SEAL-EV: Self-Calibrating LLM Agents for Predictive Electric Vehicle Fleet Optimization
Abstract
A novel framework combining steerable LLM reasoning with predictive battery management for electric vehicle fleets. The system uses multi-agent coordination to optimize charging schedules, route planning, and battery thermal management while adapting to real-world conditions through self-calibrating reasoning paths.
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Research Gap Analysis
Current approaches lack integration between LLM-based decision-making and domain-specific EV fleet optimization, particularly in combining thermal management, charging optimization, and adaptive reasoning.
SEAL-EV: Self-Calibrating LLM Agents for Predictive Electric Vehicle Fleet Optimization
Motivation
Electric vehicle (EV) fleet management faces complex challenges in optimizing battery longevity, charging schedules, and thermal management across diverse operating conditions. While existing solutions address these challenges separately, there's a critical need for an integrated approach that can dynamically adapt to real-world scenarios. Current LLM-based multi-agent systems show promise in complex decision-making but often suffer from inefficient reasoning paths and lack domain-specific optimization.
Proposed Approach
SEAL-EV introduces a novel framework that combines steerable LLM reasoning calibration with predictive battery management:
1. Multi-Agent Architecture
- Fleet Coordinator Agent: Oversees high-level fleet management and resource allocation
- Vehicle-Specific Agents: Monitor individual EV performance and requirements
- Charging Infrastructure Agent: Manages charging station availability and scheduling
- Thermal Management Agent: Optimizes battery temperature across the fleet
2. Steerable Reasoning Integration
- Adapt SEAL (Steerable reasoning calibration) for EV-specific decision-making
- Implement domain-specific steering vectors for battery management, routing, and charging
- Dynamic calibration of reasoning paths based on real-world performance metrics
3. Predictive Optimization
- Cloud-based predictive analytics for route planning and energy consumption
- Real-time thermal management optimization using historical and environmental data
- Adaptive charging schedules based on usage patterns and grid conditions
Expected Outcomes
- Improved battery longevity through optimized thermal management
- Reduced operational costs via efficient charging and route optimization
- Enhanced fleet reliability through predictive maintenance
- More efficient decision-making with calibrated LLM reasoning
Potential Applications
- Commercial EV fleet operations
- Public transportation systems
- Autonomous delivery services
- Ride-sharing platforms
- Smart city infrastructure integration
The system's modular design allows for scalable deployment across different fleet sizes and use cases, while the self-calibrating nature ensures continuous improvement in decision-making efficiency.
Proposed Methodology
Develop a multi-agent system using steerable LLM reasoning calibration combined with predictive battery management, incorporating real-time data feedback for continuous optimization.
Potential Impact
Could significantly improve EV fleet efficiency, reduce operational costs, extend battery life, and provide a scalable framework for intelligent transportation systems. The approach could be adapted for other complex systems requiring multi-agent coordination.