New Research Highlights Importance of Charging Coordination in Electric Vehicle Network

Researchers from Hong Kong Polytechnic University have stressed the need for improved charging coordination in the electric vehicle network, citing the growing interdependence between transportation networks and power grids. With the proliferation of electric vehicles, the transportation network and power grid have become increasingly coupled, posing challenges for both networks. Financial support for the research came from the National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, Hong Kong Research Grants Council, and Otto Poon Charitable Foundation.

Key Takeaways:

• The study highlights the importance of charging coordination in electric vehicle networks, with a focus on maximizing overall traffic efficiency while enhancing power grid safety.
• Researchers formulated the en-route charging station recommendation problem as a constrained Markov decision process, developing an online prediction-assisted safe reinforcement learning (OP-SRL) method to learn the optimal and secure policy.
• The proposed method outperformed baselines in terms of road network efficiency, power grid safety, and EV user satisfaction in comprehensive experimental studies.
• The case study on a real-world network illustrated the applicability of the proposed method in a practical context.
• The research is a critical step in addressing the challenges of charging coordination in electric vehicle networks, which is essential for developing sustainable transportation systems.

Statistics:

• The study was conducted on two networks: the Nguyen-Dupuis network and a large-scale real-world road network, coupled with IEEE 33-bus and IEEE 69-bus distribution systems, respectively.
• The proposed method achieved an average increase of 23.4% in road network efficiency, a 17.5% improvement in power grid safety, and a 25.1% increase in EV user satisfaction compared to the baseline methods.
• The research was supported by the National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, Hong Kong Research Grants Council, and Otto Poon Charitable Foundation.

Sources:

• Online Prediction-assisted Safe Reinforcement Learning for Electric Vehicle Charging Station Recommendation In Dynamically Coupled Transportation-power Systems. Transportation Research Part C-emerging Technologies, 2025;176.
• NewsRx. New Data from Hong Kong Polytechnic University Illuminate Findings in Technology (Online Prediction-assisted Safe Reinforcement Learning for Electric Vehicle Charging Station Recommendation In Dynamically Coupled Transportation-power Systems). Journal of Engineering. July 7, 2025; p 1959.
• Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.