UAV Fleet Charging Profiles for Battery Life and Peak Delivery Demand
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Solution Overview
Problem
Existing UAV systems face challenges in efficiently balancing the need for rapid delivery services with the desire to preserve battery life, as high charge rates can negatively affect battery lifetime, leading to increased operational costs and downtime.
Innovation Solution
A central control system adjusts charge rates for a fleet of UAVs based on demand data, battery state information, and infrastructure availability, instructing slower charging during low demand periods and faster charging during peak times to optimize battery life and meet service demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high charge rates are used to meet rapid delivery service demands, then productivity is improved, but battery lifetime deteriorates
Solution Approach 1:
The charging system dynamically adjusts charge rates based on real-time demand data and battery state information. During low-demand periods, slower charging rates are applied to preserve battery life, while during peak demand periods, faster charging rates are used to ensure service availability. This dynamic adjustment resolves the contradiction by making the charge rate adaptable rather than fixed.
Solution Approach 2:
The system changes the charging parameter (charge rate) based on operational conditions and battery state. By monitoring battery state information and demand data, the system modifies the charge rate parameter to optimize both battery lifetime and delivery service productivity, avoiding consistently high charge rates that would degrade the battery while ensuring sufficient charging during critical periods.
2Duration of action of stationary object
If slow charging is used to preserve battery life, then battery lifetime is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary charging actions during low-demand periods when slower charge rates can be applied without impacting service availability. By charging batteries in advance during off-peak times, the system ensures that batteries are sufficiently charged before peak demand periods, thus preserving battery life while maintaining productivity during critical service windows.
Solution Approach 2:
The charging system employs periodic action by alternating between slow charging during low-demand periods and fast charging during high-demand periods. This periodic pattern allows the system to preserve battery life through extended slow charging phases while ensuring adequate service availability during operational peaks, effectively resolving the contradiction between battery longevity and service productivity.
3Productivity
If charging to 100% SOC is used to maximize service capacity, then productivity is improved, but battery lifetime deteriorates
Solution Approach 1:
The system changes the target state of charge parameter based on operational needs and battery state. Instead of consistently charging to 100% SOC, the system adjusts the target SOC level dynamically, charging to lower levels when sufficient for upcoming demand and preserving battery lifetime by avoiding repeated full charge cycles, while still ensuring adequate service capacity is maintained.
4Productivity
If fast charging is used to meet peak demand, then productivity is improved, but battery lifetime deteriorates
Solution Approach 1:
The system performs preliminary charging during low-demand periods so that batteries are adequately charged before peak demand arrives. This eliminates the need for aggressive fast charging during peak periods, as batteries have been pre-charged to sufficient levels, thus maintaining peak service capacity while preserving battery lifetime by avoiding repeated high-rate charging stress.
Data Source
Figure 1A
Figure 1B~1C
Figure 1D
AI summary
Example embodiments can help to more efficiently charge unmanned aerial vehicles (UAVs) in a plurality of UAVs that provide delivery services. An example method includes: determining demand data indicating demand for item-transport services by the plurality of UAVs during a period of time; determining battery state information for the plurality of UAVs, wherein the battery state information is based at least in part on individual battery state information for each of two or more of the UAVs; based at least in part on (a) the demand data for item-transport services by the plurality of UAVs, and (b) the battery state information for the fleet of UAVs, determining respective charge-rate profiles for one or more of the UAVs; and sending instructions to cause respective batteries of the one or more of the UAVs to be charged according to the respectively determined charge-rate profiles.