The rapid proliferation of electric vehicles (EVs) necessitates enhanced data privacy and efficiency in EV charging systems, which are increasingly vulnerable to cyber threats and inefficiencies due to centralized data processing models. We introduce a novel data privacy framework integrating asynchronous blockchain and adaptive homomorphic encryption technologies to address these challenges within dynamic EV charging networks. Our framework employs an asynchronous blockchain architecture, allowing individual charging stations to process transactions independently without awaiting network-wide consensus, significantly reducing latency and enhancing scalability. Concurrently, an adaptive homomorphic scheme is developed to dynamically adjust encryption parameters based on real-time network load and data sensitivity, optimizing computational resources while ensuring robust privacy preservation. We evaluate the effectiveness of our proposed framework through a series of simulations that demonstrate substantial improvements in transaction processing speed and data security compared to traditional synchronous blockchain and static encryption methods. This study provides a scalable and secure approach to managing data privacy in EV charging networks. It offers a blueprint for integrating these technologies in other real-time data-intensive applications.