Kalman-Filter Design and Evaluation for PMSM Rotor-Temperature Estimation
(2022)Department of Automatic Control
- Abstract
- The permanent magnet synchronous motor, PMSM, is an efficient electrical motor that has seen a greater prevalence in the automotive industry from the increasing demand for electrical vehicles. Managing the temperature of the permanent magnet rotor is important to optimize motor utilization and avoid hardware failures. Direct temperature measurements of the moving rotor with a sensor are, however, both difficult and costly and an observer-based approach to estimate the rotor temperature can instead be attained using measured currents, voltages and a model.
The work in this thesis studies observers based on a Kalman Filter, KF, and an extended Kalman filter, EKF. The observability of the system was found to be poor at low rotational speeds... (More) - The permanent magnet synchronous motor, PMSM, is an efficient electrical motor that has seen a greater prevalence in the automotive industry from the increasing demand for electrical vehicles. Managing the temperature of the permanent magnet rotor is important to optimize motor utilization and avoid hardware failures. Direct temperature measurements of the moving rotor with a sensor are, however, both difficult and costly and an observer-based approach to estimate the rotor temperature can instead be attained using measured currents, voltages and a model.
The work in this thesis studies observers based on a Kalman Filter, KF, and an extended Kalman filter, EKF. The observability of the system was found to be poor at low rotational speeds and filter designs were implemented that used low-speed estimators, that slowly drive the rotor temperature estimate towards the coolant temperature. For circumstances when the inductance accuracy in the model was limited, EKFs with inductance estimation and gain scheduled noise covariance matrices were also evaluated. There were also potential numerical robustness issues, so normalized and rescaled state variable system descriptions were evaluated.
The simulation analysis of the KFs showed a great reduction in rotor temperature estimation error of roughly 40°C when using a low-speed estimator, compared to without the use of a low-speed estimator. In circumstances with limited inductance accuracy, the EKF with inductance estimation had a maximum temperature estimation error magnitude of ≈2.5°C compared to ≈11°C of the KF design. Using an EKF with lower sampling frequency and gain scheduling did, however, come at the cost of robustness. Normalizing or rescaling state variables had a visible effect on the noise covariance settings but did not show noticeable improvements of the computational robustness in a simulation environment with high numerical precision.
The thesis was concluded with a brief analysis using measurement from a real but different motor model. The worst case estimation error magnitudes was approximately 12°C for the rotor temperature. The estimation results were very sensitive to model parameter accuracy and more testing has to be conducted using experimental data, but the results presented show some promise. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9097063
- author
- Mårtensson, Daniel
- supervisor
- organization
- year
- 2022
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6156
- ISSN
- 0280-5316
- language
- English
- id
- 9097063
- date added to LUP
- 2022-08-12 09:45:39
- date last changed
- 2022-09-01 15:17:12
@misc{9097063,
abstract = {{The permanent magnet synchronous motor, PMSM, is an efficient electrical motor that has seen a greater prevalence in the automotive industry from the increasing demand for electrical vehicles. Managing the temperature of the permanent magnet rotor is important to optimize motor utilization and avoid hardware failures. Direct temperature measurements of the moving rotor with a sensor are, however, both difficult and costly and an observer-based approach to estimate the rotor temperature can instead be attained using measured currents, voltages and a model.
The work in this thesis studies observers based on a Kalman Filter, KF, and an extended Kalman filter, EKF. The observability of the system was found to be poor at low rotational speeds and filter designs were implemented that used low-speed estimators, that slowly drive the rotor temperature estimate towards the coolant temperature. For circumstances when the inductance accuracy in the model was limited, EKFs with inductance estimation and gain scheduled noise covariance matrices were also evaluated. There were also potential numerical robustness issues, so normalized and rescaled state variable system descriptions were evaluated.
The simulation analysis of the KFs showed a great reduction in rotor temperature estimation error of roughly 40°C when using a low-speed estimator, compared to without the use of a low-speed estimator. In circumstances with limited inductance accuracy, the EKF with inductance estimation had a maximum temperature estimation error magnitude of ≈2.5°C compared to ≈11°C of the KF design. Using an EKF with lower sampling frequency and gain scheduling did, however, come at the cost of robustness. Normalizing or rescaling state variables had a visible effect on the noise covariance settings but did not show noticeable improvements of the computational robustness in a simulation environment with high numerical precision.
The thesis was concluded with a brief analysis using measurement from a real but different motor model. The worst case estimation error magnitudes was approximately 12°C for the rotor temperature. The estimation results were very sensitive to model parameter accuracy and more testing has to be conducted using experimental data, but the results presented show some promise.}},
author = {{Mårtensson, Daniel}},
issn = {{0280-5316}},
language = {{eng}},
note = {{Student Paper}},
title = {{Kalman-Filter Design and Evaluation for PMSM Rotor-Temperature Estimation}},
year = {{2022}},
}