A Comparative Study of Machine Learning Models for SOC Prediction

Accurate State of Charge (SOC) estimation ensures the more safe and more efficient operation of lithium-ion batteries, especially as electric vehicles and energy storage systems do function. Using a thorough dataset, this study presents a comparative analysis for various machine learning models for SOC prediction that MATLAB simulations generated under diverse operating conditions. To capture battery behaviour across complete charge and discharge cycles, key battery parameters are analysed, such as voltage, current, temperature, and aging effects. The model's performance is improved through application of advanced preprocessing techniques and feature engineering methods. A number of algorithms get evaluated in light of prediction accuracy, robustness, and also computational efficiency across a range starting from customary regression methods and ending in deep learning architectures. As per results, the proposed approaches greatly outperform conventional estimation techniques that show greater reliability for integration into real-time battery management systems. This work develops some predictive models, as well as these models support smarter energy management while prolonging battery lifespan.