## 4 Discussion and Conclusions  
### Significance of the Research  
This study presents a new paradigm for the management and utilization of electric vehicle (EV) operational data by proposing an integrated blockchain-federated learning framework. Specifically, by combining a partially decentralized blockchain structure with a Byzantine-robust FL algorithm, this research addresses privacy violations and single points of failure inherent in traditional centralized management systems while also achieving revolutionary improvements in model performance and robustness. From the perspective of black-box data management, the proposed blockchain structure ensures both data confidentiality and integrity through the use of RSA encryption and SHA-256 hash chains in a zero-knowledge proof framework [1]. Moreover, by optimizing transaction merging and limiting consensus participation, it enhances processing performance and effectively mitigates communication and computational costs proportional to the number of consensus nodes. Notably, the management of data ownership and anonymized tokens on the blockchain ensures user control over personal information and lays the foundation for activating the data economy [2]. In terms of developing energy consumption prediction models, the proposed framework also innovates in accuracy, scalability, and robustness. The FL-QLMS algorithm evaluates local model performance using external validation data and performs model merging through dynamic selection and weighted averaging, considering Byzantine robustness, thus overcoming the limitations of existing methods like FedAvg. The experimental results show that FL-QLMS maintains over 90% test accuracy, even with nearly half of the nodes being Byzantine [4]. Additionally, by employing multi-stage prediction and hierarchical modeling, it captures short- and long-term energy usage patterns, achieving more than 26% improvement in RMSE and 23% in MAE compared to conventional single models [5]. The integration of these technologies in the proposed system not only dramatically improves the technical limitations of existing EV data management infrastructures but also creates a reliable information-sharing foundation for optimizing energy consumption and stabilizing power grids. Most importantly, by implementing the principles of data 3A (availability, accountability, auditability) [6], it facilitates collaborative decision-making among various stakeholders within the EV ecosystem and fosters a virtuous cycle of data-driven innovation. This is expected to contribute to enhancing battery grid efficiency, expanding the integration of renewable energy, and ultimately addressing the societal challenge of achieving carbon neutrality. From an industrial perspective, this research is significant as it provides a foundation for exploring energy-mobility convergence services. With the automation of the entire data collection-learning-application cycle enabled by the proposed framework, various business models, such as optimal placement of charging infrastructure, power grid demand forecasting, and dynamic pricing design, can be created [7]. Particularly, if the energy usage model trained via FL is implemented on blockchain-based smart contracts, it could significantly contribute to the promotion of EV adoption and the development of the charging industry. Furthermore, the design philosophy and core technologies of the proposed system are expected to have implications for adjacent fields such as vehicle-to-grid (V2G), autonomous driving, and smart cities. For example, the privacy-protecting data sharing protocol and incentive mechanism established in this study can be directly applied to the collection and utilization of autonomous vehicle operation data. Additionally, by evolving into a multi-agent reinforcement learning framework for integrated traffic-energy optimization, the proposed system can transcend the value of EV infrastructure to become a key driver for data-based smart city operations [8].