AI Fleet Allocation Across Mobility and Grid Services
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Solution Overview
Problem
Current navigation systems fail to optimize routes for autonomous electric vehicles based on real-time emissions data and dynamic road pricing, neglecting environmental impacts, and they do not integrate transportation and electricity grid management, missing opportunities for synergistic optimization and vehicle-to-grid capabilities.
Innovation Solution
An AI-based system dynamically allocates autonomous electric vehicles between mobility and electricity services using real-time data, multi-objective genetic algorithms, and hierarchical optimization to balance revenue, energy costs, emissions reduction, and battery health, incorporating vehicle-specific information and dynamic pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If navigation systems optimize routes based on limited criteria like distance and time, then routing efficiency is improved, but environmental impacts are neglected
Solution Approach 1:
The system changes the optimization parameters from traditional distance and time-only criteria to a multi-parameter framework that includes real-time emissions data, dynamic road pricing, and environmental zones. This allows routes to be optimized simultaneously for efficiency and environmental impact by adjusting the weight and consideration of each parameter in the routing algorithm.
Solution Approach 2:
The navigation system is enhanced to perform multiple functions: traditional route optimization, real-time emissions monitoring, dynamic pricing calculation, and environmental compliance checking. This multi-functional approach allows a single routing decision to simultaneously address efficiency, environmental protection, and economic factors.
2Device complexity
If transportation and electricity grid systems operate in isolation, then system simplicity is maintained, but synergistic optimization opportunities are missed
Solution Approach 1:
The system merges the transportation management system with the electricity grid management system into an integrated platform. This combination allows electric vehicles to simultaneously serve transportation purposes and act as mobile energy storage units, enabling vehicle-to-grid services where vehicles can discharge energy back to the grid during peak demand periods.
Solution Approach 2:
Electric vehicles are assigned dual functionality: they operate as transportation assets for mobility services and as energy assets for grid flexibility services. The system dynamically allocates vehicles between these two roles based on real-time conditions, maximizing the value of each vehicle across both domains.
3Productivity
If fleet management systems focus solely on optimizing routes and schedules, then transportation efficiency is improved, but electricity grid services are neglected
Solution Approach 1:
The fleet management system is transformed into a dual-purpose platform that optimizes both transportation operations and electricity grid services. The same fleet of electric vehicles is managed for both mobility demand-side flexibility and electricity demand-side flexibility, allowing dynamic allocation between transportation tasks and grid services based on real-time opportunities and conditions.
Data Source
AI summary
A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.