN and BMO-DL models show larger deviations and less consistent performance. These findings highlight the potential of KAN for enhancing SoC estimation accuracy in EV applications.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Artificial neural networks (ANN); Battery management; Electric vehicles (EV); Hybrid metaheuristic-deep learning; Kolmogorov-Arnold networks (KAN); State of charge
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Industrial Sciences And Technology Faculty of Computing Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	13 Nov 2024 01:27
Last Modified:	13 Nov 2024 01:46
URI:	http://umpir.ump.edu.my/id/eprint/42915
Download Statistic:	View Download Statistics

Actions (login required)

View Item