Quantum-Assisted Optimization Framework for Dynamic Wireless Electric Vehicle Charging in Highway Networks

Researchers from South China University of Technology have proposed a novel quantum computing-assisted optimization framework for modeling, operation, and control of wireless dynamic charging infrastructure in urban highway networks. The framework leverages Variational Quantum Algorithms to address the high-dimensional, multi-objective optimization problem associated with real-time energy dispatch, charging pad utilization, and traffic flow coordination. The results demonstrate significant enhancements in energy efficiency, traffic-aware adaptive charging strategies, and renewable energy utilization.

Key Takeaways:

• The proposed quantum-assisted optimization framework enhances energy efficiency by reducing energy losses by up to 18% compared to classical optimization methods.
• Traffic-aware adaptive charging strategies improve SOC recovery by 25% during peak congestion periods while ensuring equitable energy allocation among different vehicle types.
• The framework facilitates a 30% increase in renewable energy utilization, aligning energy dispatch with periods of high solar and wind generation.
• Key insights from the case study highlight the critical impact of vehicle alignment, speed variations, and congestion on wireless charging performance, emphasizing the need for intelligent scheduling and real-time optimization.
• The proposed framework contributes to advancing the integration of quantum computing into sustainable transportation planning, offering a scalable and robust solution for next-generation EV charging infrastructure.
• A comprehensive case study is conducted on a 50 km urban highway network equipped with 20 charging pad segments, supporting an average traffic flow of 10,000 EVs per day.
• The research emphasizes the need for intelligent scheduling and real-time optimization to mitigate the impact of vehicle alignment, speed variations, and congestion on wireless charging performance.

Statistics:

• Energy losses reduced by up to 18% compared to classical optimization methods.
• SOC recovery improved by 25% during peak congestion periods.
• Renewable energy utilization increased by 30% through alignment with high solar and wind generation periods.
• Average traffic flow: 10,000 EVs per day on a 50 km urban highway network equipped with 20 charging pad segments.

Sources:

• "Quantum-Inspired Multi-Objective Optimization Framework for Dynamic Wireless Electric Vehicle Charging in Highway Networks Under Stochastic Traffic and Renewable Energy Variability." World Electric Vehicle Journal, 2025,16(4):221. (World Electric Vehicle Journal - http://www.mdpi.com/journal/wevj)
• NewsRx. New Research on Renewable Energy from South China University of Technology Summarized (Quantum-Inspired Multi-Objective Optimization Framework for Dynamic Wireless Electric Vehicle Charging in Highway Networks Under Stochastic Traffic and ...). Ecology, Environment & Conservation. May 16, 2025; p 69.