LLM-Enhanced Predictive Battery Management with Multi-Agent Coordination for Urban EV Fleets
Abstract
A novel framework combining LLM-based multi-agent systems with predictive battery thermal management for coordinating urban EV fleets. The system leverages natural language interaction for fleet operators while optimizing battery longevity and energy efficiency through distributed intelligence and real-time thermal management.
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Research Gap Analysis
Current systems lack integration between battery management, fleet coordination, and human expertise. There's no framework that combines LLM-based human interaction with predictive battery management in a multi-agent setting.
LLM-Enhanced Predictive Battery Management with Multi-Agent Coordination for Urban EV Fleets
Motivation
Current EV fleet management systems treat battery thermal management and fleet coordination as separate challenges. While predictive battery management systems focus on individual vehicles, and multi-agent systems handle fleet coordination, there's a critical gap in integrating these approaches with human operators' practical needs and domain expertise. Fleet operators often have valuable insights about usage patterns, environmental conditions, and operational constraints that could enhance system performance if properly incorporated.
Proposed Approach
The framework consists of three main components:
1. LLM-Based Interface Layer
- Natural language interface for fleet operators to input operational constraints, preferences, and domain knowledge
- Translation of human insights into quantifiable parameters for the multi-agent system
- Continuous learning from operator feedback and system performance
2. Multi-Agent Coordination Layer
- Distributed agents managing individual vehicles and charging stations
- Dynamic route optimization considering battery thermal conditions
- Real-time adjustment of charging schedules based on fleet-wide demands
- Inter-agent communication for resource allocation and load balancing
3. Predictive Battery Management Layer
- Integration of cloud-based predictive models for battery thermal management
- Real-time monitoring and optimization of battery health across the fleet
- Adaptive control strategies based on environmental conditions and usage patterns
Expected Outcomes
- Improved battery longevity through optimized thermal management
- Enhanced fleet efficiency through coordinated charging and routing
- Reduced operational costs and energy consumption
- Better integration of human expertise in automated fleet management
- Scalable solution for large urban EV fleets
Potential Applications
- Urban delivery fleets
- Public transportation systems
- Ride-sharing services
- Municipal vehicle fleets
- Commercial fleet operations
Proposed Methodology
Develop a three-layer architecture integrating LLM-based human interaction, multi-agent coordination, and predictive battery management, with continuous feedback loops between layers.
Potential Impact
Could significantly improve EV fleet efficiency, reduce operational costs, extend battery life, and create more sustainable urban transportation systems while making complex fleet management more accessible to human operators.