## 3 Results
### Verification of Blockchain Efficiency  
This section presents the experimental results to verify the efficiency of the proposed partially decentralized blockchain architecture. Initially, the transaction processing throughput (TPS) and block confirmation delay changes were analyzed based on the number of electric vehicles (EVs). Figure 6 illustrates the TPS changes when the number of EVs increased from 100 to 1000. For comparison, a fully decentralized approach was assumed, where each node generates transactions independently [20]. The experimental results showed that, in a fully decentralized approach, the TPS significantly decreased as the number of EVs increased. This trend was attributed to blockchain network overload caused by each node generating individual transactions. In contrast, the proposed method maintained a stable level of TPS regardless of the number of nodes (indicated by the arrow in Figure 7), thanks to a design that minimizes unnecessary individual transactions through transaction merging and compression. These results suggest that adopting a partially decentralized structure could contribute to solving the scalability issues of blockchain. However, to support actual commercial-level EV infrastructure, network scalability needs to extend to tens of thousands of vehicles, indicating that more sophisticated performance enhancement techniques are necessary. Figure 6: Comparison of TPS improvement effects with scalability solutions. Figure 7: TPS changes according to the number of electric vehicles. One potential solution is the integration of blockchain sharding technology. Sharding divides the blockchain network into multiple partitions, allowing each shard to process transactions independently, thereby linearly increasing the overall system throughput [30]. Indeed, next-generation blockchain platforms like Ethereum 2.0 and Polkadot have adopted sharding as a core scalability solution [31; 32]. Combining the hierarchical structure proposed in this study with sharding could allow independent consensus and block creation for each EV cluster. Additionally, applying dynamic sharding during the transaction merging process could also offer advantages in terms of load balancing. Another promising approach is off-chain transaction processing. Off-chain methods handle numerous transactions in parallel outside the blockchain and then record only the final results on the blockchain, significantly improving transaction throughput and reducing latency [33]. Examples include payment channels, like the Lightning Network or Raiden Network, and sidechain technologies, such as Plasma and TrueBit [34]. Especially in environments like EV networks, where numerous micro-transactions occur frequently, off-chain processing can effectively handle large volumes of P2P transactions separately before periodically synchronizing with the main blockchain [35]. Recently, there have been notable attempts to enhance the scalability of the main chain by using zero-knowledge proof-based rollup technology to compress and verify multiple off-chain transactions [36]. Furthermore, a hybrid structure linking on-chain and off-chain technologies is emerging as a promising scalability solution. For instance, using blockchain interoperability protocols like Cosmos or Polkadot, multiple blockchains specialized for individual service areas can be loosely coupled, addressing scalability and sovereignty issues simultaneously [37]. In such a multi-chain structure, the hierarchical model proposed in this paper could be applied to sub-domains, with inter-domain information exchange and collaboration performed through mechanisms like a relay chain [38]. This could enable data linkage and value exchange across the diverse entities of the EV ecosystem, including vehicles, charging stations, and grid operators. To fully leverage the potential of blockchain in large-scale EV infrastructure, it is necessary to synergistically utilize various scalability solutions such as hierarchical consensus models, sharding, off-chain, and multi-chain paradigms [39].