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Intelligent Power Management for Unmanned Aerial Vehicles

Intelligent Power Management for Unmanned Aerial Vehicles. James Graham Roger Dixon, Pete Hubbard Loughborough University. Introduction. Predict the thermal state of generator Use Predictions to make informed load management decisions. Method. Thermal Network Model. Kalman Filter.

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Intelligent Power Management for Unmanned Aerial Vehicles

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  1. Intelligent Power Management for Unmanned Aerial Vehicles James Graham Roger Dixon, Pete Hubbard Loughborough University UKACC PhD Presentation Showcase

  2. Introduction • Predict the thermal state of generator • Use Predictions to make informed load management decisions UKACC PhD Presentation Showcase

  3. Method Thermal Network Model Kalman Filter UKACC PhD Presentation Showcase

  4. Thermal Network Model UKACC PhD Presentation Showcase

  5. Results Time (hours) 2 1 O 3 4 5 • Fault occurs at 67 mins • System changes load of both Gens to • prevent overheat of faulty one UKACC PhD Presentation Showcase

  6. Conclusion or summary or future work • Optimise load profiles to prevent overheat • Generator resting • Load displacement to spare generators UKACC PhD Presentation Showcase

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