Kolmogorov–Arnold neural networks technique for the state of charge estimation for Li-ion batteries

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.