Fleet EV Charging Path Planning for Time, Wear, and Cost
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Solution Overview
Problem
Current fleet management systems for electrified vehicles lack an efficient multi-objective charge planning strategy that balances delivery time, vehicle component wear, and charging costs, leading to suboptimal charging decisions.
Innovation Solution
A fleet management system with a control module that generates optimized charging path control strategies based on multi-objective charging cost goals, incorporating costs associated with delivery time, vehicle component wear, and charging, using information from charging stations, vehicles, and drivers, to determine when, where, and how long to charge each vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If charging is performed frequently to maintain battery charge levels, then vehicle availability for deliveries is improved, but charging costs and time losses increase
Solution Approach 1:
The system performs preliminary charging actions by identifying optimal charging opportunities before they are critically needed. The charge planning module proactively schedules charging during off-peak hours or during delivery routes, rather than waiting until battery levels become critical. This preliminary action ensures vehicle availability while minimizing disruption to delivery schedules.
Solution Approach 2:
The charging strategy is dynamically adjusted based on real-time conditions including battery state of charge, delivery priorities, charging station availability, and electricity pricing. The system continuously reoptimizes charging paths and timing, allowing flexible adaptation between maintaining charge levels and pursuing deliveries based on current operational context.
2Productivity
If fast charging is used to reduce charging time, then vehicle availability is improved, but vehicle component wear increases
Solution Approach 1:
The system applies different charging qualities to different situations by selecting between fast charging and standard charging based on specific needs. Fast charging is reserved for critical situations where vehicle availability is paramount, while standard charging is used during routine maintenance periods when time is less constrained. This localized quality approach minimizes overall component wear while maintaining operational requirements.
Solution Approach 2:
The charging rate parameter is dynamically changed based on battery state of charge, temperature conditions, and operational priorities. The system adjusts charging power levels to optimize between speed and battery health, reducing fast charging when battery levels are already adequate or when environmental conditions suggest slower charging would be beneficial for component longevity.
3Loss of time
If charging during off-peak hours is performed to reduce charging costs, then charging costs decrease, but delivery time may be affected
Solution Approach 1:
The system implements periodic charging actions by scheduling charging during off-peak electricity hours when costs are lower. The charge planning module identifies these periodic opportunities and integrates them into delivery routes, charging vehicles during economically optimal times while ensuring sufficient charge levels are maintained for subsequent deliveries during peak periods.
Solution Approach 2:
The system uses an intermediary optimization approach by introducing a charge planning module that mediates between delivery requirements and charging cost optimization. This intermediary layer analyzes multiple factors including delivery priorities, battery state, charging station availability, and electricity pricing to find balanced solutions that achieve cost reduction without significantly impacting delivery performance.
Data Source
AI summary
Systems and methods are disclosed for providing optimized, multi-objective charge planning strategies for electrified vehicles of a vehicle fleet. The proposed systems and methods may utilize a multi-objective approach to charge planning. The multi-objective approach may account for factors such as time, wear, and cost to charge by assigning a cost value to each factor. The proposed systems and methods may further leverage charging at fleet owned/managed depots, public charging stations, and private, residential charging locations when solving the charging path optimization problem for each vehicle of the fleet.