Electric vehicle (EV) adoption stands as a pivotal step in curbing carbon emissions and combating climate change. However, the persistent specter of range anxiety continues to impede widespread acceptance. Traditional state of charge (SoC) estimation methods, coupled with basic machine learning models, grapple with accuracy and adaptability limitations. Yet, the advent of federated learning heralds a transformative era in the EV landscape, placing a premium on data security and privacy. This approach involves training models across a network of distributed sources, such as individual EVs, harnessing the wealth of diverse data streams. It continually refines models, ensuring SoC calculations maintain precision as the EV fleet evolves. Notably, it fortifies against cyberattacks by obviating centralized data storage. Embracing federated learning within the EV industry not only alleviates range anxiety but also fosters sustainable transportation, underscoring the role of EVs in shaping an ecologically conscious future. This paper concentrates on SoC estimation through the prism of federated learning. Techniques including federated averaging, differential privacy techniques, and ensemble learning will be employed. To facilitate this research, a comprehensive vehicle model, encompassing the powertrain and heating circuit, was validated through real driving trips with a BMW i3 (60 Ah), yielding the requisite dataset. This dataset underwent meticulous extraction, cleansing, and exploratory data analysis involving numerous parameters. These preparatory steps lay the groundwork for the subsequent application of federated averaging, differential privacy techniques, and ensemble learning to the dataset, aiming for precise SoC estimation.