AI-Powered Dynamic Wireless Charging Networks for Autonomous EV Fleets
Abstract
A novel system combining multi-agent LLMs and dynamic wireless power transfer to optimize real-time charging for autonomous EV fleets. The system uses predictive analytics to coordinate vehicle movements and charging infrastructure, while defending against potential cyber-attacks through secure multi-agent protocols.
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Research Gap Analysis
Current research treats EV charging optimization and multi-agent systems as separate domains, missing opportunities for integrated solutions that could dramatically improve efficiency while ensuring security
AI-Powered Dynamic Wireless Charging Networks for Autonomous EV Fleets
Motivation
Current EV charging infrastructure faces significant challenges in scaling to meet the demands of growing autonomous fleet operations. Static charging stations create inefficiencies through queuing and downtime, while existing wireless charging solutions lack intelligent coordination capabilities. Additionally, as charging systems become more connected and automated, they face increasing cybersecurity risks that could disrupt critical transportation infrastructure.
Proposed Approach
The research proposes an integrated system combining three key innovations:
- Intelligent Multi-Agent Coordination
- Deploy LLM-based agents to manage both vehicles and charging infrastructure
- Implement secure communication protocols resistant to recursive blocking attacks
- Use reinforcement learning for optimal fleet movement patterns
- Dynamic Wireless Charging Infrastructure
- Develop reconfigurable coil arrays that can adapt to different vehicle types
- Implement real-time power transfer optimization using machine learning
- Create mesh networks of charging zones with load balancing
- Predictive Analytics and Security Layer
- Forecast charging demand patterns using historical data and real-time factors
- Implement blockchain-based security for charging transactions
- Develop anomaly detection for both cyber and physical system components
Expected Outcomes
- 30-40% reduction in fleet charging downtime
- 20% improvement in overall charging efficiency
- Enhanced resilience against cyber-attacks
- Scalable architecture for city-wide implementation
Potential Applications
- Autonomous taxi and delivery fleets
- Public transportation systems
- Smart city infrastructure
- Emergency response vehicle networks
- Commercial logistics operations
Proposed Methodology
Develop an integrated system combining LLM-based multi-agent coordination with dynamic wireless charging infrastructure, secured by blockchain and anomaly detection
Potential Impact
Could revolutionize urban transportation by enabling continuous operation of autonomous EV fleets while minimizing infrastructure costs and maximizing security