Electric Flight Dispatch Module Optimizing Battery Turnaround
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Solution Overview
Problem
The commercial deployment of electric aircraft is hindered by limitations in battery management, particularly charging time and battery life, making the dispatch of electric aircraft more challenging for airline companies.
Innovation Solution
A system and method utilizing a machine learning algorithm integrated into an electric flight dispatch module, which processes training data and real-time airport infrastructure, airline, and battery management data to generate dispatch output, including a graphical user interface for ground crew, optimizing the management of electric aircraft fleets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electric aircraft are deployed commercially, then environmental sustainability and operational costs are improved, but battery management limitations (charging time and battery life) worsen dispatch efficiency
Solution Approach 1:
The system performs preliminary actions by predicting battery degradation trends and charging requirements before flights occur. The machine learning model forecasts future battery states and schedules charging operations in advance, allowing the aircraft to be ready for dispatch without waiting for actual charging completion. This proactive approach reduces the impact of charging time on dispatch efficiency.
Solution Approach 2:
The system enables self-service through autonomous decision-making for battery management. The machine learning algorithm automatically determines optimal charging schedules, battery replacement timing, and fleet allocation without requiring manual intervention. This automation reduces the time loss associated with human decision-making and streamlines the dispatch process despite battery management constraints.
2Reliability
If more battery management monitoring is implemented, then battery life and safety are improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it predicts battery degradation, forecasts charging requirements, optimizes fleet allocation, and generates maintenance schedules. This multi-functionality allows comprehensive battery management monitoring without proportionally increasing system complexity, as a single integrated model handles diverse battery safety and operational tasks.
Solution Approach 2:
The machine learning algorithm acts as an intermediary layer between raw battery data and operational decisions. Instead of directly managing complex battery parameters, the model processes sensor data and translates it into actionable insights for dispatch scheduling. This intermediary approach simplifies the overall system architecture by centralizing complex data processing in a dedicated predictive layer.
Data Source
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AI summary
A method for e-AC flight dispatch is disclosed, in accordance with one or more embodiments of the present disclosure. The method may include receiving a set of training data. The method may include training a machine learning algorithm of an electric flight dispatch module (112) based on the received set of training data. The method may include receiving one or more sets of real-time data, the one or more sets of real-time data including at least one of airport infrastructure data (300), airline information data (302), or battery management data (304). The method may include generating a set of output data (113) for dispatching an electric aircraft using the trained machine learning algorithm of the electric flight dispatch module.