EV Fleet Charging Control for Real-Time Grid Balancing
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Solution Overview
Problem
The increased penetration of renewable energy sources leads to electrical grid volatility due to their intermittent nature, necessitating systems to stabilize the grid and manage electric vehicle charging efficiently across multiple customers and fleets.
Innovation Solution
A computer-implemented method for scheduling electric vehicle charging that incorporates real-time updates, machine learning, and bidirectional charging efficiency to optimize charging strategies based on grid demand, vehicle arrival times, and power consumption patterns, allowing for flexible and adaptive charging schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electric vehicle charging is scheduled without real-time updates and fixed arrival times, then system flexibility and adaptability improve, but charging cost optimization and grid stability become more difficult to achieve
Solution Approach 1:
The charging schedule is transformed from a static fixed plan to a dynamic real-time adjustable plan. The system continuously monitors grid conditions, vehicle arrivals, and charging rates, then updates schedules dynamically to balance flexibility with grid stability requirements
Solution Approach 2:
The system implements real-time feedback loops that monitor charging rates, grid demand, and vehicle arrivals. This feedback enables the system to adjust charging schedules dynamically, maintaining grid stability while adapting to changing conditions and improving overall system reliability
2Productivity
If multiple charging depots are coordinated using traditional methods, then individual depot operation simplicity is maintained, but cross-depot scheduling efficiency and cost optimization deteriorate
Solution Approach 1:
Multiple charging depots are merged into a coordinated fleet management system that optimizes charging across all depots simultaneously. The system combines scheduling data, grid conditions, and vehicle information from multiple depots to achieve cross-depot efficiency while managing complexity through centralized intelligence
Solution Approach 2:
The scheduling system is designed to handle multiple functions: individual depot optimization, cross-depot coordination, grid stability management, and cost optimization. This universal system can adapt to different operational scales and requirements, managing complexity through modular design
3Loss of energy
If charging schedules are optimized considering only individual vehicle needs, then vehicle-specific charging requirements are met, but overall fleet charging cost and grid impact optimization are compromised
Solution Approach 1:
The system merges individual vehicle charging requirements with fleet-wide optimization goals. By combining vehicle-specific needs with overall fleet objectives, the system achieves cost optimization across the entire fleet while maintaining adaptability to individual vehicle requirements through integrated scheduling
Data Source
AI summary
The present disclosure provides methods, systems, and devices for controlling electric vehicle charging across multiple customers and multiple fleets of electric vehicles. These methods, systems, and devices may implement machine learning to determine distinct charging strategies for a plurality of charging depots. Scheduling methods systems, and devices disclosed herein do not require fixed electric vehicle arrival times but may instead update charging strategies in real time based on changes in a state of a system.