Cluster-Based Regression Models for Enhanced Battery State of Charge (SOC) Prediction

Battery performance optimization and effective energy management are dependent on accurate state-of-charge (SOC) prediction. The problems presented by different operational and environmental situations in the estimate of the SOC are the main focus of this work. In order to improve the dependability and flexibility of SOC prediction models, the suggested framework proposes a unique method that integrates data-driven insights. The results demonstrate how this strategy can enhance battery monitoring systems and facilitate applications in electric vehicle and renewable energy storage technologies.