UAV Fleet Battery Charge Voltage Control for Capacity Alignment
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Solution Overview
Problem
Managing battery capacities in a fleet of unmanned aerial vehicles (UAVs) is challenging due to varying battery characteristics and usage patterns, leading to inconsistent performance and premature degradation, making it difficult to assign tasks effectively and extend battery lifetimes.
Innovation Solution
A method and system that determine a threshold capacity for each UAV battery, adjust the target charge voltage based on the battery's full charge capacity, and periodically compare it to maintain consistent performance, thereby aligning battery capacities and extending their lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If batteries are charged to full capacity, then flight operations can be extended, but battery degradation accelerates and lifespan decreases
Solution Approach 1:
The system dynamically adjusts the target charge voltage parameter based on battery age and capacity metrics. As batteries age and their full charge capacity decreases, the system modifies the target charge voltage to optimize the balance between flight duration and battery lifespan, preventing overcharging that would accelerate degradation
Solution Approach 2:
The charging system transitions from a static full-charge approach to a dynamic adjustment mechanism. The target charge voltage is not fixed but evolves over time based on battery health metrics, allowing the system to adapt charging parameters to current battery conditions and extend overall fleet operational capacity
2Reliability
If target charge voltage is increased to maintain capacity, then battery performance is improved, but degradation rate increases
Solution Approach 1:
The system implements a feedback loop that continuously monitors battery full charge capacity metrics and uses this information to adjust target charge voltage. This closed-loop control ensures that charging parameters are optimized based on actual battery performance and health data, maintaining reliability while minimizing harmful degradation effects
Solution Approach 2:
The battery management system autonomously adjusts charging parameters based on self-diagnosed battery health metrics. The system uses its own monitoring data to make intelligent decisions about optimal charge voltage, eliminating the need for external intervention while maintaining performance and reducing degradation
3Adaptability or versatility
If fleet batteries are managed individually, then specific battery characteristics are optimized, but fleet-wide consistency and task assignment efficiency decrease
Solution Approach 1:
The system applies a universal charging management framework that works across all fleet batteries regardless of individual characteristics. By using common metrics (full charge capacity, age) and a standardized adjustment algorithm, the system achieves both individual battery optimization and fleet-wide consistency, enabling efficient task assignment based on predictable performance levels
Data Source
AI summary
A method includes determining a threshold capacity associated with at least a first unmanned aerial vehicle (UAV) and a second UAV. The method includes initially setting a target charge voltage of a first battery of the first UAV to less than a full charge voltage to limit a state of charge of the first battery based on the threshold capacity. The method includes, over a lifetime of the first battery of the first UAV, periodically comparing a full charge capacity of the first battery to the threshold capacity. The method includes, based on the comparing, periodically adjusting the target charge voltage of the first battery, such that, as the full charge capacity of the first battery decreases with age, the target charge voltage increases towards the full charge voltage of the first battery.