Vehicle Energy Visualization for Predictive Flight Monitoring
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Solution Overview
Problem
Operators of autonomous and semi-autonomous electric aerial vehicles face challenges in managing energy efficiently, particularly in densely populated urban environments, where safe stopping points are scarce, and existing systems fail to provide timely and accurate decision-making for energy management, leading to potential battery depletion and safety risks.
Innovation Solution
A vehicle energy monitoring (VEM) platform integrates an onboard system with a remote vehicle operation hub to predict energy expenditure, display real-time visualizations, and provide recommendations for mitigating adverse situations, using a vehicle performance prediction model to correlate trip plans with battery state of function (SoF) and update displays dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If operators monitor all raw data from sensors and systems, then situational awareness is improved, but operator workload and decision-making complexity increase
Solution Approach 1:
The patent introduces an energy visualization system as an intermediary between raw battery data and the operator. The system processes raw battery state data through a vehicle performance prediction model and presents it as intuitive visualizations (progress bars, color-coded indicators, predicted range displays). This mediator translates complex raw data into actionable insights without requiring operators to analyze all underlying sensor data directly.
Solution Approach 2:
The system creates simplified copies or representations of the actual battery state through visual metaphors. Instead of showing raw voltage, current, and capacity data, the system generates visual copies such as progress bars representing charge levels, color-coded warnings for critical states, and predicted range estimates. These visual copies convey essential information in an easily digestible format.
2Measurement precision
If the system provides detailed energy data and predictions, then decision-making accuracy is improved, but the time required to process and display information increases
Solution Approach 1:
The system performs preliminary calculations and predictions before the operator needs the information. The vehicle performance prediction model continuously computes energy expenditure predictions, predicted range, and potential adverse situations in advance. When an operator queries the system, the results are already prepared and displayed immediately, rather than requiring real-time computation during critical decision moments.
Solution Approach 2:
The system implements continuous feedback loops where predicted energy expenditure is monitored against actual consumption. The vehicle performance prediction model updates predictions based on actual vehicle operation data, creating a self-correcting system that improves accuracy over time without requiring additional processing time from the operator. The feedback mechanism automatically adjusts predictions based on deviations between predicted and actual energy usage.
Data Source
AI summary
Embodiments of the present disclosure are directed to a vehicle energy monitoring (VEM) platform configured to monitor one or more vehicles. An onboard VEM system associated with a vehicle is communicably coupled to a remote vehicle operation hub associated with the VEM platform and can monitor a current energy expenditure of the vehicle as the vehicle executes a trip plan. A vehicle performance prediction model is configured to determine a predicted energy expenditure of a vehicle. Embodiments are also configured to generate, based on output from the vehicle performance prediction model, a predicted energy visualization representing the predicted energy expenditure, where the predicted energy visualization corresponds to a defined leg between a plurality of flight phases associated with the trip plan, and where the predicted energy visualization is displayed on a situation interface in relation to the defined leg between the plurality of flight phases.