To address hardware constraints, the study demonstrates how on-board chargers can act as intelligent agents for distributed vehicle-grid coordination. Using a distributed framework that embeds edge intelligence directly into resource-constrained chargers, the system optimizes charging autonomously.
Validated on a high-fidelity hardware-in-the-loop platform that mirrors microgrid cyber-physical dynamics, the approach yielded significant technical and financial improvements. Experimental results show that the framework reduces memory consumption by 96.1% and communication overhead by 74.3% compared to traditional distributed coordination schemes. Additionally, unlocking this hardware-level flexibility delivered a 10.8% profit increase over uncoordinated charging setups.
The research, involving institutions such as The University of Hong Kong, Harvard University, Tsinghua University, and the University of Oxford, highlights broad economic, environmental, and social benefits for the energy landscape looking forward from 2025 to 2040.