Comparative Study of Artificial Intelligence and Machine Learning Algorithms on Synthetic Lithium-ion Cell Data

The proposed paper presents a comprehensive comparative study of various Artificial Intelligence and Machine Learning algorithms applied to synthetic data of Lithium-Ion cell. This study utilizes complex datasets generated using a detailed battery cell model, simulating both charging and discharging processes using MATLAB ${ }^{\text {TM }}$. Once generated, the data is subsequently loaded into NI SystemLink ${ }^{\mathrm{TM}}$ Enterprise, where it is processed and analyzed using the Jupyter Notebook feature. Multiple AI/ML models are trained and evaluated on this data, with their performances being compared based on key metrics such as prediction accuracy, computational efficiency, and robustness to determine the most effective algorithms for this application.