WO2023006296A1 - Procédé et appareil pour déterminer une température de point d'accès sans fil dans un composant d'un moteur électrique pour un système d'entraînement électrique - Google Patents

Procédé et appareil pour déterminer une température de point d'accès sans fil dans un composant d'un moteur électrique pour un système d'entraînement électrique Download PDF

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Publication number
WO2023006296A1
WO2023006296A1 PCT/EP2022/066295 EP2022066295W WO2023006296A1 WO 2023006296 A1 WO2023006296 A1 WO 2023006296A1 EP 2022066295 W EP2022066295 W EP 2022066295W WO 2023006296 A1 WO2023006296 A1 WO 2023006296A1
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WO
WIPO (PCT)
Prior art keywords
temperature
variables
electric motor
temperature information
value
Prior art date
Application number
PCT/EP2022/066295
Other languages
German (de)
English (en)
Inventor
Christian Hahn
Uwe Knappenberger
Wael Hilali
Rene Diener
Mukunda GOPALAKRISHNAN
Svetlana ZHITKOVA
Mareike Schenk
Rainer Keck
Jannik LEES
Marco Zucchini
Original Assignee
Robert Bosch Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch Gmbh filed Critical Robert Bosch Gmbh
Publication of WO2023006296A1 publication Critical patent/WO2023006296A1/fr

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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors
    • H02P29/60—Controlling or determining the temperature of the motor or of the drive
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0014—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks

Definitions

  • the invention relates to electric motors for electric drive systems, in particular methods for determining a maximum temperature in a component of the electric motor, in particular for carrying out a power or torque limitation.
  • the heat generated by an electric motor is therefore monitored during operation and overheating is prevented by limiting the motor torque, i. H. by limiting the current consumption.
  • Temperature sensors usually as temperature-sensitive resistors, such as for example NTC (Negative Thermal Coefficient) sensors, on stator coils. These measure a temperature at a specific point on the stator, but this usually does not correspond to a maximum temperature in the electric motor, since the position at which the maximum temperature occurs differs from the position or positions of the temperature sensors.
  • the position at which the maximum temperature occurs in the component of the electric motor is called the hotspot temperature.
  • transfer models are usually used to convert a temperature measured by a temperature sensor into a hotspot temperature.
  • thermal node models to determine the temperature distribution in the components of the electric motor and to use this to determine the maximum temperature at the hotspot.
  • these node models must be designed to be very complex in order to provide sufficient accuracy for determining a maximum temperature.
  • Such a simulation cannot be implemented in real time in conventional control units for electric motors.
  • This object is achieved by the method for providing a hotspot temperature in a component of an electric motor according to claim 1 and by a corresponding device and a motor system with an electric motor.
  • a method for operating an electric motor and for determining temperature information regarding a hotspot temperature in a component of the electric motor is provided, with the following steps:
  • Hotspot temperature in electric motor components is typically difficult because the hotspot, i. H. an area in a component where a maximum operating temperature occurs, is internal to the component and therefore difficult to access from the outside.
  • the hotspot position ie the position within the component at which the maximum temperature occurs, depends on the operating point of the electric motor, so that the transfer model for determining the hotspot temperature depends on the measured component temperature or temperatures.
  • the above method provides for determining the hotspot temperature of a component of the electric motor of the electric drive system using a temperature model with a data-based model.
  • this temperature model is operated on the input side with operating variables in order to obtain temperature information about the hotspot temperature at the hotspot of the electric motor component.
  • the operating variables can be measured variables, such as phase currents, a speed of the electric motor, an operating voltage or phase voltages of the electric motor and one or more measured temperatures from temperature sensors arranged on components of the electric motor and a coolant temperature for cooled electric motors, such as oil or another type of Coolant, and others include.
  • the operating variables can also include calculated variables as input variables for the data-based temperature model, such as d and q components of the motor current, which are dependent on the rotor position of a rotor of the electric motor.
  • time-delayed values of one or more of the operating variables can also be taken into account as input variables of the data-based model.
  • the data-based model can include a neural network or a Gaussian process model and, in particular, be embedded in an NARX structure in which historical values of the operating variables are also taken into account on the input side of the data-based model.
  • the use of a Gaussian process model has the particular advantage of a low computing load in an engine control unit for the electric motor, in particular if this is equipped with a hardware calculation unit for Gaussian process models.
  • the data-based temperature model is preferably trained based on simulation and/or measurement data of temperatures at actual hotspots in components of the electric motor.
  • the training is based on the Input variables that include the operating variables and includes at least one delayed value of an operating variable.
  • the training is carried out, for example, by simulating time series of the operating variables, in particular using a thermal node model, such as FEM or LPTN (Lumped Parameter Thermal Network) models, so that the temperature distribution in the components of the electric motor can be determined in each time step .
  • the corresponding set of input variables for the temperature model which includes the current values and at least one historical value of one or more of the operating variables, is determined from the time series of the operating variables.
  • the maximum hotspot temperature within the component or the temperature difference between the maximum temperature in the component of the current and previous time step is determined as the output variable.
  • a temperature model can be provided that can be used to reliably determine the temperature of the hotspot of a component of the electric motor.
  • This temperature model can be provided using a temperature sensor on the component or without an additional component temperature and can therefore be flexibly adapted to the type of electric motor.
  • the profile value of at least one of the one or more operating variables can correspond to a value generated from a profile of the relevant at least one of the one or more operating variables using a low-pass filter.
  • a temperature gradient or a temperature change related to the current time step through the data-based model is determined, with a temperature value being determined by integration of the temperature gradient.
  • a temperature value can be determined by the data-based model as temperature information.
  • a method for training a data-based model for a temperature model for determining temperature information relating to a hotspot in a component of an electric motor, with sets of training variables being generated, with each set of training data assigning an input variable set to a corresponding piece of temperature information, the input variable set consisting of curves of one or more operating variables is generated and the temperature information is obtained by simulating the curves of the one or more operating variables, in particular using a thermal node model, such as FEM or LPTN (Lumped Parameter Thermal Network) models, wherein the Temperature information is determined depending on the maximum temperature in the components of the electric motor.
  • a thermal node model such as FEM or LPTN (Lumped Parameter Thermal Network) models
  • a device for operating an electric motor and for determining temperature information relating to a hotspot temperature in a component of the electric motor, the device being designed for:
  • Figure 1 is a schematic representation of an electric motor with a
  • FIG. 2 shows an exemplary profile of a hotspot temperature and a temperature measured using a temperature sensor
  • Figure 3 is a block diagram to illustrate a
  • Model structure for determining a hotspot temperature based on operating parameters of the electric motor.
  • FIG. 1 schematically shows a cross-sectional illustration through an electric motor 2 as part of a drive system 1.
  • the electric motor 2 has a stator 21 and a rotor 22 mounted on a shaft, which represent components of the electric motor.
  • the stator 21 can be provided with stator coils 211, which can be controlled electrically via phase voltages and phase currents.
  • rotor coils and both stator and rotor coils can also be provided.
  • the electric motor 2 is controlled with the aid of a control unit 10, which provides for the control of the electric motor by applying phase voltages to the stator coil 211 in accordance with a commutation pattern.
  • the control unit 10 can therefore also include a power driver circuit 11 in the form of a B6 bridge circuit or the like in a manner known per se.
  • the rotor 22 can be coupled to a position sensor 23 which can detect a rotor position of the rotor with respect to the arrangement of the stator coils 211 .
  • a temperature sensor 25 can also be arranged on a component of the electric motor 2 in order to measure a temperature at a specific position inside the electric motor 2 . This position usually does not correspond to the hotspot position and therefore records a temperature that deviates from the hotspot temperature.
  • a temperature time diagram is shown in FIG. It can be seen that with a varying profile of the hotspot temperature, the maximum of the measured temperature lags behind the maximum of the hotspot temperature. This indicates a dynamic behavior of the hotspot temperature TH with regard to the current operating state of the electric motor 2 .
  • the control unit 10 operates the electric motor 2 in a manner known per se by specifying the phase voltages in order to set specific phase currents in such a way that a specified motor torque is set.
  • phase currents being applied, power is converted in the electric motor 2 , which can lead to components of the electric motor 2 being heated.
  • the heating occurs unevenly in the components and areas or points in the components with a maximum temperature, the so-called hotspots, can develop.
  • the hotspot temperature is then present at these hotspots.
  • the control unit 10 is also designed to operate a temperature model in real time in order to always have information about the hotspot temperature available.
  • the hot spot temperature is monitored and a torque limit or power limit can be activated if the hot spot temperature is at or above a threshold.
  • the torque limitation can also be achieved as a function of a torque threshold value that is dependent on the hotspot temperature.
  • the maximum phase current can be limited depending on the hotspot temperature.
  • FIG. 3 schematically shows a functional circuit diagram for a temperature model 30 with a data-based model 35 that can be executed in the control unit 10.
  • Operating variables B are supplied to the temperature model 30 in successive journals.
  • the operating variables B can be measured or provided in some other way, e.g. modeled operating variables, such as the phase voltages, the phase currents, the motor speed, a measured temperature in the electric motor and/or a measured temperature within the drive system.
  • Calculated or modeled operating variables can, for example, also be variables determined by calculation from the operating variables, such as a d-current and q-current with regard to the rotor position, which are determined from the stator currents and the rotor position.
  • the respective current values of the operating variables can be limited in an optional input-side limiting block 31 in order to prevent the subsequent data-based model 35 from being evaluated in operating areas in which the data-based model 35 has not been trained.
  • the operating variables B are limited in their value ranges, with the value ranges being predetermined and being able to result in particular from the training data sets for training the data-based model 35 .
  • the current values of the limited operating variables B and/or one or more historical values of one or more of the operating variables are now provided as input variables with the aid of a delay block 32 .
  • the one or the multiple history values correspond to specific values of the (limited) operating variables for previous time steps. These can be determined by delaying the values of the operating variables, in particular using a shift register.
  • the time delays can each correspond to a number of time steps, which is specified according to the configuration of the temperature model.
  • one or more low-pass filters in particular in the form of a PT1 element, can be implemented in the delay block 32, which provides one or more filtered operating variables as a progress variable of the operating variable or input variable(s).
  • temperature information determined using the data-based model can also be taken into account when determining the historical progress variables.
  • This current modeled temperature information provided on the output side of the data-based temperature model can also be used as a progression variable and processed with the aid of the delay block 32, in particular by filtering or a corresponding delay, in order to provide another option for dynamic mapping of the overall system.
  • the respective current value and the delayed or filtered values of the temperature information can be provided as additional input variables.
  • the input variables provided in this way are pre-processed in a pre-processing block 23, in which the multiple input variables are filtered or sampled again or differences between the individual input variables are calculated in order to obtain pre-processed input variables.
  • the pre-processed input variables are then fed to the data-based model 35 in order to obtain a temperature gradient as temperature information for each magazine.
  • This temperature gradient can be limited in a second output-side limiting block 36 in order to limit temperature gradients that are too high as implausible in terms of absolute value.
  • the finite temperature gradient can be processed in an integration block 37 to obtain the actual hot spot temperature of the hot spot by integration.
  • the temperature gradient and the temperature value are therefore available as temperature information.
  • Temperature gradient and/or temperature value can be fed back into delay block 23 . There, the current value of the temperature gradient and/or the temperature value and one or more course details of the temperature gradient and/or the temperature value can be provided as an input variable for the calculation of the next time step.
  • the integration block 37 requires an initial temperature value, which is determined when the control unit 10 is switched on.
  • a switch-off temperature of the hotspot stored when control unit 10 was switched off can be used as the last determined temperature value of temperature model 30 and a known cooling behavior of the drive system can be determined using a further physical or data-based model.
  • a model-based determined temperature can be assumed as the hotspot temperature at the time when the control unit 10 is switched on.
  • the data-based model of the temperature model is trained on training data sets.
  • the data-based model can be assumed to be a Gaussian process model, a neural network, or a comparable function model that can be trained using training data sets.
  • the data-based model is thus embedded in a NARX structure.
  • the operating variables to be used are first selected.
  • the temperature distribution in the components of the electric motor is now determined based on the progression of operating variables using a simulation model, for example an FEM or LPTN (Lumped Parameter Thermal Network) model. From the maximum temperature that occurs (at the hotspot), temperature information can now be determined as, for example, a temperature gradient with respect to a temperature for a previously calculated calculation step.
  • the absolute temperature value for the training data set can also be determined as temperature information.
  • a training data record then corresponds to the input variable set determined from the operating variables with the delay block 32 and the associated temperature information.
  • the data-based model can be trained in a conventional manner for Gaussian process models or neural networks.
  • the entire drive system can also be taken into account, which can also include the power circuit and transmission in addition to the electric motor. This is achieved by taking operating variables from the entire drive system into account, such as coolant temperature and the like. As a result, thermal factors influencing the components of the electric motor can be sufficiently taken into account.
  • test bench data i. H. a measurement of the electric motor on a test bench, further training data records can be created for training the data-based temperature model.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Control Of Electric Motors In General (AREA)

Abstract

L'invention concerne un procédé pour faire fonctionner un moteur électrique et pour déterminer des informations de température relatives à une température de point d'accès sans fil dans un composant du moteur électrique, comprenant les étapes suivantes : - fournir une ou plusieurs variables de fonctionnement, qui caractérisent le fonctionnement du moteur électrique, dans des étapes de temps successives ; - générer, pour chaque étape de temps, un ensemble de variables d'entrée qui comprend la valeur actuelle d'au moins une parmi la ou les variables de fonctionnement et au moins une valeur historique d'au moins une parmi la ou les variables de fonctionnement dans chaque cas, la valeur historique particulière correspondant à une valeur fournie au préalable de la ou des variables de fonctionnement ou étant formée à partir de valeurs précédentes de la ou des variables de fonctionnement ; - déterminer les informations de température par rapport à la température de point d'accès sans fil à l'aide d'un modèle à base de données entraîné sur la base de l'ensemble de variables d'entrée, le modèle à base de données étant entraîné pour délivrer les informations de température sur la base de l'ensemble de variables d'entrée ; - signaler des informations de température.
PCT/EP2022/066295 2021-07-29 2022-06-15 Procédé et appareil pour déterminer une température de point d'accès sans fil dans un composant d'un moteur électrique pour un système d'entraînement électrique WO2023006296A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021208167.3A DE102021208167A1 (de) 2021-07-29 2021-07-29 Verfahren und Vorrichtung zum Ermitteln einer Hotspot-Temperatur in einer Komponente eines Elektromotors für ein elektrisches Antriebssystem
DE102021208167.3 2021-07-29

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WO2023006296A1 true WO2023006296A1 (fr) 2023-02-02

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PCT/EP2022/066295 WO2023006296A1 (fr) 2021-07-29 2022-06-15 Procédé et appareil pour déterminer une température de point d'accès sans fil dans un composant d'un moteur électrique pour un système d'entraînement électrique

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WO (1) WO2023006296A1 (fr)

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DE102023203111A1 (de) 2023-04-04 2024-10-10 Zf Friedrichshafen Ag Verfahren zur Bestimmung einer Temperatur einer elektrischen Maschine

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
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LEE JUN ET AL: "Temperature Estimation of PMSM Using a Difference-Estimating Feedforward Neural Network", IEEE ACCESS, IEEE, USA, vol. 8, 15 July 2020 (2020-07-15), pages 130855 - 130865, XP011800830, DOI: 10.1109/ACCESS.2020.3009503 *
QI FANG ET AL: "Model Predictive Control of a Switched Reluctance Machine for Guaranteed Overload Torque", IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 55, no. 2, 7 October 2018 (2018-10-07), pages 1321 - 1331, XP011714311, ISSN: 0093-9994, [retrieved on 20190313], DOI: 10.1109/TIA.2018.2874618 *
WALLSCHEID OLIVER: "Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges", IEEE OPEN JOURNAL OF INDUSTRY APPLICATIONS, IEEE, vol. 2, 23 June 2021 (2021-06-23), pages 204 - 223, XP011869995, DOI: 10.1109/OJIA.2021.3091870 *
WILHELM KIRCHG\"ASSNER ET AL: "Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning", ARXIV.ORG, 8 April 2021 (2021-04-08), XP081919473 *

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