Autonomous EV Dispatch Charging by Time Slot and Ride Demand
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Solution Overview
Problem
Existing vehicle dispatching systems face challenges in maintaining high operation rates and preventing battery shortages in autonomous electric vehicles due to the trade-off between charging time and battery level, which affects travel distance and efficiency.
Innovation Solution
A vehicle dispatching system that adjusts the upper limit charging level of autonomous vehicles' batteries based on time slots and user demand, prioritizing vehicles with appropriate charging levels for specific ride distances, and employing a management server to direct normal or quick charging as needed to optimize vehicle availability and battery health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the charging level is raised to extend travel distance, then the battery capacity increases, but the charging time becomes longer and operation rate decreases
Solution Approach 1:
The patent implements dynamic charging level adjustment based on time slots and predicted user demand. The charging planning unit changes the upper limit charging level according to different time periods, making the charging strategy adaptive rather than static. This resolves the contradiction by allowing high charging levels during low-demand periods (extending travel distance) while maintaining faster charging or lower levels during high-demand periods (preserving operation rate).
Solution Approach 2:
The system performs preliminary charging actions during off-peak time slots when demand is low. By charging vehicles to higher levels in advance during periods when operation rate impact is minimal, the system prepares vehicles for future long-distance trips without sacrificing current operation rates. This anticipatory charging strategy resolves the timing conflict between charging duration and vehicle availability.
2Productivity
If the charging time is shortened to increase operation rate, then the vehicle availability improves, but the charging level becomes lower and travel distance decreases
Solution Approach 1:
The system dynamically adjusts charging levels based on time slots and predicted demand patterns. During time slots with low predicted demand, the system allows longer charging times to achieve higher charging levels, thereby extending travel distance without significantly impacting overall operation rate. This dynamic approach resolves the contradiction by making charging duration flexible rather than fixed.
Solution Approach 2:
The charging planning unit changes the upper limit charging level parameter according to different time slots. By adjusting this key parameter, the system can optimize the balance between charging time and travel distance for each time period, resolving the contradiction through parameter optimization rather than fixed charging policies.
3Adaptability or versatility
If the upper limit charging level is increased for long-distance trips, then the travel capability improves, but the charging time increases and battery deterioration accelerates
Solution Approach 1:
The system performs preliminary charging to high levels during off-peak periods when vehicles are not immediately needed. This advance preparation ensures travel capability is restored without impacting current operation rates, as the charging occurs during low-demand time slots when vehicle availability pressure is lower.
Solution Approach 2:
The system dynamically determines charging levels based on time slots and predicted demand. Rather than always charging to maximum level, the system adjusts the upper limit charging level parameter to match actual needs, reducing unnecessary high-level charging that would accelerate battery deterioration while still maintaining adequate travel capability when needed.
4Device complexity
If simple charging strategies are used, then the system complexity is reduced, but the operation rate cannot be raised and battery shortages occur
Solution Approach 1:
The charging planning unit automatically determines optimal charging levels and strategies based on time slot information and predicted user demand patterns. The system serves itself by making autonomous charging decisions without requiring complex manual intervention or highly complex infrastructure, resolving the contradiction through automated intelligent control rather than simplified passive charging.
Solution Approach 2:
The system uses predicted user demand information as feedback to adjust charging strategies. By incorporating demand prediction into the charging planning process, the system can proactively optimize charging levels to prevent battery shortages while maintaining high operation rates, achieving improved productivity through feedback-driven adaptive control.
Data Source
AI summary
The vehicle dispatching system accepts a dispatch request from a user, selects an autonomous vehicle matching with the dispatch request from among a plurality of autonomous vehicles, and dispatches a selected autonomous vehicle to the user. The plurality of autonomous vehicles include a plurality of battery-mounted vehicles having an in-vehicle battery capable of being charged externally as an energy source. Each of the plurality of battery-mounted vehicles performs charging at a charging station when a charging level of the in-vehicle battery decreases. The vehicle dispatching system comprises a management server including a processor for executing programs stored in memory, the management server programmed to act as a charging planning unit that changes an upper limit charging level of the in-vehicle battery when charging at the charging station according to a time slot.