Fleet Charger Rerouting for EV Depot Failure Recovery
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Solution Overview
Problem
Electric vehicle fleet charging depots face significant challenges due to high charger failure rates, leading to lost charging time and the need to relocate vehicles, which can result in inefficiencies and safety risks from potential collisions when redirecting vehicles to available chargers.
Innovation Solution
An electric vehicle fleet management system that utilizes data analytics and machine learning to predict charger failures, optimally route vehicles to functional chargers through graph network modeling, and prioritize charging to minimize downtime and prevent collisions by determining and implementing the shortest paths while avoiding intersecting routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles are redirected to available chargers when chargers fail, then fleet charging continuity is maintained, but vehicle relocation time and potential collision risks increase
Solution Approach 1:
The system pre-calculates and stores shortest paths between all charger pairs before failures occur. When a charger fails, vehicles are immediately redirected along pre-computed paths without calculation delays, maintaining charging continuity while minimizing relocation time
Solution Approach 2:
The fleet management system acts as an intermediary that monitors charger status, predicts failures using machine learning, and coordinates vehicle redirection. This central coordination prevents collision risks while efficiently managing vehicle relocation to available chargers
2Productivity
If multiple vehicles are routed simultaneously to available chargers, then charging efficiency is improved, but collision risks increase due to intersecting paths
Solution Approach 1:
The system continuously monitors vehicle positions and charger availability, dynamically adjusting routing decisions. When detecting potential path intersections, the system provides feedback to modify routes, ensuring multiple vehicles can be routed simultaneously without collision risks while maintaining charging efficiency
Solution Approach 2:
The routing system is dynamic and adaptive, continuously optimizing vehicle paths based on real-time conditions. The system can adjust routes on-the-fly to avoid intersections while still achieving high charging efficiency through parallel vehicle movements
3Reliability
If charger failure prediction is implemented using machine learning, then preventive vehicle relocation is enabled, but system complexity increases
Solution Approach 1:
The fleet management system performs self-diagnosis and predictive maintenance by using machine learning algorithms to analyze charger performance data and predict failures. This self-service capability enables preventive vehicle relocation without requiring external monitoring systems, maintaining high reliability while managing complexity through integrated analytics
Data Source
AI summary
Systems and methods for a computer-based process that optimizes vehicle fleet charging systems. Failed chargers are detected and optimal paths are determined between these failed chargers and available chargers. Upon determining the optimal path, instructions to route the vehicle from the failed chargers to available chargers via these shortest paths are then communicated.