Estimating the State of Charge (SoC) of a battery is a complex task influenced by various chemical and environmental factors. Traditional SoC estimation methods often rely on high-frequency measurements taken directly at the battery terminals and require detailed knowledge of battery models and parameters. These obstacles make them difficult to implement in practical scenarios. We propose a novel algorithm that estimates SoC using measurements from the Point of Common Coupling (PCC), which is more readily accessible. Although PCC-based SoC estimation is inherently challenging our approach overcomes these limitations by treating the electric vehicle as a black box. The proposed method is model- and age-independent, making it adaptable across different battery types and usage scenarios. Our method explicitly uses a third-party measuring device named smart plug without requiring any information about the internal battery model or characteristics. We present a Convolutional Neural Network (CNN)-based algorithm that uses eight extracted features from smart plug measurements to estimate SoC with low error, offering a scalable and generalizable solution for real-world applications.
