Bio-Inspired Multi-Agent Energy Management for EV Fleets Using Adaptive Optical Wireless Power Transfer
Abstract
A novel approach combining bio-inspired multi-agent systems with optical wireless power transfer for dynamic EV fleet management. The system uses adaptive LED arrays and reinforcement learning to optimize both power distribution and charging schedules, while incorporating real-time fleet behavior patterns.
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Research Gap Analysis
Current research lacks integration between physical power transfer mechanisms and intelligent coordination systems for EV charging. Additionally, bio-inspired algorithms haven't been applied to optical wireless power transfer optimization.
Bio-Inspired Multi-Agent Energy Management for EV Fleets Using Adaptive Optical Wireless Power Transfer
Motivation
Electric vehicle fleets face significant challenges in energy management and charging optimization. Current solutions often treat vehicles as isolated units rather than an interconnected ecosystem. While optical wireless power transfer shows promise for flexible charging, existing implementations lack intelligent coordination and adaptive capabilities. Additionally, multi-agent systems have demonstrated success in complex coordination tasks but haven't been fully leveraged for physical power distribution optimization.
Proposed Approach
The research proposes a hybrid system combining three key technologies:
- Adaptive LED-Based OWPT Network
- Distributed LED arrays installed in parking structures and charging stations
- Dynamic beam forming and power allocation based on vehicle positions
- Real-time adjustment of power transfer patterns using sensor feedback
- Bio-Inspired Multi-Agent Control System
- Swarm intelligence algorithms for coordinating charging priorities
- Agent-based negotiation for power distribution optimization
- Self-organizing behavior patterns based on historical fleet data
- Reinforcement Learning Optimization Layer
- Offline learning from historical charging patterns
- Online adaptation to real-time demand changes
- Multi-objective optimization considering power efficiency, cost, and fleet availability
Expected Outcomes
- 20-30% improvement in overall charging efficiency
- Reduced peak load on the power grid through intelligent distribution
- More flexible and scalable charging infrastructure
- Enhanced fleet availability through predictive charging scheduling
- Reduced infrastructure costs compared to traditional charging solutions
Potential Applications
- Commercial EV fleet operations
- Public transportation systems
- Autonomous vehicle charging stations
- Smart parking structures
- Mobile charging solutions for emergency vehicles
The system would be particularly valuable in urban environments where traditional charging infrastructure is constrained by space and power grid limitations.
Proposed Methodology
Develop a three-layer system combining adaptive LED-based optical wireless power transfer, bio-inspired multi-agent coordination, and reinforcement learning optimization for dynamic EV fleet charging management.
Potential Impact
The research could revolutionize EV fleet charging infrastructure by enabling more efficient, flexible, and scalable solutions while reducing infrastructure costs and grid load. This would accelerate EV adoption in commercial and public transportation sectors.