Temperature estimation method and related device

Abstract

The invention provides a temperature estimation method and a related device. The method comprises the following steps: acquiring a motor feedback current of a motor and an electronic control unit temperature; and the feedback current of the motor and the temperature of the electronic control unit are input into a target temperature estimation model, target temperatures of the multiple target working devices are obtained, and the target temperature estimation model is configured with target temperature estimation parameters associated with the motor. Thus, temperature estimation of the multiple target working devices can be achieved only through the motor feedback current and the temperature of the electronic control unit which are easy to obtain, additional hardware equipment and computing resources are not needed, the cost of temperature estimation of the target working devices in the motor is reduced, the requirement for an MCU memory is greatly reduced, and the reliability of the motor is improved. And a temperature estimation algorithm model is simplified.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a temperature estimation method and related devices. Background Art

[0002] Permanent magnet synchronous motors are widely used in electric vehicles due to their advantages such as high energy conversion efficiency and high power density. During the operation of the motor, the temperature of its working components is one of the key factors that reflect whether the motor and its controller can operate normally. The commonly used solution in the prior art is to build different thermal models for different working components in the motor to estimate the temperature of different working components. However, this temperature estimation solution requires building a corresponding thermal model for each working component, and each thermal model involves measuring and calculating sample data that is difficult to obtain, which requires a lot of time and computing resources. Since a large amount of computing resources are required, the memory requirements of the MCU (Microcontroller Unit) are relatively large. Summary of the invention

[0003] The embodiments of the present application provide a temperature estimation method and related devices, in order to reduce the calculation cost and time cost of motor temperature estimation and simplify the algorithm model of temperature estimation.

[0004] In a first aspect, an embodiment of the present application provides a temperature estimation method, comprising:

[0005] Get the motor feedback current and ECU temperature of the motor;

[0006] The motor feedback current and the electronic control unit temperature are input into a target temperature estimation model to obtain target temperatures of a plurality of target working devices. The target temperature estimation model is configured with target temperature estimation parameters associated with the motor.

[0007] In a second aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0008] In a third aspect, an embodiment of the present application provides a vehicle, comprising the electronic device in the second aspect of the embodiment of the present application.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps in the first aspect of the embodiment of the present application.

[0010] It can be seen that in the embodiment of the present application, the MCU first obtains the motor feedback current and the electronic control unit temperature of the motor, and then inputs the motor feedback current and the electronic control unit temperature into a target temperature estimation model configured with target temperature estimation parameters associated with the motor, to obtain the target temperatures of multiple target working devices. In this way, compared with the prior art which requires a lot of time and computing resources to measure and calculate the sample data required to establish different thermal models, and to establish different thermal models for different working devices in the motor to achieve temperature estimation, the present application can achieve temperature estimation of multiple target working devices only through easily obtained motor feedback current and electronic control unit temperature, without spending a lot of time and computing resources. Since a large amount of computing resources are not required, the memory requirements of the MCU are greatly reduced, and the cost of temperature estimation of the target working device in the motor is also reduced, simplifying the algorithm model of temperature estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 is a structural block diagram of an electronic device provided in an embodiment of the present application;

[0013] Figure 2 is a flow chart of a temperature estimation method provided in an embodiment of the present application;

[0014] Figure 3 This is an example diagram of a temperature estimation control model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0016] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0017] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0018] The following introduces the relevant terms involved in the embodiments of the present application.

[0019] Electronic Control Unit (ECU): is the hardware foundation for implementing the permanent magnet synchronous motor control algorithm, and is responsible for achieving precise control of the motor. The electronic control unit ECU includes a microcontroller unit MCU and peripheral circuits. In the embodiment of the present application, the electronic control unit ECU can also be called a controller.

[0020] MOS tube (Metal-Oxide-Semiconductor Field-Effect Transistor): Used as a power switching device in the controller of a permanent magnet synchronous motor. High temperature will damage the PN junction of the MOS tube, reducing its service life and even causing thermal runaway.

[0021] Winding: Also known as stator winding, the stator is the stationary part of the permanent magnet synchronous motor, and the stator winding is a component of the stator. The winding generates a rotating magnetic field through current to drive the motor to work, and high temperature will accelerate the aging of the winding insulation material.

[0022] Permanent magnet: also known as rotor permanent magnet, the rotor is the rotating part of the permanent magnet synchronous motor, and the rotor permanent magnet is a component of the rotor. Permanent magnets are used to provide a stable magnetic field for permanent magnet synchronous motors, and high temperature will cause irreversible demagnetization.

[0023] Simulink: It is a visual simulation tool in MATLAB, used for multi-domain simulation and model-based design.

[0024] Cftool: It is a function fitting toolbox in MATLAB, which has rich fitting algorithms and can realize a variety of linear and nonlinear function fitting.

[0025] See also Figure 1 , Figure 1 is a structural block diagram of an electronic device provided in an embodiment of the present application. Figure 1 As shown, the electronic device 10 may include one or more of the following components: a processor 11, and a memory 12 coupled to the processor 11, wherein the memory 12 may store one or more computer programs, and the one or more computer programs may be configured to implement the methods described in the following embodiments when executed by one or more processors 11.

[0026] The processor 11 may include one or more processing cores. The processor 11 uses various interfaces and lines to connect various parts of the entire electronic device 10, and executes various functions and processes data of the electronic device 10 by running or executing instructions, programs, code sets or instruction sets stored in the memory 12, and calling data stored in the memory 12. Optionally, the processor 11 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA).

[0027] The memory 12 may include a random access memory (RAM) or a read-only memory (ROM). The memory 12 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 12 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the following various method embodiments, etc. The data storage area may also store data created by the electronic device 10 during use, etc.

[0028] It can be understood that in the embodiment of the present application, the electronic device 10 may refer to the MCU in the permanent magnet synchronous motor. In practical applications, the MCU may include more or fewer structural elements than those in the above structural block diagram, which is not limited here.

[0029] A temperature estimation method provided by an embodiment of the present application is introduced below.

[0030] See also Figure 2 , Figure 2is a flow chart of a temperature estimation method provided in an embodiment of the present application. Figure 2 As shown, the temperature estimation method includes:

[0031] S201, obtaining a motor feedback current and an electronic control unit temperature of the motor.

[0032] The motor feedback current and the electronic control unit temperature are used to characterize the operating state of the permanent magnet synchronous motor. It should be noted that during the actual operation of the permanent magnet synchronous motor, the motor feedback current and the electronic control unit temperature are data that can be collected or calculated by relatively simple means, with low hardware requirements and no need to consume a lot of computing resources.

[0033] S202, inputting the motor feedback current and the electronic control unit temperature into a target temperature estimation model to obtain target temperatures of multiple target working components.

[0034] The target temperature estimation model is configured with target temperature estimation parameters associated with the motor, and the temperature estimation parameters are predetermined parameters used to characterize the correlation between the target temperature of the target working device in the motor and the motor feedback current and the temperature of the electronic control unit of the motor. That is, in the embodiment of the present application, the temperature estimation parameters are related to the motor itself and have nothing to do with the working conditions, that is, the temperature estimation parameters of the same motor under different working conditions are the same.

[0035] It can be seen that in the embodiment of the present application, the MCU first obtains the motor feedback current and the electronic control unit temperature of the motor, and then inputs the motor feedback current and the electronic control unit temperature into a target temperature estimation model configured with target temperature estimation parameters associated with the motor, to obtain the target temperatures of multiple target working devices. In this way, compared with the prior art which requires a lot of time and computing resources to measure and calculate the sample data required to establish different thermal models, and to establish different thermal models for different working devices in the motor to achieve temperature estimation, the present application can achieve temperature estimation of multiple target working devices only through easily obtained motor feedback current and electronic control unit temperature, without spending a lot of time and computing resources. Since a large amount of computing resources are not required, the memory requirements of the MCU are greatly reduced, and the cost of temperature estimation of the target working device in the motor is also reduced, simplifying the algorithm model of temperature estimation.

[0036] In one possible example, obtaining the motor feedback current and electronic control unit temperature of the motor includes: obtaining the three-phase current of the motor; calculating the motor feedback current of the motor based on the three-phase current; and obtaining the electronic control unit temperature of the motor through a first temperature sensor.

[0037] Among them, the three-phase current refers to the current form in the three-phase AC system, which is composed of three sinusoidal currents with a phase difference of 120 degrees. It should be noted that in actual applications, the controller can easily obtain the above three-phase current without consuming a lot of computing resources, and then calculate the motor feedback current based on the three-phase current.

[0038] The first temperature sensor may be arranged at a position close to the MCU so as to obtain a more accurate temperature measurement result. The first temperature sensor may be a thermocouple temperature sensor or an infrared temperature sensor, etc., which is not limited here.

[0039] It can be seen that in this example, the motor feedback current of the motor is calculated by the three-phase current that can be easily obtained, and the electronic control unit temperature of the motor is obtained by the first temperature sensor. Finally, the motor feedback current and the electronic control unit temperature are input into the target temperature estimation model to obtain the target temperatures of multiple target working components. The temperature estimation of multiple target working components can be completed without consuming a large amount of computing resources, which greatly reduces the memory demand of the MCU and also reduces the cost of temperature estimation of the target working components in the motor.

[0040] In a possible example, the motor feedback current of the motor is calculated based on the three-phase current, including: performing Clarke transformation on the three-phase current to obtain the quadrature-axis current of the motor, the quadrature-axis current being the current value of the motor in a two-phase stationary coordinate system; performing Park transformation on the quadrature-axis current to obtain the direct-axis current of the motor, the direct-axis current being the current value of the motor in a two-phase rotating coordinate system; and calculating the motor feedback current of the motor based on the direct-axis current and the quadrature-axis current.

[0041] The Clark transform is used to transform the three-phase coordinate system into a two-phase stationary coordinate system, and the Prak transform is used to transform the two-phase stationary coordinate system into a two-phase rotating coordinate system. In this example, the motor feedback current can be obtained by calculating the square sum of the direct axis current and the quadrature axis current. It can be understood that the process of obtaining the motor feedback current before using the target estimation model for temperature estimation is the same as the above process.

[0042] It can be seen that in this example, the motor feedback current is obtained by performing Clarke transformation and Parker transformation on the collected three-phase current in sequence, and the calculation process is simple, does not require excessive computing resources, and reduces the demand for MCU memory. In a possible example, before obtaining the motor feedback current and the electronic control unit temperature of the motor, the method also includes: obtaining multiple test data sets of the motor under multiple working conditions, the test data sets including the motor feedback current, the electronic control unit temperature and the temperature measured value of the target working device; constructing a data fitting model, the data fitting model is configured with an iterative initial temperature estimation parameter; screening the multiple working conditions according to the temperature measured value of the target working device to obtain multiple standard test conditions; using the motor feedback current and the electronic control unit temperature under the same standard test condition as input values ​​and the temperature measured value of the target working device as output values, and importing them into the data fitting model; setting an iteration target, calling the automatic fitting toolbox to perform data fitting operations according to the initial temperature estimation parameters and the iteration target, and obtaining the target temperature estimation parameters, and the target temperature estimation parameters are configured in the target temperature estimation model.

[0043] Wherein, the construction of the data fitting model can specifically be a temperature estimation model of the original state constructed by the R&D personnel in Simulink. The initial temperature estimation parameters can specifically be empirical values ​​obtained based on statistical analysis of historical data, and the historical data include temperature change data of other motors with the same specifications and models as the motor during actual operation. The standard test condition refers to a condition that may cause the motor to fail due to high temperature, so as to improve the accuracy of the model output. The setting of the iteration target, calling the automatic fitting toolbox to perform data fitting operations according to the initial temperature estimation parameters and the iteration target, and obtaining the target temperature estimation parameters, can specifically be the following process: the R&D personnel import the input value and the output value into the data fitting model, and then set an iteration target between the fitting value and the actual value, and use Cftool to complete the automatic fitting of the data. The above-mentioned initial temperature estimation parameters will be continuously iterated and optimized. After the iteration, the data fitting model outputs the final result, that is, the target temperature estimation parameters.

[0044] It can be seen that in this example, by constructing a data fitting model, the test data of the motor under multiple different standard test conditions are input into the data fitting model, and the data fitting process is performed using the automatic fitting toolbox, and the temperature estimation parameters configured in the data fitting model are continuously iterated and optimized, and finally the target temperature estimation parameters corresponding to the permanent magnet synchronous motor are obtained. In this way, the temperature estimation parameters corresponding to the motor can be determined in advance and stored, so that the motor can directly call its corresponding temperature estimation parameters in actual operation, which improves the efficiency of temperature estimation.

[0045] In one possible example, the target working device includes a controller MOS tube, and the obtaining of multiple test data sets of the motor under multiple working conditions includes: performing the following operations for the multiple working conditions: obtaining the motor feedback current and the electronic control unit temperature of the motor under the current working condition; obtaining the first temperature measured value of the controller MOS tube under the current working condition through a second temperature sensor; determining the test data set of the motor under the current working condition according to the motor feedback current, the electronic control unit temperature and the first temperature measured value of the motor under the current working condition; repeating the above operations until all the multiple working conditions are processed, and obtaining multiple test data sets of the motor under multiple working conditions.

[0046] Among them, the current operating condition refers to the operating condition currently being processed or the operating condition currently being tested. In this example, different operating conditions specifically refer to the different motor feedback currents, electronic control unit temperatures, and controller MOS tube temperatures in different usage scenarios.

[0047] The second temperature sensor can be arranged near the controller MOS tube, such as near the drain and source of the MOS tube, or close to the gate, so as to obtain a more accurate measurement result. The second temperature sensor can be a thermocouple temperature sensor or an infrared temperature sensor, etc., which is not limited here.

[0048] Among them, the second temperature sensor is only used to assist the MCU in obtaining the actual temperature value of the controller MOS tube under multiple working conditions, so as to provide a data basis for determining the target temperature estimation parameters. Optionally, the motor including the second temperature sensor can be specifically designed as a test prototype for testing to obtain the temperature estimation parameters of the same motor, and the motor used for actual application may not include the hardware for measuring the first temperature actual value, which can reduce the production cost of the motor.

[0049] It can be seen that in this example, by testing the motor under different working conditions, the motor feedback current, ECU temperature, and the temperature measured values ​​of the controller MOS tube under different working conditions are obtained, which provides a data basis for data fitting to obtain the target temperature estimation parameters. The data fitting is performed using the actual temperature measured values, which improves the accuracy of temperature estimation.

[0050] In one possible example, the target working device includes a winding, and obtaining multiple test data sets of the motor under multiple operating conditions includes: performing the following operations for the multiple operating conditions: obtaining the motor feedback current and the electronic control unit temperature of the motor under the current operating condition; obtaining the second temperature measured value of the winding under the current operating condition through a winding temperature test interface; determining the test data set of the motor under the current operating condition based on the motor feedback current, the electronic control unit temperature and the second temperature measured value of the motor under the current operating condition; repeating the above operations until all the multiple operating conditions are processed, and obtaining multiple test data sets of the motor under multiple operating conditions.

[0051] Among them, the current operating condition refers to the operating condition currently being processed or the operating condition currently being tested. In this example, different operating conditions specifically refer to the different motor feedback currents, electronic control unit temperatures and winding temperatures of the motor in different usage scenarios.

[0052] Among them, the above-mentioned winding temperature test interface is only used to assist the MCU in obtaining the actual temperature values ​​of the motor winding under multiple working conditions, so as to provide a data basis for determining the target temperature estimation parameters. Optionally, the motor including the above-mentioned winding temperature test interface can be specifically designed as a test prototype for testing to obtain the temperature estimation parameters of the same motor, and the motor used for actual application may not include the above-mentioned hardware for measuring the second temperature actual value, which can reduce the production cost of the motor.

[0053] It can be seen that in this example, by testing the motor under different working conditions, the motor feedback current, ECU temperature, and winding temperature measured values ​​under different working conditions are obtained, which provides a data basis for data fitting to obtain the target temperature estimation parameters. The data fitting is performed using the temperature measured values, which improves the accuracy of temperature estimation.

[0054] In one possible example, the target working device includes a permanent magnet, and the obtaining of multiple test data sets of the motor under multiple operating conditions includes: performing the following operations for the multiple operating conditions: obtaining the motor feedback current and the electronic control unit temperature of the motor under the current operating condition; obtaining the third temperature measured value of the permanent magnet under the current operating condition through a permanent magnet temperature test interface; determining the test data set of the motor under the current operating condition according to the motor feedback current, the electronic control unit temperature and the third temperature measured value of the motor under the current operating condition; repeating the above operations until all the multiple operating conditions are processed, and obtaining multiple test data sets of the motor under multiple operating conditions.

[0055] Among them, the current operating condition refers to the operating condition currently being processed or the operating condition currently being tested. In this example, different operating conditions specifically refer to the different motor feedback currents, electronic control unit temperatures, and permanent magnet temperatures of the motor in different usage scenarios.

[0056] Among them, the above-mentioned permanent magnet temperature test interface is only used to assist the MCU in obtaining the actual temperature values ​​of the motor permanent magnet under multiple working conditions, so as to provide a data basis for determining the target temperature estimation parameters. Optionally, the motor including the above-mentioned permanent magnet temperature test interface can be specifically designed as a test prototype for testing to obtain the temperature estimation parameters of the same motor, and the motor used for actual application may not include the above-mentioned hardware for measuring the actual temperature value of the third temperature, which can reduce the production cost of the motor.

[0057] It can be seen that in this example, by testing the motor under different working conditions, the motor feedback current, ECU temperature, and actual measured temperature of the permanent magnet under different working conditions are obtained, which provides a data basis for data fitting to obtain the target temperature estimation parameters. Data fitting is performed using the actual measured temperature values, which improves the accuracy of temperature estimation.

[0058] In one possible example, the multiple operating conditions are screened according to the measured temperature value of the target working device to obtain multiple standard test conditions, including: performing the following operations on the multiple operating conditions: determining whether the measured temperature value of the target working device under the current operating condition satisfies the following conditions: the first measured temperature value is greater than or equal to the first preset temperature threshold; and / or the second measured temperature value is greater than or equal to the second preset temperature threshold; and / or the third measured temperature value is greater than or equal to the third preset temperature threshold; if satisfied, determining that the current operating condition is the standard test condition; if not satisfied, determining that the current operating condition is not the standard test condition; repeating the above operations until all the multiple operating conditions are processed to obtain the multiple standard test conditions.

[0059] Among them, the first preset temperature threshold refers to the alarm temperature threshold corresponding to the MOS tube of the controller of the motor, that is, during the operation of the motor, when the temperature of the MOS tube exceeds the first preset temperature threshold, an alarm reminder is output, and the first preset temperature threshold can be set by the specific hardware parameters of the selected MOS tube. The second preset temperature threshold refers to the alarm temperature threshold corresponding to the winding, that is, during the operation of the motor, when the temperature of the winding exceeds the second preset temperature threshold, an alarm reminder is output. The third preset temperature threshold refers to the alarm temperature threshold corresponding to the permanent magnet, that is, during the operation of the motor, when the temperature of the permanent magnet exceeds the third preset temperature threshold, an alarm reminder is output. Among them, the second and third preset temperature thresholds can be specifically obtained from the interface of the motor manufacturer. Optionally, the R&D personnel can also adjust the preset temperature threshold based on different test scenarios or working conditions and input the adjusted temperature threshold into the controller.

[0060] Among them, the standard test condition refers to a condition that is prone to cause the target working device to fail due to high temperature. It is understandable that when the motor is actually running, any excessive temperature of the controller MOS tube, winding and permanent magnet may cause different types of failures in the motor, that is, when any temperature of the three target working devices exceeds the preset temperature threshold, an alarm reminder needs to be output. Therefore, in this example, when at least one of the three measured temperature values ​​is greater than the preset temperature threshold corresponding to the target working device, the condition is determined to be a high-temperature usage scenario that is prone to failure, that is, the condition is determined to be a standard test condition.

[0061] It can be seen that in this example, whether the current operating condition is a standard test condition is determined by detecting whether at least one of the three measured temperature values ​​is greater than the corresponding preset temperature threshold, so as to simulate the actual high-temperature usage scenario and improve the accuracy of the temperature estimation results.

[0062] In one possible example, the target temperature estimation parameters include a filter coefficient corresponding to a target working device, a temperature compensation factor corresponding to the target working device, a temperature coefficient corresponding to the target working device and a power coefficient of the motor, and the target temperature estimation model includes a filter model and a temperature prediction model, and the motor feedback current and the electronic control unit temperature are input into the target temperature estimation model to obtain target temperatures of multiple target working devices, including: inputting the motor feedback current of the motor, the power coefficient of the motor and the filter coefficient corresponding to the target working device into the filter model to obtain a filter output result corresponding to the target working device; determining the temperature compensation coefficient corresponding to the target working device according to the filter output result corresponding to the target working device and the temperature compensation factor corresponding to the target working device; inputting the electronic control unit temperature of the motor, the temperature compensation coefficient corresponding to the target working device and the temperature coefficient corresponding to the target working device into the temperature prediction model to obtain target temperatures of multiple target working devices.

[0063] Among them, the target temperature estimation parameters can be divided into parameters of the motor and parameters of the target working device. In this example, the parameters of the motor are, for example, the power coefficient of the motor, and the parameters of the target working device are, for example, the filter coefficient, temperature compensation factor, temperature coefficient, and temperature compensation coefficient. Different target working devices correspond to different parameters. For example, the temperature coefficients of the controller MOS tube and winding of the same motor are different, which is caused by the different materials of the controller MOS tube and winding.

[0064] Optionally, the filtering model includes but is not limited to low-pass, IIR, Kalman and other filtering algorithms, and the filtering algorithm with the filtering effect that best meets the actual working conditions can be selected to filter out the interference of clutter.

[0065] Optionally, the filtering model adopts a first-order low-pass filtering algorithm, and the calculation formula is:

[0066] y(n)=α·x(n)+(1-α)·y(n-1)

[0067] In the formula, α is the filter coefficient corresponding to the target working device, x(n) is the sampled value of this filtering, y(n-1) is the output value of the previous filtering, and y(n) is the output value of this filtering. In this example, the sampled values ​​of the filtering include the motor feedback current and the motor power coefficient, and the output value of the filtering is the filter output result corresponding to the target working device.

[0068] After filtering, the filter output result corresponding to the target working device is obtained, and then the temperature compensation coefficient corresponding to the target working device is calculated in combination with the temperature compensation factor corresponding to the target working device. Optionally, the temperature compensation coefficient can be the product of the filter output result and the temperature compensation factor. Finally, the ECU temperature, the temperature compensation coefficient corresponding to the target working device, and the temperature coefficient corresponding to the target working device are input into the temperature prediction model to calculate the target temperature of the target working device. Among them, there can be multiple temperature coefficients corresponding to the target working device. For example, in an embodiment of the present application, the temperature coefficient may include a first temperature coefficient and a second temperature coefficient.

[0069] It can be understood that the target temperature estimation model may include three target temperature estimation sub-models, namely, a controller MOS tube temperature estimation model, a winding temperature estimation model and a permanent magnet temperature estimation model, wherein each target temperature estimation sub-model includes a filtering model and a temperature prediction model, for example, the controller MOS tube temperature estimation model includes a controller MOS tube filtering model and a controller MOS tube temperature prediction model.

[0070] It can be seen that in this example, the target temperature estimation parameters are input into the target temperature estimation model, and then filtered and temperature predicted in turn to output the temperature value of each target working device. The whole process is simple and efficient, saving computing resources and reducing the memory requirements for the MCU.

[0071] In a possible example, the temperature prediction model includes a lead-lag control algorithm, and the calculation formula of the lead-lag control algorithm is:

[0072]

[0073] In the formula, A1 is the first temperature coefficient corresponding to the target working device, B1 is the second temperature coefficient corresponding to the target working device, Ts is the temperature compensation coefficient corresponding to the target working device, x(n) is the electronic control unit temperature of the motor, and Y(n) is the target temperature of the target working device.

[0074] Among them, the lead-lag control algorithm combines the lead correction algorithm and the lag correction algorithm. Lead correction means that when the zero frequency \omega_z of the controller is less than the pole frequency \omega_p, that is, \omega_z<\omega_p, this means that the phase angle is positive and the response speed is faster, but the steady-state accuracy will be reduced; lag correction means that when the zero frequency omega_z of the controller is greater than the pole frequency \omega_p, that is, \omega_z>\omega_p, this is usually used to reduce the oscillation of the system and improve stability, but it will reduce the response speed. The lead-lag control algorithm involved in this example combines the two and has the advantages of both algorithms, which can not only speed up the response speed but also improve stability.

[0075] It can be seen that in this example, the lead-lag control algorithm is used in the temperature prediction model, which not only speeds up the system response speed, but also improves the stability of the system and further improves the efficiency of temperature estimation.

[0076] In one possible example, after obtaining the target temperature of the target working device, the method further includes: if it is detected that the target temperature of the target working device is greater than the preset temperature threshold corresponding to the target working device, the output torque of the motor is limited. Specifically, the controller MOS tube temperature, winding temperature and permanent magnet temperature can be used as input data, and the output torque value to be limited can be used as the output value to build and train the torque limitation model, and finally the torque limitation model is integrated with the target temperature estimation model to form an overall temperature estimation control model. In this way, the permanent magnet synchronous motor can automatically perform temperature self-detection and control, which improves safety, versatility and intelligence.

[0077] The temperature estimation control model involved in the embodiments of the present application is introduced below with reference to the accompanying drawings.

[0078] See also Figure 3 , Figure 3 is an example diagram of a temperature estimation control model provided in an embodiment of the present application. Figure 3As shown, the temperature estimation control model 30 includes a MOS filter model 31, a MOS temperature prediction model 32, a winding filter model 33, a winding temperature prediction model 34, a permanent magnet filter model 35, a permanent magnet temperature prediction model 36 and a torque limit model 37. Among them, the input data of the temperature estimation control model 30 are the motor feedback current and the temperature of the electronic control unit, and the output data is the output torque value that needs to be limited. Specifically, the input data of the MOS filter model 31 are the motor feedback current, the power coefficient and the MOS filter coefficient, and the output data is the filter output result corresponding to the MOS; the input data of the MOS temperature prediction model 32 are the MOS temperature coefficient 1, the MOS temperature compensation coefficient and the MOS temperature coefficient 2, and the output data is the MOS temperature; wherein the MOS temperature compensation coefficient is the product of the filter output result corresponding to the MOS and the MOS temperature compensation factor. Similarly, the input data of the winding filter model 33 are the motor feedback current, power coefficient and winding filter coefficient, and the output data are the filter output results corresponding to the winding; the input data of the winding temperature prediction model 34 are the winding temperature coefficient 1, the winding temperature compensation coefficient and the winding temperature coefficient 2, and the output data are the winding temperature; wherein the winding temperature compensation coefficient is the product of the filter output result corresponding to the winding and the winding temperature compensation factor. Similarly, the input data of the permanent magnet filter model 35 are the motor feedback current, power coefficient and permanent magnet filter coefficient, and the output data are the filter output results corresponding to the permanent magnet; the input data of the permanent magnet temperature prediction model 36 are the permanent magnet temperature coefficient 1, the permanent magnet temperature compensation coefficient and the permanent magnet temperature coefficient 2, and the output data are the permanent magnet temperature; wherein the permanent magnet temperature compensation coefficient is the product of the filter output result corresponding to the permanent magnet and the permanent magnet temperature compensation factor. And, the input data of the torque limit model 37 are the MOS temperature, the winding temperature and the permanent magnet temperature, and the output data are the output torque value to be limited. It can be understood that when the temperatures of the three target working components are all lower than the threshold values, the output data of the torque limiting model 37 indicates that the output torque does not need to be limited.

[0079] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0080] An embodiment of the present application further provides a vehicle, which includes the electronic device 10 described in the above embodiment.

[0081] An embodiment of the present application also provides a computer storage medium, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, part or all of the steps of any method recorded in the above method embodiments are implemented.

[0082] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method recorded in the above method embodiments.

[0083] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0084] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A temperature estimation method, characterized in that: include: Get the motor feedback current and ECU temperature of the motor; The motor feedback current and the electronic control unit temperature are input into a target temperature estimation model to obtain target temperatures of multiple target working components.

2. The method according to claim 1, characterized in that: The obtaining of the motor feedback current and the temperature of the electronic control unit of the motor comprises: Obtaining the three-phase current of the motor; Obtaining a motor feedback current of the motor according to the three-phase current calculation; The temperature of the electronic control unit of the motor is obtained through a first temperature sensor.

3. The method according to claim 2, characterized in that: The step of calculating the motor feedback current of the motor according to the three-phase current comprises: Performing Clarke transformation on the three-phase current to obtain the quadrature-axis current of the motor, wherein the quadrature-axis current is the current value of the motor in a two-phase stationary coordinate system; Performing a Park transformation on the quadrature-axis current to obtain a direct-axis current of the motor, wherein the direct-axis current is a current value of the motor in a two-phase rotating coordinate system; The motor feedback current of the motor is calculated according to the direct-axis current and the quadrature-axis current.

4. The method according to any one of claims 1 to 3, characterized in that: Before obtaining the motor feedback current and the electronic control unit temperature of the motor, the method further includes: Acquire a plurality of test data sets of the motor under a plurality of working conditions, the test data sets including the motor feedback current, the electronic control unit temperature and the temperature measured value of the target working device; Constructing a data fitting model, wherein the data fitting model is configured with iterable initial temperature estimation parameters; Screening the multiple working conditions according to the measured temperature value of the target working device to obtain multiple standard test working conditions; The motor feedback current and the electronic control unit temperature under the same standard test condition are used as input values, and the actual temperature measurement value of the target working device is used as output value, and are imported into the data fitting model; An iteration target is set, and an automatic fitting toolbox is called to perform a data fitting operation according to the initial temperature estimation parameters and the iteration target to obtain target temperature estimation parameters, and the target temperature estimation parameters are configured in the target temperature estimation model.

5. The method according to claim 4, characterized in that: The target working device includes a controller MOS tube, and the obtaining of multiple test data sets of the motor under multiple working conditions includes: The following operations are performed for the multiple working conditions: Obtaining a motor feedback current and an electronic control unit temperature of the motor under a current working condition; Acquire a first temperature measured value of the controller MOS tube under the current working condition through a second temperature sensor; Determine a test data set of the motor under the current working condition according to the motor feedback current, the electronic control unit temperature and the first temperature measured value of the motor under the current working condition; The above operations are repeatedly performed until the multiple operating conditions are all processed, thereby obtaining multiple test data sets of the motor under multiple operating conditions.

6. The method according to claim 4, characterized in that: The target working device includes a winding, and the step of obtaining a plurality of test data sets of the motor under a plurality of working conditions includes: The following operations are performed for the multiple working conditions: Obtaining a motor feedback current and an electronic control unit temperature of the motor under a current working condition; Obtaining a second temperature measured value of the winding under the current working condition through a winding temperature test interface; Determine a test data set of the motor under the current working condition according to the motor feedback current, the electronic control unit temperature and the second temperature measured value of the motor under the current working condition; The above operations are repeatedly performed until the multiple operating conditions are all processed, thereby obtaining multiple test data sets of the motor under multiple operating conditions.

7. The method according to claim 4, characterized in that: The target working device includes a permanent magnet, and the step of obtaining a plurality of test data sets of the motor under a plurality of working conditions includes: The following operations are performed for the multiple working conditions: Obtaining a motor feedback current and an electronic control unit temperature of the motor under a current working condition; Obtaining a third temperature measured value of the permanent magnet under the current working condition through a permanent magnet temperature test interface; Determine a test data set of the motor under the current working condition according to the motor feedback current, the electronic control unit temperature and the third temperature measured value of the motor under the current working condition; The above operations are repeatedly performed until the multiple operating conditions are all processed, thereby obtaining multiple test data sets of the motor under multiple operating conditions.

8. The method according to any one of claims 5 to 7, characterized in that: The multiple working conditions are screened according to the measured temperature value of the target working device to obtain multiple standard test working conditions, including: The following operations are performed for the multiple working conditions: Determine whether the temperature measured value of the target working device under the current working condition meets the following conditions: the first temperature measured value is greater than or equal to the first preset temperature threshold; and / or the second temperature measured value is greater than or equal to the second preset temperature threshold; and / or the third temperature measured value is greater than or equal to the third preset temperature threshold; If satisfied, determining that the current operating condition is the standard test operating condition; If not, then it is confirmed that the current operating condition is not the standard test operating condition; The above operations are repeatedly performed until all the multiple working conditions are processed to obtain the multiple standard test working conditions.

9. The method according to claim 8, characterized in that: The target temperature estimation parameters include a filter coefficient corresponding to a target working device, a temperature compensation factor corresponding to a target working device, a temperature coefficient corresponding to a target working device, and a power coefficient of the motor. The target temperature estimation model includes a filter model and a temperature prediction model. The motor feedback current and the electronic control unit temperature are input into the target temperature estimation model to obtain target temperatures of multiple target working devices, including: Inputting the motor feedback current of the motor, the power coefficient of the motor and the filter coefficient corresponding to the target working device into the filter model to obtain the filter output result corresponding to the target working device; Determine a temperature compensation coefficient corresponding to the target working device according to a filter output result corresponding to the target working device and a temperature compensation factor corresponding to the target working device; The temperature of the electronic control unit of the motor, the temperature compensation coefficient corresponding to the target working device and the temperature coefficient corresponding to the target working device are input into the temperature prediction model to obtain the target temperatures of multiple target working devices.

10. The method according to claim 9, characterized in that: The step of determining the temperature compensation coefficient corresponding to the target working device according to the filtering output result corresponding to the target working device and the temperature compensation factor corresponding to the target working device includes: The temperature compensation coefficient corresponding to the target working device is obtained by multiplying the filtering output result by the temperature compensation factor corresponding to the target working device.

11. The method according to claim 10, characterized in that: The temperature prediction model includes a lead-lag control algorithm, and the calculation formula of the lead-lag control algorithm is: In the formula, A1 is the first temperature coefficient corresponding to the target working device, B1 is the second temperature coefficient corresponding to the target working device, Ts is the temperature compensation coefficient corresponding to the target working device, X(n) is the electronic control unit temperature of the motor, and Y(n) is the target temperature of the target working device.

12. An electronic device, characterized in that: The method comprises a processor, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 11.

13. A vehicle, characterized in that: Comprising the electronic device as claimed in claim 12.

14. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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