Patent Description:
An online estimation of motor parameters enables a condition monitoring of an electric motor such as an estimation of phase failures or an estimation of a coil temperature.

In particular, for the coil temperature estimation, an accurate estimation of a phase resistance RS is necessary. However, an estimation of the phase resistance RS depends on a value of a permanent magnet flux linkage ΨPM. Moreover, during load or speed changes, an existence of non-modeled voltage losses causes deviations in the estimated phase resistance RS.

A "Model Reference Adaptive Controller (MRAC)"and an "Adaline Neural Network (ANN)" are well-known parameter estimation algorithms. In particular, the MRAC algorithm can be used for estimating the phase resistance RS and the ANN algorithm can be used for estimating the permanent magnet flux linkage ΨPM so that an algorithm combining these two algorithms appears suitable for requested estimation. The documents "<NPL>, as well as "<NPL>, and also patents <CIT>, <CIT> and <CIT> also disclose algorithms for electric motor parameter estimation.

However, due to a rank deficiency, a simultaneous estimation of the permanent magnet flux linkage ΨPM and of the phase resistance RS is impossible under reference control so that a direct current is set to zero (Id = <NUM> A). Therefore, in a MRAC model, the permanent magnet flux linkage ΨPM is set to a constant value to estimate the phase resistance RS and, in an ANN model, the phase resistance RS is set to a constant value to estimate the permanent magnet flux linkage ΨPM. However, these models do not account for the non-modeled voltage losses during load and speed changes and, therefore, deviations in the estimated parameters from their actual values are caused. However, these deviations cause errors in the coil temperature estimation.

Therefore, the object underlying the invention is to remedy the above disadvantages and to provide an equipment and a method which enables an accurate estimation of motor parameters on occurring operating conditions.

The object is achieved by an equipment according to claim <NUM>, a method according to claim <NUM> and a computer program product according to claim <NUM>.

Advantageous further developments are included in the dependent claims.

According to an aspect of the invention, an equipment for estimating motor parameters comprises an input interface configured to receive an operating parameter of a motor, a first device configured 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, and a second device configured 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. The equipment further comprises a third device configured 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 configured 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.

By such an equipment, an influence of a non-modeled operating parameter can be considered in order to provide a more exact estimate of the motor parameters.

According to the invention, the operating parameter comprises at least one of a detected voltage, a detected current, and a detected electrical speed, the first motor parameter comprises a permanent magnet flux linkage, and the second motor parameter comprises a phase resistance.

By the selection of these operating parameters and motor parameters to be estimated, an estimation of the motor parameters necessary for estimating a coil temperature can be accurately performed.

By the provision of the respective parameter estimation algorithm for the estimation of the motor parameters, suitable parameter estimation algorithm are provided and an accurate and effective estimation of the motor parameters is possible.

In another advantageous implementation of the equipment, the initially determined first motor parameter and the initially determined second motor parameter are constant values.

By this determination, suitable start values for the estimation are provided.

In yet another advantageous implementation of the equipment, the third or fourth device is configured to estimate a third motor parameter.

By the estimation of a further parameter in a more accurate manner, the estimation of the coil temperature can be improved or an estimation of further parameters is possible.

In still another advantageous implementation of the equipment, the third motor parameter comprises an inductance.

By the estimation of the inductance, failure of a coil can be determined.

By such a method claimed in claim <NUM>, the influence of the non-modeled operating parameter can be considered in order to provide a more exact estimate of the motor parameters.

According to the invention, the operating parameter comprises a detected voltage, a detected current and a detected electrical speed, the first motor parameter comprises a phase resistance, and the second motor parameter comprises a permanent magnet flux linkage.

By the selection of these operating parameters and motor parameters in the method, an estimation of the motor parameters necessary for estimating the coil temperature can be accurately performed.

By the use of the respective parameter estimation algorithm for the estimation of the motor parameters, suitable parameter estimation algorithms are used and an accurate and effective estimation of the motor parameters is possible.

In a further advantageous implementation of the method, the initially determined first motor parameter and the initially determined second motor parameter are set as constant values.

By this determination, suitable start values for the estimation are used.

In yet another advantageous implementation, the method further comprising the step: estimating the third motor parameter.

By the estimation of the further motor parameter, the estimation of the coil temperature can be improved or the estimation of further motor parameters is possible.

According to a further advantageous implementation, the third motor parameter comprises the inductance.

According to a further aspect, a computer program product comprises instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method.

By the estimation of the inductance, the failure of a coil can be determined.

Below, the invention is elucidated by means of embodiments referring to the attached drawings.

<FIG> shows a block diagram of an equipment and of an algorithm according to the invention.

In the embodiment provided as an equipment, reference sign <NUM> depicts an equipment for estimating a motor parameter of an electric motor (not shown).

A first motor parameter comprises a permanent magnet flux linkage ΨPM. A second motor parameter comprises a phase resistance Rs. Alternatively, other motor parameters can be estimated.

The equipment <NUM> comprises a first estimator <NUM> and a second estimator <NUM>. Furthermore, the equipment <NUM> comprises an input interface <NUM>.

The input interface <NUM> receives operating parameters of the electric motor. The input interface <NUM> receives a detected voltage ud,q, a detected current id,q, and a detected electrical speed ωe. In alternative embodiments, not all of these operating parameters or additional operating parameters are input.

The first estimator <NUM> comprises a first device <NUM> comprising an artificial neuronal network (ANN) model and a second device <NUM> 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, can be provided.

The first device <NUM> is configured to perform a first parameter estimation algorithm according to the artificial neuronal network (ANN) model estimating an estimated first motor parameter ΨPM,<NUM> based on the operating parameters ud,q, id,q, ωe. and on an initially determined second motor parameter Rs,<NUM>.

The second device <NUM> is configured to perform a second parameter estimation algorithm according to the model reference adaptive control (MRAC) model estimating an estimated second motor parameter Rs,<NUM> based on the operating parameters ud,q, id,q, ωe and on an initially determined first motor parameter ΨPM,<NUM>.

The second estimator <NUM> comprises a third device <NUM> comprising the model reference adaptive control (MRAC) model and a fourth device <NUM> 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, can be provided.

The third device <NUM> 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 Rs,<NUM> based on the estimated first motor parameter ΨPM,<NUM> and on the operating parameters ud,q, id,q, ωe.

The third device <NUM> further estimates a third motor parameter Ls. The third motor parameter comprises an inductance. Alternatively, another third motor parameter is estimated.

The fourth device <NUM> is configured to perform a fourth parameter estimation algorithm according to the artificial neuronal network (ANN) model estimating a revised estimated first motor parameter ΨPM,<NUM> based on the estimated second motor parameter Rs,<NUM> and on the operating parameters ud,q, id,q, ωe.

The initially determined first motor parameter ΨPM,<NUM> and the initially determined second motor parameter Rs,<NUM> are constant values. Alternatively, the initially determined motor parameters are variable values.

The estimators <NUM>, <NUM> and the devices <NUM>, <NUM>, <NUM>, <NUM> are illustrated as separate modules, nevertheless, alternatively, they can, entirely or partly, be integrated in one or several modules.

In the embodiment provided as a method, reference sign <NUM>' depicts a method for estimating a motor parameter of an electric motor.

In use, the method for estimating a motor parameter <NUM>' inputs the operating parameters ud,q, id,q, ω. Further, the initially determined second motor parameter Rs,<NUM> and the initially determined first motor parameter ΨPM,<NUM> are set.

Based on the operating parameters ud,q, id,q, ωe and the initially determined second motor parameter Rs,<NUM>, the first estimated motor parameter ΨPM,<NUM> is estimated by a first parameter estimation algorithm <NUM>'. Further, based on the operating parameters ud,q, id,q, ωe and the initially determined first motor parameter ΨPM,<NUM>, the estimated second motor parameter Rs,<NUM> is estimated by a second parameter estimation algorithm <NUM>'.

Furthermore, based on the operating parameters ud,q, id,q, ωe and the first estimated motor parameter ΨPM,<NUM>, the revised estimated second motor parameter Rs,<NUM> is estimated by a third parameter estimation algorithm <NUM>'. Moreover, based on the operating parameters ud,q, id,q, ωe and the estimated second motor parameter Rs,<NUM>, the revised estimated first motor parameter ΨPM,<NUM> is estimated by the fourth parameter estimation algorithm <NUM>'.

The first parameter estimation algorithm <NUM>' and the second parameter estimation algorithm <NUM>' are components of the first estimator <NUM>' which denotes a device as well as a software module. The third parameter estimation algorithm <NUM>' and the fourth parameter estimation algorithm <NUM>' are components of the first estimator <NUM>' which also denotes a device as well as a software module.

As mentioned above, the first motor parameter comprises the permanent magnet flux linkage ΨPM and the second motor parameter comprises the phase resistance Rs. Alternatively, other motor parameters can be estimated.

The estimate of the estimated first motor parameter ΨPM,<NUM> and of the revised estimated first motor parameter ΨPM,<NUM> are performed by means of the artificial neuronal network (ANN) model and the estimate of the estimated second motor parameter Rs,<NUM> and of the revised estimated second motor parameter Rs,<NUM> are performed by means of 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.

The initially determined first motor parameter ΨPM,<NUM> and the initially determined second motor parameter Rs,<NUM> are set as constant values. Alternatively, they can be variable values.

The third parameter estimation algorithm <NUM>' further estimates the third motor parameter which is an inductance LS. Alternatively, another or no further motor parameter is estimated.

<FIG> shows diagrams demonstrating an efficacy of the equipment or method according to the invention.

The upper diagrams depict the second motor parameter Rs, the diagrams in the middle depict the inductance LS, and the lower diagrams depict the first motor parameter ΨPM. On the left, results of the estimation of the first estimator (<NUM>, <NUM>') are illustrated. On the right, results of the estimation of the second estimator (<NUM>, <NUM>') are illustrated.

The solid lines indicate estimated values and the dashed lines indicate measured values.

At <NUM> seconds, there is an increase in motor speed, and at <NUM> seconds and <NUM> seconds, there is an increase in the applied load torque.

Claim 1:
An equipment for estimating a motor parameter (<NUM>), the equipment (<NUM>) comprising
an input interface (<NUM>) configured to receive an operating parameter (ud,q, id,q, ωe) of an electric motor,
a first device (<NUM>) configured to perform a first parameter estimation algorithm (<NUM>') estimating an estimated first motor parameter (ΨPM,<NUM>) based on the operating parameter (ud,q, id,q, ωe) and on an initially determined second motor parameter (Rs,<NUM>), and
a second device (<NUM>) configured to perform a second parameter estimation algorithm (<NUM>') estimating an estimated second motor parameter (Rs,<NUM>) based on the operating parameter (ud,q, id,q, ωe) and on an initially determined first motor parameter (ΨPM,<NUM>),
wherein the equipment (<NUM>) further comprises
a third device (<NUM>) configured to perform a third parameter estimation algorithm (<NUM>') estimating a revised estimated second motor parameter (Rs,<NUM>) based on the estimated first motor parameter (ΨPM,<NUM>) and on the operating parameter (ud,q, id,q, ωe), and
a fourth device (<NUM>) configured to perform a fourth parameter estimation algorithm (<NUM>') estimating a revised estimated first motor parameter (ΨPM,<NUM>) based on the estimated second motor parameter (Rs,<NUM>) and on the operating parameter (ud,q, id,q, ωe),
wherein the operating parameter (ud,q, id,q, ωe) comprises at least one of a detected voltage (ud,q), a detected current (id,q), and a detected electrical speed (ωe), a first motor parameter (ΨPM) comprises a permanent magnet flux linkage, and a second motor parameter (Rs) comprises a phase resistance,
characterized in that
the first device (<NUM>) and the fourth device (<NUM>) comprise an artificial neuronal network (ANN) model performing the first and fourth parameter estimation algorithm (<NUM>', <NUM>'), and
the second device (<NUM>) and the third device (<NUM>) comprise a model reference adaptive control (MRAC) model performing the second and third parameter estimation algorithm (<NUM>', <NUM>').