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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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Elsevier
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42915/ 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 |
| _version_ | 1848826734000996352 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:49:31Z |
| format | Article |
| id | ump-42915 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:49:31Z |
| publishDate | 2024 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42915/ http://umpir.ump.edu.my/id/eprint/42915/ http://umpir.ump.edu.my/id/eprint/42915/ 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 |