A method for determining at least one temperature value of an electric motor by an electronic computing device, a corresponding computer program product

Abstract

At least one temperature value 18 of an electric motor is determined by an electronic computing device 14. A model 20 of the electric motor 12 is provided by the electronic computing device, wherein t

Description

[0001] The present invention relates to the field of automobiles. More specifically, the present invention relates to a method for determining at least one temperature value of an electric motor by an electronic computing device. Furthermore, the present invention relates to a corresponding computer program product, a corresponding non-transitory computer-readable storage medium, as well as to a corresponding electronic computing device. BACKGROUND INFORMATION

[0002] Electric motor thermal model is critical for electric vehicle traction motor controls, concerning thermal safety and efficiency improvement. A motor thermal model determines the stator and rotor temperatures, where sensors are not practical to install in production machines. There are two mainstream methods used as the motor thermal model. A first method is the thermal-network-based method. Thermal and heat transfer analysis is performed on each component of the electric motor based on a so-called finite element analysis tool. This has drawbacks, namely that each different motor needs to perform complicated analysis by a thermal / motor design expert, implemented repeatedly by software developers. Furthermore, the response time is slow due to the integration of power transfer on top of initial conditions. The second method is the flux-based method. Permanent magnet flux varies with the rotor temperature. Once the flux is observed, rotor magnet temperature can be derived. Furthermore, when speed is low or stands still, there is no back-EMF. Thus, flux cannot be observed. Furthermore, the accuracy is highly relying on an accurate invertor loss model, due to the input of stator voltages. SUMMARY OF THE INVENTION

[0003] It is an object of the present invention to provide a method, a corresponding computer program product, a corresponding non-transitory computer-readable storage medium, as well as a corresponding electronic computing device, by which a temperature value of the electric motor can be determined in an improved manner.

[0004] This object is solved by a method, a corresponding computer program product, a corresponding non-transitory computer-readable storage medium, as well as a corresponding electronic computing device according to the independent claims. Advantageous embodiments are presented in the dependent claims.

[0005] One aspect of the invention relates to a method for determining at least one temperature value of an electric motor by an electric computing device. A model of the electric motor is provided by the electronic computing device, wherein the model is based on a machine learning algorithm.

[0006] At least one parameter of the electric motor is received by the electronic computing device. The at least one temperature value is determined depending on the model and the at least one parameter by the electronic computing device.

[0007] Therefore, the machine-learning-based thermal model can be used on any kind of electric motor. Once set up, parameters can be learned automatically, and there is no need to derive complicated thermal analysis for each different motor. Furthermore, the accuracy can be much improved compared with the two traditional methods. This leads to better thermal safety and efficiency improvement in electric vehicles.

[0008] In particular, to overcome the drawbacks of the known methods, a machinelearning-based electric motor thermal model is proposed in this invention. For example, the input and output nodes / signals are typical for electric motor thermal models, while flexible to adjust based on availability and necessity. In order to collect data and train the model, the output signals such as a temperature value can be measured by infrared temperature sensors or wireless temperature sensors installed on telemetric motors, which are used for development purposes.

[0009] According to an embodiment as the at least one temperature value a stator core temperature and / or a rotor temperature and / or a rotor magnet temperature is determined.

[0010] According to another embodiment, as the at least one parameter a stator winding temperature and / or a coolant temperature and / or a coolant flow rate and / or a motor speed and / or a motor stator current and / or a motor voltage and / or a direct current voltage is received.

[0011] In another embodiment the at least one temperature value is used for electric vehicle traction motor control of the electric motor.

[0012] In another embodiment for gathering training data for training the model a temperature of a test electric motor vehicle is captured.

[0013] In another embodiment the temperature is captured by using an infrared temperature sensor and / or a wireless temperature sensor.

[0014] In particular, the method is a computer-implemented method. Therefore, another aspect of the invention relates to a computer program product comprising program code means for performing a method according to the preceding aspect.

[0015] A still further aspect of the invention relates to a non-transitory computer-readable storage medium comprising at least the computer program product according to the preceding aspect.

[0016] Furthermore, the invention relates to an electronic computing device for determining at least one temperature value of an electric motor, comprising at least one receiving device, wherein the electronic computing device is configured for performing a method according to the aspect. In particular, the method is performed by the electric computing device.

[0017] A still further aspect of the invention relates to a motor vehicle comprising at least the electronic computing device and at least the electric motor. The motor vehicle may be configured as an at least in part electrically operated motor vehicle or a fully electrically operated motor vehicle.

[0018] Advantageous embodiments of the method are to be regarded as advantageous embodiments of the computer program product, the non-transitory computer-readable storage medium, the electronic computing device, as well as the motor vehicle. The motor vehicle as well as the electronic computing device may comprise means for performing the method.

[0019] An artificial neural network / machine learning algorithm / model can be understood as a software code or a compilation of several software code components, wherein the software code may comprise several software modules for different functions, for example one or more encoder modules and one or more decoder modules.

[0020] An artificial neural network can be understood as a non-linear model or algorithm that maps an input to an output, wherein the input is given by an input feature vector or an input sequence and the output may be an output category for a classification task or a predicted sequence.

[0021] For example, the artificial neural network may be provided in a computer-readable way, for example, stored on a storage medium of the vehicle, in particular of the at least one computing unit.

[0022] The neural network comprises several modules including the encoder module and the at least one decoder module. These modules may be understood as software modules or respective parts of the neural network. A software module may be understood as software code functionally connected and combined to a unit. A software module may comprise or implement several processing steps and / or data structures.

[0023] The modules may, in particular, represent neural networks or sub-networks themselves. If not stated otherwise, a module of the neural network may be understood as a trainable and, in particular, trained module of the neural network. For example, the neural network and thus all of its trainable modules may be trained in an end-to-end fashion before the method is carried out. However, in other implementations, different modules may be trained or pre-trained individually. In other words, the method according to the invention corresponds to a deployment phase of the neural network.

[0024] The modules may represent deep neural networks and therefore comprises at least two layers. The at least two layers may also be regarded as hidden layers. Methods used in the deep neural networks may for example supervised learning, semi-supervised learning, or unsupervised learning.

[0025] A computing unit / electronic computing device may in particular be understood as a data processing device, which comprises processing circuitry. The computing unit can therefore in particular process data to perform computing operations. This may also include operations to perform indexed accesses to a data structure, for example a look-up table, LUT. The operations may also include normalizing the data from 0-1 based on a minimum and maximum range.

[0026] In particular, the computing unit may include one or more computers, one or more microcontrollers, and / or one or more integrated circuits, for example, one or more application-specific integrated circuits, ASIC, one or more field-programmable gate arrays, FPGA, and / or one or more systems on a chip, SoC. The computing unit may also include one or more processors, for example one or more microprocessors, one or more central processing units, CPU, one or more graphics processing units, GPU, and / or one or more signal processors, in particular one or more digital signal processors, DSP. The computing unit may also include a physical or a virtual cluster of computers or other of said units.

[0027] In various embodiments, the computing unit includes one or more hardware and / or software interfaces and / or one or more memory units.

[0028] A memory unit may be implemented as a volatile data memory, for example a dynamic random access memory, DRAM, or a static random access memory, SRAM, or as a non-volatile data memory, for example a read-only memory, ROM, a programmable read-only memory, PROM, an erasable programmable read-only memory, EPROM, an electrically erasable programmable read-only memory, EEPROM, a flash memory or flash EEPROM, a ferroelectric random access memory, FRAM, a magnetoresistive random access memory, MRAM, or a phase-change random access memory, PCRAM.

[0029] Further advantages, features, and details of the invention derive from the following description of preferred embodiments as well as from the drawings. The features and feature combinations previously mentioned in the description as well as the features and feature combinations mentioned in the following description of the figures and / or shown in the figures alone can be employed not only in the respectively indicated combination but also in any other combination or taken alone without leaving the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The novel features and characteristic of the disclosure are set forth in the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and together with the description, serve to explain the disclosed principles. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and / or methods in accordance with embodiments of the present subject matter are now described below, by way of example only, and with reference to the accompanying figures.

[0031] The drawings show in:

[0032] Fig. 1 a schematic side view of an embodiment of a motor vehicle comprising an embodiment of an electronic computing device; and

[0033] Fig. 2 a schematic block diagram according to an embodiment of the electronic computing device.

[0034] In the figures the same elements or elements having the same function are indicated by the same reference signs. DETAILED DESCRIPTION

[0035] In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration”. Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0036] While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawing and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[0037] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion so that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus preceded by “comprises” or “comprise” does not or do not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

[0038] In the following detailed description of the embodiment of the disclosure, reference is made to the accompanying drawing that forms part hereof, and in which is shown by way of illustration a specific embodiment in which the disclosure may be practiced. This embodiment is described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0039] Fig. 1 shows a schematic side view according to an embodiment of a motor vehicle 10. The motor vehicle 10 is at least in part electrically operated or fully electrically operated. Therefore, the motor vehicle 10 may comprise an electric motor 12. The electric motor 12 may comprise an electronic computing device 14. The electronic computing device may comprise at least one receiving device 16.

[0040] Fig. 2 shows a schematic block diagram according to an embodiment of the electronic computing device 14. In particular, Fig. 2 shows a method for determining at least one temperature value 18 of the electric motor 12. At least a model 20 of the electric motor 12 is provided by the electronic computing device 14, wherein the model 20 is based on a machine-learning algorithm. At least one parameter 22 of the electric motor 12 is received by the receiving device 16. The at least one temperature value 18 is determined depending on the model 20 and the at least one parameter 22 by the electronic computing device 14.

[0041] In particular, Fig. 2 shows that the at least one temperature value 18 that may include a stator core temperature 24 and / or a rotor temperature 26 and / or a rotor magnet temperature 28 may be determined by the model 20.

[0042] Furthermore Fig. 2 shows the at least one parameter 22 that may include a stator winding temperature 30 and / or a coolant temperature 32 and / or a coolant flow rate 34 and / or a motor speed 36 and / or a motor stator current 38 and / or a motor stator voltage 40 and / or a direct current voltage 42 is inputted into the model 20 for determining the at least one temperature value 18.

[0043] The at least one temperature value 18 is used for electric vehicle traction motor controls of the electric motor 12. Furthermore, for gathering chain data for training the model 20 a temperature of a test electric motor is captured. Furthermore, the temperatures captured by using infrared temperature sensor and / or a wireless temperature sensor.

[0044] In particular, Fig. 2 shows that the input and output nodes / signals are typical for electric motor thermal models, while flexible to adjust based on an availability and necessity. In order to collect data and train the model 20, the output signals such as the temperature values 18 may be measured by infrared temperature sensors or wireless temperature sensors installed on telemetric motors, which are used for the development purpose. Therefore, this machine-learning-based thermal model can be used on any kind of electric motor 12. Once set up, parameters can be learned automatically, and there is no need to derive complicated thermal analysis for each different electric motor 12. Accuracy can be much improved compared with the two traditional methods. This leads to better thermal safety and efficiency improvement of motor vehicles 10. Reference Signs 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 motor vehicle electric motor electronic computing device receiving device temperature value model parameter stator core temperature rotor temperature rotor magnet temperature stator winding temperature coolant temperature coolant flow rate motor speed motor stator current motor stator voltage direct current voltage

Claims

1. A method for determining at least one temperature value (18) of an electric motor (12) by an electronic computing device (14), comprising the steps of:- providing a model (20) of the electric motor (12) by the electronic computing device (14), wherein the model (20) is based on a machine learning algorithm;- receiving at least one parameter (22) of the electric motor (12) a receiving device(16) of the electronic computing device (14); and- determining the at least one temperature value (18) depending on the model (20) and the at least one parameter (22) by the electronic computing device (14).

2. The method according to claim 1,characterized in thatas the at least one temperature value (18) a stator core temperature (24) and / or a rotor temperature (26) and / or a rotor magnet temperature (28) is determined.

3. The method according to claim 1 or 2, characterized in thatas the at least one parameter (22) a stator winding temperature (30) and / or a coolant temperature (32) and / or a coolant flow rate (34) and / or a motor speed (36) and / or a motor stator current (38) and / or a motor stator voltage (40) and / or a direct current voltage (42) is received.

4. The method according to any one of claims 1 to 3, characterized in thatthe at least one temperature value (18) is used for electric vehicle traction motor controls of the electric motor (12).

5. The method according to any one of claims 1 to 4, characterized in thatfor gathering training data for training the model (20) a temperature of a test electric motor is captured.

6. The method according claim 5, characterized in thatthe temperature is captured by using an infrared temperature sensor and / or a wireless temperature sensor.

7. A computer program product comprising program code means for performing a method according to any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium comprising at least the computer program product according to claim 7.

9. An electronic computing device (14) for determining at least one temperature value (18) of an electric motor (12), comprising at least one receiving device (16), wherein the electronic computing device (14) is configured for performing a method according to any one of claims 1 to 6.Application No: GB2313558.5Examiner: Rowland HuntClaims searched: AllDate of search: 14 February 2024Patents Act 1977: Search Report under Section 17Documents considered to be relevant:Category Relevant to claims Identity of document and passage or figure of particular relevance X 1-9 US 2021 / 0055240 Al (KONISHI) see whole document, particularly para. 2 X 1-9 US 2021 / 0034021 Al (WENGER) see particularly paras. 32, 44 X 1-9 US 2021 / 0018016 Al (KIM et al.) see whole document, particularly paras. 2, 75-79 and fig. 6 v A 1-9 CN 114004164 A (UNIV SHANGHAI JIAOTONG) see whole document X 1-9 CN 113408739 A (UNITED AUTOMOTIVE ELECT SYS CO) see whole document v A 1-9 CN 107315433 B (BEIJING ELECTRIC VEHICLE CO LTD) see whole document X 1-9 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), 2023, Li Dinan et al., "Temperature Estimation in Induction Motors using Machine Learning", p. 2398-2404. see whole document X 1-9 2021 IEEE Transportation Electrification Conference (ITEC-India), 2021, Paramoji Sharanabasappa L et al., "Application of AI to Predict PMSM Temperature", p. 1-4. see whole documentCategories:X Document indicating lack of novelty or inventive step A Document indicating technological background and / or state of the art. Y Document indicating lack of inventive step if P Document published on or after the declared priority date but combined with one or more other documents of same category. before the filing date of this invention. & Member of the same patent family E Patent document published on or after, but with priority date earlier than, the filing date of this application.Field of Search:International Classification:Subclass Subgroup Valid From H02P 0029 / 60 01 / 01 / 2016 H02P 0029 / 64 01 / 01 / 2016 H02P 0029 / 66 01 / 01 / 2016

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