eVTOL Flight Controller Optimizing Battery Range via Machine Learning
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Solution Overview
Problem
Electric vertical takeoff and landing (eVTOL) aircraft face limitations in range and recharge time due to poor energy storage and long charging times, which affect their operational efficiency and sustainability.
Innovation Solution
A flight controller system is implemented on eVTOL aircraft to generate an efficient flight plan using machine-learning processes, optimizing battery usage and route planning based on real-time data such as battery status, weather, and recharging station locations, enabling autonomous flight mode and dynamic route adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If eVTOL aircraft use battery power for sustainable flight, then environmental sustainability is improved, but range of operation and energy storage capacity deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal flight paths and identifying charging stations along the route before the flight begins. The flight controller uses machine learning models to predict battery consumption patterns and plan recharging stops in advance, allowing the aircraft to operate efficiently within battery constraints while extending effective range through strategic pre-planning.
Solution Approach 2:
The system dynamically adjusts flight parameters, charging station selections, and route planning based on real-time battery status, weather conditions, and traffic patterns. The machine learning model continuously reoptimizes the flight plan during operation, adapting to changing conditions to maximize range and efficiency, transforming the static battery limitation into a dynamic optimization problem.
2Use of energy by moving object
If eVTOL aircraft use onboard batteries for power storage, then energy independence is improved, but recharge time increases
Solution Approach 1:
The system identifies and plans visits to charging stations along the flight path before the mission begins. By pre-calculating optimal charging stops based on battery capacity, flight duration, and station availability, the system minimizes total recharge time while maintaining energy independence through strategic use of external charging infrastructure.
Solution Approach 2:
The system introduces charging stations as intermediary points between origin and destination, allowing the aircraft to transfer energy without requiring large onboard battery capacity. This mediator approach enables extended operational duration by breaking the flight into segments with intermediate energy replenishment, reducing the time penalty of recharging.
3Productivity
If flight plans are generated using machine-learning processes with real-time data, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The flight controller performs self-service by autonomously generating and optimizing flight plans using onboard machine learning capabilities. The system processes real-time data from sensors, weather services, and traffic management independently, making autonomous decisions about routing, charging stops, and energy management without requiring complex external control infrastructure.
Solution Approach 2:
The system replaces traditional rule-based flight planning mechanics with machine learning algorithms that adaptively optimize routes based on patterns learned from historical data and real-time conditions. This substitution enables more efficient operational decisions while consolidating complexity into software rather than mechanical or procedural systems.
Data Source
AI summary
Aspects relate to a system for flight plan generation of an electric vertical takeoff and landing (eVTOL) aircraft. An exemplary system for flight plan generation includes a flight controller mounted on an eVTOL aircraft. The flight controller may be configured to receive a plurality of flight plan data and generate a flight plan for the aircraft as a function of the plurality of flight plan data.