Geodesic
Kolmogorov–Arnold neural networks technique for the state of charge estimation for Li-ion batteries
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 17 (2024) no. 4, pp. 22-31 Cet article a éte moissonné depuis la source Math-Net.Ru

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Kolmogorov–Arnold Network (KAN) is an advanced type of neural network developed based on the Kolmogorov–Arnold representation theorem, offering a new approach in the field of machine learning. Unlike traditional neural networks that use linear weights, KAN applies univariate functions parameterized by splines, allowing it to flexibly capture and learn complex activation patterns more effectively. This flexibility not only enhances the model's predictive capability but also helps it handle complex issues more effectively. In this study, we propose KAN as a potential method to accurately estimate the state of charge (SoC) in energy storage devices. Experimental results show that KAN has a lower maximum error compared to traditional neural networks such as LSTM and FNN, demonstrating that KAN can predict more accurately in complex situations. Maintaining a low maximum error not only reflects KAN's stability but also shows its potential in applying deep learning technology to estimate SoC more accurately, thereby providing a more robust approach for energy management in energy storage systems.
Keywords: state of charge (SoC), Kolmogorov–Arnold networks, energy storage, neural network. 
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     author = {M. H. Dao and F. Liu and D. N. Sidorov},
     title = {Kolmogorov{\textendash}Arnold neural networks technique for the state of charge estimation for {Li-ion} batteries},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a, Matemati\v{c}eskoe modelirovanie i programmirovanie},
     pages = {22--31},
     year = {2024},
     volume = {17},
     number = {4},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/VYURU_2024_17_4_a1/}
}
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M. H. Dao; F. Liu; D. N. Sidorov. Kolmogorov–Arnold neural networks technique for the state of charge estimation for Li-ion batteries. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 17 (2024) no. 4, pp. 22-31. http://geodesic.mathdoc.fr/item/VYURU_2024_17_4_a1/

[1] Chen Zhilong, He Ting, Mao Yingzhe, Zhu Wenlong, Xiong Yifeng et all., “State of Charge Estimation Method of Energy Storage Battery Based on Multiple Incremental Features”, Journal of The Electrochemical Society, 171:7 (2024), 070522, 12 pp. | DOI

[2] Hu Xiaosong, Jiang Jiuchun, Cao Dongpu, Egardt B., “Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling”, IEEE Transactions on Industrial Electronics, 63:4 (2016), 2645–2656 | DOI

[3] Dreglea A., Foley A., Hager U., Sidorov D., Tomin N., “Hybrid Renewable Energy Systems, Load and Generation Forecasting, New Grids Structure, and Smart Technologies”, Solving Urban Infrastructure Problems Using Smart City Technologies, 2021, 475–484 | DOI

[4] Bockrath S., Rosskopf A., Koffel S., Waldhör S., Srivastava K., Lorentz V.R.H., “State of Charge Estimation Using Recurrent Neural Networks with Long Short-Term Memory for Lithium–Ion Batteries”, IECON 2019 – 45th Annual Conference of the IEEE Industrial Electronics Society (Lisbon), 2019, 2507–2511 | DOI

[5] Tian Jinpeng, Chen Cheng, Shen Weixiang, Sun Fengchun, Xiong Rui, “Deep Learning Framework for Lithium–Ion Battery State of Charge Estimation: Recent Advances and Future Perspectives”, Energy Storage Materials, 61 (2023), 102883 | DOI

[6] Liu Ziming, Wang Yixuan, S. Vaidya, F. Ruehle, J. Halverson, M. Soljacic, T.Y. Hou, M. Tegmark, KAN: Kolmogorov–Arnold Networks, 2024, 48 pp., arXiv: 2404.19756 | DOI

[7] Kolmogorov A.N., “On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition”, Doklady Akademii Nauk SSSR, 114:5 (1957), 953–956 | Zbl

[8] Arnold V.I., “On the Representation of Continuous Functions of Three Variables as Superpositions of Continuous Functions of Two Variables”, Doklady Akademii Nauk SSSR, 114:4 (1957), 679–681 | Zbl

[9] Hornik K., Stinchcombe M., White H., “Multilayer Feedforward Networks are Universal Approximators”, Neural Networks, 2:5 (1989), 359–366 | DOI | MR | Zbl

[10] De Boor C., “A Practical Guide to Splines”, Mathematics of Computation, 27:149 (1978) | DOI | MR | Zbl

[11] Dao Minh Hien, Sidorov D.N., “Estimation of the State of Charge of Energy Storage Devices Using Kolmogorov–Arnold Networks”, Dynamic Systems and Computer Sciences: Theory and Applications (DYSC 2024), Proceedings of the 6th International Conference, Irkutsk, 2024, 206–209 | DOI

[12] Kollmeyer P., “Panasonic 18650PF Li–ion Battery Data”, Mendeley Data, 2018 | DOI

[13] Kollmeyer P., Skells M., “Turnigy Graphene 5000mAh 65C Li-ion Battery Data”, Mendeley Data, 2020 | DOI

[14] Vidal C., Kollmeyer P.J., “Panasonic 18650PF Li-ion Battery Data and Example FNN and LSTM Neural Network SOC Estimator Training Script”, Mendeley Data, 2021 | DOI

[15] GitHub, (accessed on 20.10.2024) https://github.com/KindXiaoming/pykan