Download as PDF, PPTX
Uploaded bySirris
2015 12-02-opti wind-test-setup-validation-estimation-techniques-drivetrains-ku-leuven
The document discusses a project aimed at improving mechatronic drivetrains through model-based virtual sensing. It details the approach, numerical and experimental validation, and the setup used for testing, including the use of an unscented Kalman filter for state and input estimation. The conclusions highlight the effectiveness of combining multiphysical models and measurements for dynamic load torque estimation.
2015 12-02-opti wind-test-setup-validation-estimation-techniques-drivetrains-ku-leuven
- 1. Test setup for virtualsensing on mechatronic drivetrains SBO-OptiWind open project meeting 2-12-2015 Bart Forrier bart.forrier@kuleuven.be
- 2. Overview 1. Goal 2. Approach 3.Numerical validation 4. Experimental validation – test setup 5. Conclusions
- 3. Goal: improve mechatronicdrivetrains Energies 2014, 7(4), 2595-2630; doi:10.3390/en7042595 AC machine nonlinear transmission Load multi-physical, non-linear dynamic input Tload
- 4. Approach: model-based virtualsensingPHYSICALVIRTUAL Sensor data Output System info Control input Disturbance
- 5. Mechatronic drivetrain • Multi-physical •Nonlinear • Motor measurements Unscented Kalman Filter • Nonlinear • Unknown load • State/input estimation Numerical validation 1D/3D 1D Reference model UKF model PHYSICALVIRTUAL Forrier, B., Naets, F., & Desmet, W. (2015). Virtual sensing on mechatronic drivetrains using multiphysical models. ECCOMAS Thematic Conference on Multibody Dynamics. Barcelona, Catalonia, Spain, 29 June - 2 July.
- 6. Numerical validation –results • Noise reduction • Input estimation Low SNR Measured High SNR Reference Estimate Reference Estimate Motor speed Load torque 1D vs 3D model mismatch Measured acceleration Nonlinearity Forrier, B., Naets, F., & Desmet, W. (2015). Virtual sensing on mechatronic drivetrains using multiphysical models. ECCOMAS Thematic Conference on Multibody Dynamics. Barcelona, Catalonia, Spain, 29 June - 2 July.
- 7. v, i (a,b,c) ϑ,ω,α ϑ,ω,α T Experimental setup •AC induction machines o 5,5 kW – FOC o 18 Nm / 3000 rpm o FOC • Nonlinear driveline o Cardan o variable deflection angle o easy replacement • Stiff frame
- 8. Validation of loadtorque estimation Model-based state observer Parameter identification Validation RTT Kalman-based estimation
- 9. Conclusion Use operational informationto improve mechatronic drivetrains Obtain information by model-based virtual sensing • Multiphysical 1D torsional model • Combined input/state estimation • Multiphysical measurements Validation • Numerical – 1D/3D reference o Dynamic load torque estimation • Experimental – test setup o Design, construction, instrumentation o Driveline dynamics & frame stiffness o Dynamic load torque estimation
- 10.