Aircraft Battery Fleet Allocation Using Predictive Health Monitoring
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Solution Overview
Problem
Aircraft batteries are often replaced too soon or too late due to inefficient charging and maintenance practices, leading to reduced service life and increased costs, as existing systems lack a comprehensive method for predicting battery health and optimizing allocation and maintenance across multiple airports.
Innovation Solution
A system and method for maintaining a fleet of aircraft batteries that involves swapping individual batteries between charging stations and aircraft, using predictive analytics based on battery health, charge, flight schedule, and utility information to optimize allocation, maintenance, and leasing, with components like servers, client platforms, and charging containers for data collection and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batteries are replaced based on average failure rates, then fleet availability is maintained, but some batteries are replaced too soon and others too late, reducing overall service life
Solution Approach 1:
The system performs preliminary assessment of battery health status and predicts future failures before they occur. By monitoring multiple parameters (temperature, voltage, current, impedance) and analyzing trends, the system identifies batteries that are likely to fail soon and schedules their replacement in advance, preventing unexpected failures while avoiding premature replacement of healthy batteries.
Solution Approach 2:
The system continuously monitors battery performance parameters and feeds this information back to update predictions. Real-time data from sensors during charging and operation is compared against historical data and failure patterns, allowing the system to adjust replacement schedules dynamically based on actual battery condition rather than static average failure rates.
2Productivity
If high speed charging is used to charge batteries as needed, then charging time is reduced, but batteries are degraded leading to shorter service life
Solution Approach 1:
The system dynamically adjusts charging parameters based on real-time battery condition assessment. Instead of using fixed high-speed charging for all batteries, the system modifies charging current and voltage profiles according to the specific battery's health status, temperature, and state of charge, optimizing the balance between charging speed and battery longevity.
Solution Approach 2:
The system changes charging parameters (current, voltage, temperature thresholds) based on battery condition. When batteries show signs of degradation or are in poor condition, the system automatically reduces charging intensity to minimize stress on the battery, thereby extending service life while still meeting operational requirements.
3Duration of action of moving object
If comprehensive monitoring and predictive analytics are implemented, then battery replacement timing is optimized, but system complexity increases
Solution Approach 1:
The system divides the fleet into segments or groups based on battery type, aircraft model, operational patterns, and risk profiles. This segmentation allows the complex predictive analytics to be applied more efficiently to smaller, homogeneous groups, reducing the overall computational complexity while maintaining accurate predictions for each segment.
Solution Approach 2:
The system introduces an intermediary layer (predictive analytics platform) that sits between the simple sensor monitoring and the complex replacement decision-making. This intermediary processes raw sensor data, applies predictive models, and outputs simplified recommendations, thereby managing complexity by breaking down the decision process into manageable stages.
Data Source
AI summary
A system for maintaining a fleet of aircraft batteries located at multiple airports. The system may be configured to obtain charge information and battery health information for individual batteries within the fleet of aircraft batteries from charging containers located at individuals ones of the multiple airports. The charge information may include charge statuses of the individual batteries and/or the battery health information may characterize the degradation of the individual batteries over time. The system may determine predictions of one or more batteries to be re-allocated and/or to receive maintenance based on the battery health information. The system may generate a battery management plan including the one or more predictions recommending one or more batteries to be re-allocated and/or to receive maintenance.