EQUIPMENT AND METHOD FOR ESTIMATING A MOTOR PARAMETER

Abstract

A method and equipment (apparatus) for estimating motor parameters includes: receiving an operating parameter of an electric motor, estimating an estimated first motor parameter based on the operating parameter and on an initially determined second motor parameter and estimating an estimated second motor parameter based on the operating parameter and on an initially determined first motor parameter. The equipment (apparatus) and the method further include estimating a revised estimated second motor parameter based on the estimated first motor parameter and on the operating parameter, and estimating a revised estimated first motor parameter based on the estimated second motor parameter and on the operating parameter.

Claims

1-13. (canceled)

14. An apparatus for estimating a motor parameter, comprising: an input interface to receive an operating parameter of an electric motor, a first device to perform a first parameter estimation algorithm estimating an estimated first motor parameter based on the operating parameter and on an initially determined second motor parameter, a second device to perform a second parameter estimation algorithm estimating an estimated second motor parameter based on the operating parameter and on an initially determined first motor parameter, a third device to perform a third parameter estimation algorithm estimating a revised estimated second motor parameter based on the estimated first motor parameter and on the operating parameter, and a fourth device to perform a fourth parameter estimation algorithm estimating a revised estimated first motor parameter based on the estimated second motor parameter and on the operating parameter.

15. The apparatus of claim 14, wherein the operating parameter includes at least one of a detected voltage, a detected current, and a detected electrical speed, the first motor parameter includes a permanent magnet flux linkage, and the second motor parameter includes a phase resistance.

16. The apparatus of claim 15, wherein the first device and the fourth device include an artificial neuronal network (ANN) model performing the first and fourth parameter estimation algorithm, and wherein the second device and the third device include a model reference adaptive control (MRAC) model performing the second and third parameter estimation algorithm.

17. The apparatus of claim 14, wherein the initially determined first motor parameter and the initially determined second motor parameter are constant values.

18. The apparatus of claim 14, wherein the third device is configured to estimate a third motor parameter.

19. The apparatus of claim 18, wherein the third motor parameter includes an inductance.

20. A method for estimating motor parameters, the method comprising: estimating an estimated first motor parameter based on an operating parameter and on an initially determined second motor parameter; estimating an estimated second motor parameter based on the operating parameter and on an initially determined first motor parameter; estimating a revised estimated second motor parameter based on the estimated first motor parameter and on the operating parameter; and estimating a revised estimated first motor parameter based on the estimated second motor parameter and on the operating parameter.

21. The method of claim 20, wherein the operating parameter includes a detected voltage, a detected current and a detected electrical speed, wherein the first motor parameter includes a phase resistance, and wherein the second motor parameter includes a permanent magnet flux linkage.

22. The method of claim 21, wherein the estimate of the estimated first motor parameter and of the revised estimated first motor parameter are performed by means of an artificial neuronal network (ANN) model, and wherein the estimate of the estimated second motor parameter and of the revised estimated second motor parameter are performed by a model reference adaptive control (MRAC) model.

23. The method of claim 20, wherein the initially determined first motor parameter and the initially determined second motor parameter are set as constant values.

24. The method of claim 20, further comprising: estimating a third motor parameter.

25. The method of claim 24, wherein the third motor parameter includes an inductance.

26. A non-transitory computer readable medium having a computer program, which is executable by a processor, comprising: a program code arrangement having program code for estimating motor parameters, by performing the following: estimating an estimated first motor parameter based on an operating parameter and on an initially determined second motor parameter; estimating an estimated second motor parameter based on the operating parameter and on an initially determined first motor parameter; estimating a revised estimated second motor parameter based on the estimated first motor parameter and on the operating parameter; and estimating a revised estimated first motor parameter based on the estimated second motor parameter and on the operating parameter.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0034] FIG. 1 shows a block diagram of an equipment and of an algorithm according to the invention.

[0035] FIG. 2 shows diagrams demonstrating an efficacy of the equipment or method according to the invention.

DETAILED DESCRIPTION

[0036] FIG. 1 shows a block diagram of an equipment and of an algorithm according to the invention.

[0037] In the embodiment provided as an equipment, reference sign 1 depicts an equipment for estimating a motor parameter of an electric motor (not shown).

[0038] A first motor parameter comprises a permanent magnet flux linkage Ψ.sub.PM. A second motor parameter comprises a phase resistance R.sub.s. Alternatively, other motor parameters can be estimated.

[0039] The equipment 1 comprises a first estimator 2 and a second estimator 3. Furthermore, the equipment 1 comprises an input interface 4.

[0040] The input interface 4 receives operating parameters of the electric motor. The input interface 4 receives a detected voltage u.sub.d,q, a detected current i.sub.d,q, and a detected electrical speed We. In alternative embodiments, not all of these operating parameters or additional operating parameters are input.

[0041] The first estimator 2 comprises a first device 5 comprising an artificial neuronal network (ANN) model and a second device 6 comprising a model reference adaptive control (MRAC) model respectively performing a parameter estimation algorithm. Alternatively, another quantity of the devices, only one or more than two, or another kind of models, e.g. an Extended-Kalman-Filter (EKF), can be provided.

[0042] The first device 5 is configured to perform a first parameter estimation algorithm according to the artificial neuronal network (ANN) model estimating an estimated first motor parameter Ψ.sub.PM,1 based on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e. and on an initially determined second motor parameter R.sub.s,0.

[0043] The second device 6 is configured to perform a second parameter estimation algorithm according to the model reference adaptive control (MRAC) model estimating an estimated second motor parameter R.sub.s,1 based on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e and on an initially determined first motor parameter Ψ.sub.PM,0.

[0044] The second estimator 3 comprises a third device 7 comprising the model reference adaptive control (MRAC) model and a fourth device 8 comprising the artificial neuronal network (ANN) model respectively performing a parameter estimation algorithm. Alternatively, another quantity of the devices, only one or more than two, or another kind of models, e.g. a concurrent learning adaptive control, can be provided.

[0045] The third device 7 is configured to perform a third parameter estimation algorithm according to the model reference adaptive control (MRAC) model estimating a revised estimated second motor parameter R.sub.s,2 based on the estimated first motor parameter Ψ.sub.PM,1 and on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e.

[0046] The third device 7 further estimates a third motor parameter L.sub.s. The third motor parameter comprises an inductance. Alternatively, another third motor parameter is estimated or no third motor parameter is estimated.

[0047] The fourth device 8 is configured to perform a fourth parameter estimation algorithm according to the artificial neuronal network (ANN) model estimating a revised estimated first motor parameter Ψ.sub.PM,2 based on the estimated second motor parameter R.sub.s,1 and on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e.

[0048] The initially determined first motor parameter Ψ.sub.PM,0 and the initially determined second motor parameter R.sub.s,0 are constant values. Alternatively, the initially determined motor parameters are variable values.

[0049] The estimators 2, 3 and the devices 5, 6, 7, 8 are illustrated as separate modules, nevertheless, alternatively, they can, entirely or partly, be integrated in one or several modules.

[0050] In the embodiment provided as a method, reference sign 1′ depicts a method for estimating a motor parameter of an electric motor.

[0051] In use, the method for estimating a motor parameter 1′ inputs the operating parameters u.sub.d,q, i.sub.d,q, ω. Further, the initially determined second motor parameter R.sub.s,0 and the initially determined first motor parameter Ψ.sub.PM,0 are set.

[0052] Based on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e and the initially determined second motor parameter R.sub.s,0, the first estimated motor parameter Ψ.sub.PM,1 is estimated by a first parameter estimation algorithm 5′. Further, based on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e and the initially determined first motor parameter Ψ.sub.PM,0, the estimated second motor parameter R.sub.s,1 is estimated by a second parameter estimation algorithm 6′.

[0053] Furthermore, based on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e and the first estimated motor parameter Ψ.sub.PM,1, the revised estimated second motor parameter R.sub.s,2 is estimated by a third parameter estimation algorithm 7′. Moreover, based on the operating parameters u.sub.d,q, i.sub.d,q, ω.sub.e and the estimated second motor parameter R.sub.s,1, the revised estimated first motor parameter Ψ.sub.PM,2 is estimated by the fourth parameter estimation algorithm 8′.

[0054] The first parameter estimation algorithm 5′ and the second parameter estimation algorithm 6′ are components of the first estimator 2′ which denotes a device as well as a software module. The third parameter estimation algorithm 7′ and the fourth parameter estimation algorithm 8′ are components of the first estimator 3′ which also denotes a device as well as a software module.

[0055] As mentioned above, the first motor parameter comprises the permanent magnet flux linkage Ψ.sub.PM and the second motor parameter comprises the phase resistance R.sub.s. Alternatively, other motor parameters can be estimated.

[0056] The estimate of the estimated first motor parameter Ψ.sub.PM,1 and of the revised estimated first motor parameter Ψ.sub.PM,2 are performed by the artificial neuronal network (ANN) model and the estimate of the estimated second motor parameter R.sub.s,1 and of the revised estimated second motor parameter R.sub.s,2 are performed by a model reference adaptive control (MRAC) model. As also mentioned above, the estimates can be performed by only one kind of models or by another kind of models, e.g. a concurrent learning adaptive control.

[0057] The initially determined first motor parameter Ψ.sub.PM,0 and the initially determined second motor parameter R.sub.s,0 are set as constant values. Alternatively, they can be variable values.

[0058] The third parameter estimation algorithm 7′ further estimates the third motor parameter which is an inductance L.sub.S. Alternatively, another or no further motor parameter is estimated.

[0059] FIG. 2 shows diagrams demonstrating an efficacy of the equipment or method according to the invention.

[0060] The upper diagrams depict the second motor parameter R.sub.s, the diagrams in the middle depict the inductance L.sub.S, and the lower diagrams depict the first motor parameter Ψ.sub.PM. On the left, results of the estimation of the first estimator (2, 2′) are illustrated. On the right, results of the estimation of the second estimator (3, 3′) are illustrated.

[0061] The solid lines indicate estimated values and the dashed lines indicate measured values.

[0062] At 12 seconds, there is an increase in motor speed, and at 7 seconds and 18 seconds, there is an increase in the applied load torque.

[0063] In the diagrams depicting the results of the first estimator (2, 2′), a deviation of the estimated parameters due to the increase in the motor speed and in the applied load is to be seen. Moreover, as to be seen in the diagrams on the right in the results of the second estimator (3, 3), the device and the algorithm according to the invention considerably improve the result since, except a brief peak at the time of the change, the revised estimated parameters Ψ.sub.PM,2 and R.sub.s,2 remain unchanged during dynamic load changes.

[0064] The invention has been described in conjunction with various embodiments herein. However, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. Such modifications may involve other features, which are already known in the art and may be used instead of or in addition to features already described herein. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality.

[0065] The List of Reference Signs is as Follows: [0066] 1 equipment for estimating a motor parameter [0067] 1′ method for estimating a motor parameter [0068] 2 first estimator [0069] 2′ first estimator [0070] 3 second estimator [0071] 3′ second estimator [0072] 4 input interface [0073] 5 first device [0074] 5′ first parameter estimation algorithm [0075] 6 second device [0076] 6′ second parameter estimation algorithm [0077] 7 third device [0078] 7′ third parameter estimation algorithm [0079] 8 fourth device [0080] 8′ fourth parameter estimation algorithm [0081] Ψ.sub.PM first motor parameter (permanent magnet flux linkage) [0082] Ψ.sub.PM,0 initially determined first motor parameter [0083] Ψ.sub.PM,1 estimated first motor parameter [0084] Ψ.sub.PM,2 revised estimated first motor parameter [0085] R.sub.s second motor parameter (phase resistance) [0086] R.sub.s,0 initially determined second motor parameter [0087] R.sub.s,1 estimated second motor parameter [0088] R.sub.s,2 revised estimated second motor parameter [0089] L.sub.S third motor parameter (inductance) [0090] u.sub.d,q detected voltage [0091] i.sub.d,q detected current [0092] ω.sub.e detected electrical speed [0093] ANN artificial neuronal network [0094] MRAC model reference adaptive control