eVTOL Battery Model Calibration Using Closed-Loop Ground Load Testing
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Solution Overview
Problem
Electric vertical takeoff and landing (eVTOL) aircraft face challenges in accurately predicting battery range due to nonlinear degradation patterns of lithium-ion batteries, which are influenced by usage history, power demand, state of charge, and temperature, making it difficult to model worst-case cell performance and ensuring safety and usability.
Innovation Solution
A closed-loop calibration process using ground support equipment (GSE) that applies controlled load profiles to the batteries, continuously monitoring voltage, current, and temperature to update battery models, providing high-fidelity predictions and robust state-of-health tracking across the battery's lifetime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If battery models use nominal flight data for range predictions, then the modeling process is simple, but the accuracy of worst-case performance characterization is insufficient
Solution Approach 1:
The system performs preliminary calibration tests under controlled ground conditions before flight operations to establish baseline battery model parameters. This preliminary action captures degradation patterns under known conditions, which then inform and improve the accuracy of range predictions during actual flight operations without requiring complex real-time modeling
Solution Approach 2:
The system implements a closed-loop calibration process where battery performance data from flight operations and ground tests is continuously fed back to update and refine the battery model. This feedback mechanism allows the model to progressively capture nonlinear degradation patterns and corner case conditions, improving worst-case performance characterization while maintaining operational simplicity
2Measurement precision
If battery models capture complex nonlinear degradation patterns, then prediction accuracy improves, but model complexity increases
Solution Approach 1:
The battery model is segmented into multiple components: an electrochemical model for fundamental degradation mechanisms, an equivalent circuit model for electrical behavior, and empirical correction factors for nonlinear effects. This segmentation allows each component to handle specific aspects of degradation, improving overall prediction accuracy while keeping individual model components manageable and interpretable
Solution Approach 2:
The system dynamically adjusts model parameters based on operating conditions such as temperature, state of charge, and power demand. By changing parameters adaptively rather than using fixed values, the model captures nonlinear degradation patterns more accurately without requiring a fundamentally more complex model structure
3Reliability
If calibration tests are performed frequently to maintain model accuracy, then prediction reliability improves, but time and operational availability decrease
Solution Approach 1:
The system implements periodic calibration testing at scheduled intervals rather than continuous testing. Between calibration events, the battery model operates in a predictive mode using the last calibrated parameters. This periodic approach maintains model accuracy over time while minimizing the loss of operational availability, as the aircraft is grounded for calibration only during scheduled maintenance periods
Data Source
AI summary
Example methods calibrate battery models in electric aircraft using closed-loop feedback. Test points gaps are received. The aircraft connects to ground support equipment (GSE) and is immobilized. Certain battery packs are selected for testing. Propellers are ramped to create power draws per a test plan while monitoring voltage, current, and temperature telemetry. Selective battery packs are activated to focus loads. Telemetry data is processed and stored. Rest periods allow battery temperature stabilization between test points. Additional packs are tested if needed. Battery models are updated by analyzing telemetry data. Overall, the method maintains customized, accurate battery models using lab testing and in-situ calibration with the GSE. Frequent and ongoing recalibration via closed-loop feedback replaces assumptions with observed data for high-fidelity state-of-health predictions throughout the battery lifetime.