Flight Path Energy Prediction for Aerial Vehicle Rerouting

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Solution Overview

Problem

Aerial vehicles face challenges in accurately predicting and managing energy consumption along flight paths, leading to potential energy shortfalls during flight, which can impact mission success.

Innovation Solution

Utilizing machine learning models to predict power and energy consumption based on flight path attributes, and implementing a contingency system to adjust operations when energy allocation is at risk, including rerouting or reducing flight path length to avoid energy shortfall.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the flight path is extended to increase mission capability, then the productivity is improved, but the energy consumption increases leading to potential energy shortfalls

Engineering Contradiction:

Improvemission capabilityVSAvoidenergy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary energy consumption predictions using machine learning models before the aerial vehicle executes the flight path. This allows the system to pre-calculate energy requirements for different flight path segments and identify potential energy shortfalls before they occur, enabling proactive flight path adjustments rather than reactive measures during flight

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the flight path based on real-time energy consumption monitoring and predictions. When the model predicts that energy reserves will be insufficient to complete the mission, the system automatically modifies the flight path to reduce energy consumption while still achieving mission objectives, creating a dynamic adaptation between mission requirements and energy availability

Inventive Principle:

Principle #15Dynamics

2Use of energy by moving object

If the flight path is modified to reduce energy consumption, then the energy efficiency is improved, but the mission capability may be compromised

Engineering Contradiction:

Improveenergy efficiencyVSAvoidmission capability

Core Design Contradiction:

Use of energy by moving objectVSProductivity

Solution Approach 1:

The system implements continuous feedback loops where actual energy consumption measurements are compared against predicted values. This feedback informs subsequent flight path decisions, allowing the system to learn from actual performance and make increasingly accurate adjustments that balance energy efficiency with mission capability rather than simply reducing energy use

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes flight path parameters such as altitude, speed, and route selection to optimize the balance between energy consumption and mission capability. By adjusting these parameters dynamically based on energy predictions and actual consumption, the system can maintain mission effectiveness while improving energy efficiency without simply shortening the flight path

Inventive Principle:

Principle #35Parameter changes

3Reliability

If real-time energy monitoring is implemented to detect energy shortfalls, then the reliability is improved, but the system complexity increases

Engineering Contradiction:

Improveenergy management reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system uses the aerial vehicle's existing sensors and onboard computer to perform energy consumption monitoring and predictions. Rather than adding dedicated complex hardware, the system leverages existing vehicle components to self-monitor energy usage, reducing the need for additional complex systems while maintaining high reliability through intelligent software-based solutions

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260056551A1Generating Power and Energy Predictions for Flight Paths

Publication Date: 2026.02.26 WING AVIATION LLC

AI summary

A method includes determining a portion of a flight path of an aerial vehicle. The method also includes determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path. The method additionally includes determining, based on the attribute value and using a non-linear model, a power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method further includes determining, based on the power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method yet further includes determining the flight path based on the energy value.