###### Abstract
This consortium establishes a data governance committee to oversee the selection process of the validation dataset, ensuring fairness and transparency. This ensures the reliability and objectivity of the dataset used in FL-QLMS. The reliability of the validation dataset is evaluated based on data quality metrics defined in the international standards ISO/IEC 25024:2015 [22] and ISO 20762:2018 [23], which specifically deal with EV driving data. The dataset is comprehensively judged in terms of (1) completeness, (2) accuracy, (3) consistency, (4) timeliness, and (5) accessibility, as well as EV-specific criteria such as (6) energy consumption data under various driving conditions, (7) driving range data by battery charge status, and (8) data analyzing the impact of external environmental conditions. The evaluation methods for each metric utilize the measurement procedures and analysis techniques provided in the respective standards. Only datasets that satisfy all these criteria are selected as validation datasets for FL-QLMS. If no dataset completely satisfies the data quality criteria, the following alternatives are sequentially considered. First, the criteria are partially relaxed to utilize the next-best dataset. In this case, the relaxed criteria and the limitations of the resulting dataset are clearly documented to maintain transparency. If securing even the next-best dataset is difficult, collecting additional data from authorized external data sources (government agencies, research institutions, etc.) is considered. If it is still challenging to compose an appropriate validation dataset, the application of the FL-QLMS technique itself is suspended, and alternative approaches are explored. As EV technologies evolve and usage environments change, the validation dataset is periodically updated and managed. The data governance committee evaluates the validity of the dataset quarterly and takes measures such as collecting new data or filtering existing data when necessary. In this process, changes in the performance of the FL-QLMS model are monitored to analyze the impact of dataset updates on model performance. In addition, regular feedback is collected from each participant in FL-QLMS to gather ideas for dataset quality improvement and expansion. Through this continuous update and management, the FL-QLMS technique can produce valid and reliable results in the long term. This approach minimizes the negative impact of outlier data and dishonest participation while effectively addressing non-standard data and non-independent and identically distributed (non-IID) issues. The robust model aggregation (RMA) technique recently proposed by [PERSON] and [PERSON] [24] is similar to our approach but differs in that it verifies model loss trends in real-time across continuous rounds. Building on this background, this paper proposes an intelligent power grid management system based on federated learning that securely stores and manages EV black box data on the blockchain and uses the operational information of multiple vehicles to predict energy usage. The proposed system is based on data sovereignty and privacy protection, securing interoperability between EVs and the power grid while promoting rational resource management and efficiency improvements through integrated data analysis. This is expected to be a key foundation supporting the stable acceptance and sustainable utilization of EVs in the era of energy transition. Most importantly, the proposed system in this study offers significant implications for realizing the potential value of EV operational data. According to an analysis by KPMG [25], the market for connected car data-based services is expected to grow to approximately USD 750 billion by 2030. However, data silos and privacy issues have been obstacles to realizing this potential. Thus, the combination of a decentralized data management system based on blockchain and privacy-protective analysis techniques could accelerate data-driven innovation across the mobility industry, not just in the energy sector.