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

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 Neur...

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Main Authors: Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Amir Izzani, Mohamed, Ahmad Salihin, Samsudin, Muhammad Ikram, Mohd Rashid
Format: Article
Language:English
English
Published: Elsevier 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/42915/1/Battery%20state%20of%20charge%20estimation%20for%20electric%20vehicle_ABST.pdf
http://umpir.ump.edu.my/id/eprint/42915/2/Battery%20state%20of%20charge%20estimation%20for%20electric%20vehicle%20using%20Kolmogorov-Arnold%20networks.pdf
http://umpir.ump.edu.my/id/eprint/42915/
https://doi.org/10.1016/j.energy.2024.133417
https://doi.org/10.1016/j.energy.2024.133417
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spelling my.ump.umpir.429152025-01-16T03:16:09Z http://umpir.ump.edu.my/id/eprint/42915/ Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks Mohd Herwan, Sulaiman Zuriani, Mustaffa Amir Izzani, Mohamed Ahmad Salihin, Samsudin Muhammad Ikram, Mohd Rashid TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier 2024-12-01 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42915/1/Battery%20state%20of%20charge%20estimation%20for%20electric%20vehicle_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/42915/2/Battery%20state%20of%20charge%20estimation%20for%20electric%20vehicle%20using%20Kolmogorov-Arnold%20networks.pdf 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). (Published) https://doi.org/10.1016/j.energy.2024.133417 https://doi.org/10.1016/j.energy.2024.133417
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mohd Herwan, Sulaiman
Zuriani, Mustaffa
Amir Izzani, Mohamed
Ahmad Salihin, Samsudin
Muhammad Ikram, Mohd Rashid
Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks
description 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.
format Article
author Mohd Herwan, Sulaiman
Zuriani, Mustaffa
Amir Izzani, Mohamed
Ahmad Salihin, Samsudin
Muhammad Ikram, Mohd Rashid
author_facet Mohd Herwan, Sulaiman
Zuriani, Mustaffa
Amir Izzani, Mohamed
Ahmad Salihin, Samsudin
Muhammad Ikram, Mohd Rashid
author_sort Mohd Herwan, Sulaiman
title Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks
title_short Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks
title_full Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks
title_fullStr Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks
title_full_unstemmed Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks
title_sort battery state of charge estimation for electric vehicle using kolmogorov-arnold networks
publisher Elsevier
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42915/1/Battery%20state%20of%20charge%20estimation%20for%20electric%20vehicle_ABST.pdf
http://umpir.ump.edu.my/id/eprint/42915/2/Battery%20state%20of%20charge%20estimation%20for%20electric%20vehicle%20using%20Kolmogorov-Arnold%20networks.pdf
http://umpir.ump.edu.my/id/eprint/42915/
https://doi.org/10.1016/j.energy.2024.133417
https://doi.org/10.1016/j.energy.2024.133417
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score 13.232414

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