### Proposed System Overview and Features  
This paper proposes a system composed of three core elements: blockchain-based data management, robust federated learning, and multi-stage prediction and optimization. Initially, black box data are encrypted and stored locally in each EV, with only the hash of the ciphertext and metadata recorded on the blockchain. This blockchain adopts a partially decentralized structure that limits consensus participation to authorized nodes and processes transactions in batches, simultaneously enhancing privacy and efficiency. The driving data collected in this manner are utilized for robust energy consumption model training through the FL-QLMS algorithm, which protects privacy. Specifically, FL-QLMS uses a reliable validation dataset to assess the performance of each vehicle's local model and selects participants for the global model update based on this assessment. This minimizes the impact of malicious or low-quality data, ensuring model robustness. Moreover, the proposed system goes beyond individual EV driving unit demand predictions by employing spatio-temporal clustering and multi-stage deep prediction to forecast specific regional and temporal charging demands. This approach considers both local demand variability and mid-to-long-term trends, enabling adaptive charging infrastructure deployment and dynamic pricing policy formulation. It also evaluates the potential of EVs as demand response resources and their utilization for power grid stabilization. The system architecture proposed here differs from traditional centralized management and prediction systems in several ways. Firstly, by integrating encryption with blockchain technology, it establishes a secure data-sharing foundation while ensuring data sovereignty for vehicle owners. Additionally, the collection of non-standard data and participation of various stakeholders enhance the accuracy and robustness of energy usage predictions [26]. Furthermore, by linking federated learning with a blockchain incentive system, the system motivates EV users to provide data [27], facilitating voluntary data circulation and collaborative decision-making among service providers. This can lead to the development of personalized services such as charging recommendations and energy transactions and ultimately support the harmonious integration of EVs and the power grid in the long term. Moreover, this study has academic significance as it explores the multifaceted value that blockchain and federated learning can generate in the energy sector, particularly from a data 3A (availability, authorization, audit) perspective, balancing privacy and accountability. It also suggests the potential for expansion into various domains. Most importantly, it proposes the value of this infrastructure as a data foundation for the activation and efficient operation of EVs, a key driver of the energy transition; thus, it is also expected to contribute policy-wise. In summary, the blockchain-federated learning-based EV data platform presented in this paper proposes a new paradigm to simultaneously address the multifaceted challenges of data accessibility, privacy protection, and energy efficiency enhancement. However, realizing this vision requires the establishment of mutual trust and cooperative relationships among stakeholders, as well as the creation of supporting institutional and technical foundations. Particularly, challenges such as a fair compensation system for federated learning participation, advanced data verification mechanisms, and ensuring interoperability with various service platforms require ongoing research and social consensus. Nevertheless, this study is significant as it explores innovative methodologies for uncovering and safely and efficiently utilizing the potential value of EV operational data. It is expected to have a ripple effect on the related industry ecosystem as a pioneering attempt to open new horizons in energy data governance.