Temperature estimation method and related apparatus

Abstract

The application provides a temperature estimation method and related device, the method comprises: obtaining motor feedback current and electronic control unit temperature of a motor; inputting the motor feedback current and the electronic control unit temperature into a target temperature estimation model to obtain target temperatures of a plurality of target working devices, and the target temperature estimation model is configured with target temperature estimation parameters associated with the motor. In this way, the temperature of the plurality of target working devices can be estimated only by the easily obtained motor feedback current and electronic control unit temperature, without additional hardware devices and computing resources, thereby reducing the cost of temperature estimation of the target working devices in the motor, greatly reducing the memory requirement of the MCU, and simplifying the algorithm model of temperature estimation.

Description

Technical Field

[0001] This application relates to the field of automotive technology, specifically to a temperature estimation method and related apparatus. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in electric vehicles due to their high energy conversion efficiency and high power density. During motor operation, the temperature of its operating components is a key factor reflecting the normal operation of the motor and its controller. A common existing approach is to build different thermal models for different operating components in the motor to estimate their temperatures. However, this temperature estimation method requires building a corresponding thermal model for each operating component, and each thermal model involves measuring and calculating sample data that is difficult to obtain, requiring significant time and computational resources. This demand for large computational resources places a high demand on the memory of the MCU (Microcontroller Unit). Summary of the Invention

[0003] This application provides a temperature estimation method and related apparatus to reduce the computational and time costs of motor temperature estimation and simplify the algorithm model for temperature estimation.

[0004] In a first aspect, embodiments of this application provide a temperature estimation method, including:

[0005] Obtain the motor feedback current and electronic control unit temperature of the motor;

[0006] 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 devices. The target temperature estimation model is configured with target temperature estimation parameters associated with the motor.

[0007] Secondly, embodiments of this application provide an electronic device including a processor, a memory, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps in the first aspect of embodiments of this application.

[0008] Thirdly, embodiments of this application provide a vehicle that includes the electronic equipment described in the second aspect of embodiments of this application.

[0009] Fourthly, embodiments of this application provide 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 embodiments of this application.

[0010] As can be seen, in this embodiment, 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. Thus, compared to the prior art, which requires a significant amount of time and computational resources to measure and calculate the sample data needed to establish different thermal models, and to establish different thermal models for different working devices in the motor to achieve temperature estimation, this application can achieve temperature estimation of multiple target working devices using only the easily obtainable motor feedback current and electronic control unit temperature, without consuming a large amount of time and computational resources. Since it does not require a large amount of computational resources, the memory requirements of the MCU are greatly reduced, and the cost of temperature estimation of target working devices in the motor is also reduced, simplifying the temperature estimation algorithm model. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0013] Figure 2 This is a schematic flowchart of a temperature estimation method provided in an embodiment of this application;

[0014] Figure 3 This is a simplified example diagram of a temperature estimation control model provided in an embodiment of this application. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0016] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0017] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0018] The following describes the relevant terminology used in the embodiments of this application.

[0019] Electronic Control Unit (ECU): This is the hardware foundation for implementing the control algorithm of the permanent magnet synchronous motor, responsible for achieving precise control of the motor. The ECU includes a microcontroller unit (MCU) and peripheral circuitry; in this embodiment, the ECU can also be referred to as a controller.

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

[0021] Stator windings: Also known as stator windings, the stator is the stationary part of a permanent magnet synchronous motor, and the stator windings are a component of the stator. The windings generate a rotating magnetic field through which current flows, driving the motor. High temperatures accelerate the aging of the winding insulation material.

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

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

[0024] Cftool is a function fitting toolbox in MATLAB that has a rich set of fitting algorithms and can perform various linear and nonlinear function fitting.

[0025] Please see Figure 1 , Figure 1 This is a structural block diagram of an electronic device provided in an embodiment of this application. For example... 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, which may be configured to implement the methods described in the following embodiments when executed by one or more processors 11.

[0026] Processor 11 may include one or more processing cores. Processor 11 connects to various parts within the electronic device 10 using various interfaces and lines, and performs various functions and processes data of the electronic device 10 by running or executing instructions, programs, code sets, or instruction sets stored in memory 12, and by calling data stored in memory 12. Optionally, processor 11 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0027] The memory 12 may include random access memory (RAM) or read-only memory (ROM). The memory 12 can be used to store instructions, programs, code, 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 various method embodiments below, etc. The data storage area may also store data created by the electronic device 10 during use.

[0028] It is understood that in the embodiments of this 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] The following describes a temperature estimation method provided by an embodiment of this application.

[0030] Please see Figure 2 , Figure 2This is a schematic flowchart of a temperature estimation method provided in an embodiment of this application. Figure 2 As shown, the temperature estimation method includes:

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

[0032] The motor feedback current and the electronic control unit temperature are used to characterize the operating status of the permanent magnet synchronous motor. It should be noted that during actual operation, the motor feedback current and electronic control unit temperature of the permanent magnet synchronous motor can be acquired or calculated using relatively simple methods, requiring low hardware specifications and minimal computational resources.

[0033] S202, 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 devices.

[0034] The target temperature estimation model is configured with target temperature estimation parameters associated with the motor. These parameters are pre-determined parameters used to characterize the correlation between the target temperature of the target working device in the motor and the motor feedback current and electronic control unit temperature. In this embodiment, the temperature estimation parameters are related to the motor itself but not to the operating conditions; that is, the temperature estimation parameters for the same motor are the same under different operating conditions.

[0035] As can be seen, in this embodiment, 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. Thus, compared to the prior art, which requires a significant amount of time and computing resources to measure and calculate the sample data needed to establish different thermal models, and to establish different thermal models for different working devices in the motor to achieve temperature estimation, this application can achieve temperature estimation of multiple target working devices using only the easily obtainable motor feedback current and electronic control unit temperature, without consuming a large amount of time and computing resources. Since it does not require a large amount of computing resources, the memory requirements of the MCU are greatly reduced, and the cost of temperature estimation of target working devices in the motor is also reduced, simplifying the temperature estimation algorithm model.

[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] Three-phase current refers to the current form in a three-phase alternating current system, consisting of three sinusoidal currents with a phase difference of 120 degrees. It should be noted that in practical 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 can be positioned close to the MCU to obtain more accurate temperature measurement results. Specifically, the first temperature sensor can be a thermocouple temperature sensor or an infrared temperature sensor, etc., without being limited to a single one.

[0039] As can be seen, in this example, the motor feedback current of the motor can be calculated by easily obtaining the three-phase current, and the temperature of the motor's electronic control unit can be 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 temperature of multiple target working devices. The temperature estimation of multiple target working devices can be completed without consuming a lot of computing resources, which greatly reduces the memory requirements of the MCU and also reduces the cost of estimating the temperature of target working devices in the motor.

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

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

[0042] As can be seen, in this example, the motor feedback current is obtained by sequentially performing Clarke and Parker transforms on the collected three-phase currents. The calculation process is simple, does not require excessive computing resources, and reduces the demand on MCU memory. In a possible example, before obtaining the motor feedback current and electronic control unit temperature, the method further includes: acquiring multiple test data sets of the motor under multiple operating conditions, the test data sets including the motor feedback current, electronic control unit temperature, and the measured temperature value of the target working device; constructing a data fitting model, the data fitting model being configured with iterative initial temperature estimation parameters; filtering the multiple operating conditions based on the measured temperature value of the target working device to obtain multiple standard test operating conditions; importing the motor feedback current and electronic control unit temperature under the same standard test operating condition as input values ​​and the measured temperature value of the target working device as output values ​​into the data fitting model; setting an iteration target, calling the automatic fitting toolbox to perform a data fitting operation based on the initial temperature estimation parameters and the iteration target to obtain the target temperature estimation parameters, the target temperature estimation parameters being configured in the target temperature estimation model.

[0043] Specifically, the construction of the data fitting model can be a raw temperature estimation model built by researchers in Simulink. The initial temperature estimation parameters can be empirical values ​​obtained based on historical data statistical analysis, including temperature change data of other motors of the same specifications and model during actual operation. The standard test condition refers to a condition that may cause the motor to malfunction due to high temperature, in order to improve the accuracy of the model output. Setting the iteration target and calling the automatic fitting toolbox to perform data fitting operations based on the initial temperature estimation parameters and the iteration target to obtain the target temperature estimation parameters can be performed as follows: Researchers import the input and output values ​​into the data fitting model, then set an iteration target between the fitted value and the actual value, and use Cftool to complete the automatic data fitting. The initial temperature estimation parameters will be continuously iterated and optimized. After the iteration is completed, the data fitting model outputs the final result, which is the target temperature estimation parameter.

[0044] As can be seen in this example, by constructing a data fitting model, test data of the motor under multiple different standard test conditions are input into the data fitting model. The automatic fitting toolbox is used to execute the data fitting process, continuously iterating and optimizing the temperature estimation parameters configured in the data fitting model, and finally obtaining the target temperature estimation parameters corresponding to the permanent magnet synchronous motor. In this way, the temperature estimation parameters corresponding to the motor can be predetermined and stored, so that the motor can directly call its corresponding temperature estimation parameters during actual operation, improving the efficiency of temperature estimation.

[0045] In one possible example, the target working device includes a controller MOSFET. The step of acquiring multiple test data sets of the motor under multiple operating conditions includes: performing the following operations for the multiple operating conditions: acquiring the motor feedback current and electronic control unit temperature of the motor under the current operating condition; acquiring a first measured temperature value of the controller MOSFET under the current operating condition using a second temperature sensor; determining the test data set of the motor under the current operating condition based on the motor feedback current, electronic control unit temperature, and the first measured temperature value; repeating the above operations until all multiple operating conditions are processed, thereby obtaining multiple test data sets of the motor under multiple operating conditions.

[0046] The current operating condition refers to the current processing condition or the current testing condition. In this example, different operating conditions specifically refer to the different motor feedback current, electronic control unit temperature, and controller MOS transistor temperature under different usage scenarios.

[0047] The second temperature sensor can be positioned close to the controller's MOSFET, such as near the drain and source of the MOSFET, or close to the gate, to obtain more accurate measurement results. Specifically, the second temperature sensor can be a thermocouple temperature sensor or an infrared temperature sensor, etc., without limitation.

[0048] The second temperature sensor is used only to assist the MCU in acquiring the measured temperature values ​​of the controller's MOSFET under multiple operating conditions, providing a data basis for determining the target temperature estimation parameters. Optionally, the motor containing the second temperature sensor can be designed as a test prototype for obtaining the temperature estimation parameters of the same motor, while the motor used in actual applications may not include the hardware for measuring the first measured temperature value, thus reducing the motor's production cost.

[0049] As can be seen, in this example, by testing the motor under different operating conditions, the measured values ​​of the motor feedback current, ECU temperature, and controller MOSFET temperature under different operating conditions are obtained. This provides a data foundation for data fitting to obtain the target temperature estimation parameters. Using the measured temperature values ​​for data fitting improves the accuracy of temperature estimation.

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

[0051] The current operating condition refers to the current processing condition or the current testing condition. In this example, different operating conditions specifically refer to the different motor feedback current, electronic control unit temperature, and winding temperature under different usage scenarios.

[0052] The aforementioned winding temperature test interface is only used to assist the MCU in obtaining measured temperature values ​​of the motor windings under multiple operating conditions, providing a data basis for determining the target temperature estimation parameters. Optionally, the motor containing the aforementioned winding temperature test interface can be specifically designed as a test prototype for testing to obtain the same motor's temperature estimation parameters, while the motor used in actual applications may not include the aforementioned hardware for measuring the second measured temperature value, which can reduce the motor's production cost.

[0053] As can be seen, in this example, by testing the motor under different operating conditions, the measured values ​​of the motor feedback current, ECU temperature, and winding temperature under different operating conditions are obtained. This provides a data foundation for data fitting to obtain the target temperature estimation parameters. By using the measured temperature values ​​for data fitting, the accuracy of temperature estimation is improved.

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

[0055] The current operating condition refers to the current processing condition or the current testing condition. In this example, different operating conditions specifically refer to the different motor feedback current, electronic control unit temperature, and permanent magnet temperature under different usage scenarios.

[0056] The aforementioned permanent magnet temperature testing interface is only used to assist the MCU in obtaining measured temperature values ​​of the motor's permanent magnet under multiple operating conditions, providing a data basis for determining the target temperature estimation parameters. Optionally, the motor containing the aforementioned permanent magnet temperature testing interface can be specifically designed as a test prototype for testing to obtain the same motor's temperature estimation parameters, while the motor used in actual applications may not include the aforementioned hardware for measuring the third temperature measurement value, thus reducing the motor's production cost.

[0057] As can be seen, in this example, by testing the motor under different operating conditions, the measured values ​​of the motor feedback current, ECU temperature, and permanent magnet temperature under different operating conditions are obtained. This provides a data foundation for data fitting to obtain the target temperature estimation parameters. By using the measured temperature values ​​for data fitting, the accuracy of temperature estimation is improved.

[0058] In one possible example, the step of filtering the multiple operating conditions based on the measured temperature values ​​of the target working device to obtain multiple standard test operating conditions includes: 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 meets the following conditions: a first measured temperature value is greater than or equal to a first preset temperature threshold; and / or, a second measured temperature value is greater than or equal to a second preset temperature threshold; and / or, a third measured temperature value is greater than or equal to a third preset temperature threshold; if the conditions are met, then the current operating condition is determined to be the standard test operating condition; if the conditions are not met, then the current operating condition is indeed not the standard test operating condition; repeating the above operations until all the multiple operating conditions are processed to obtain the multiple standard test operating conditions.

[0059] The first preset temperature threshold refers to the alarm temperature threshold corresponding to the MOSFET of the motor controller. That is, during motor operation, when the MOSFET temperature exceeds the first preset temperature threshold, an alarm is output. The first preset temperature threshold can be set by the specific hardware parameters of the selected MOSFET. The second preset temperature threshold refers to the alarm temperature threshold corresponding to the winding. That is, during motor operation, when the winding temperature exceeds the second preset temperature threshold, an alarm is output. The third preset temperature threshold refers to the alarm temperature threshold corresponding to the permanent magnet. That is, during motor operation, when the permanent magnet temperature exceeds the third preset temperature threshold, an alarm is output. The second and third preset temperature thresholds can be obtained from the motor manufacturer's interface. Optionally, researchers can also adjust the preset temperature thresholds based on different test scenarios or operating conditions and input the adjusted temperature thresholds into the controller.

[0060] The standard test condition refers to a condition that is prone to causing the target working components to malfunction due to high temperatures. It is understood that during actual operation, excessively high temperatures in any of the controller MOSFETs, windings, or permanent magnets can lead to different types of motor malfunctions. Specifically, if the temperature of any one of the three target working components exceeds a preset temperature threshold, an alarm should be triggered. Therefore, in this example, when at least one of the three measured temperature values ​​exceeds the preset temperature threshold corresponding to the target working component, this condition is determined to be a high-temperature usage scenario prone to failure, and thus, it is defined as the standard test condition.

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

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

[0063] The target temperature estimation parameters can be divided into motor parameters and target device parameters. In this example, the motor parameters include, for example, the motor power coefficient, and the target device parameters include, for example, the filter coefficient, temperature compensation factor, temperature coefficient, and temperature compensation coefficient. Different target devices correspond to different parameters. For example, the temperature coefficients of the controller MOSFET and windings of the same motor may differ due to the different materials used in the controller MOSFET and windings.

[0064] Optionally, the filtering model may include, but is not limited to, low-pass, IIR, Kalman, and other filtering algorithms. The filtering algorithm that best matches the actual working conditions should be selected, with the aim of filtering out noise interference.

[0065] Optionally, the filtering model employs a first-order low-pass filtering algorithm, calculated as follows:

[0066] y(n)=α·x(n)+(1-α)·y(n-1)

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

[0068] After filtering, the filtered output result corresponding to the target working device is obtained. Then, the temperature compensation coefficient corresponding to the target working device is calculated by combining the temperature compensation factor corresponding to the target working device. Optionally, the temperature compensation coefficient can be the product of the filtered 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. There can be multiple temperature coefficients corresponding to the target working device; for example, in this embodiment, the temperature coefficients may include a first temperature coefficient and a second temperature coefficient.

[0069] It is understood that the target temperature estimation model may include three target temperature estimation sub-models, namely the controller MOSFET temperature estimation model, the winding temperature estimation model, and the permanent magnet temperature estimation model. Each target temperature estimation sub-model includes a filtering model and a temperature prediction model. For example, the controller MOSFET temperature estimation model includes a controller MOSFET filtering model and a controller MOSFET temperature prediction model.

[0070] As can be seen, in this example, the target temperature estimation parameters are input into the target temperature estimation model, and after filtering and temperature prediction, the temperature values ​​of each target working device are output. The whole process is simple and efficient, saves computing resources, and reduces the memory requirements of the MCU.

[0071] In one possible example, the temperature prediction model includes a lead-lag control algorithm, the calculation formula of which 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 temperature of the electronic control unit of the motor, and Y(n) is the target temperature of the target working device.

[0074] The lead-lag control algorithm combines lead compensation and lag compensation algorithms. Lead compensation refers to the controller's zero-frequency ωz being less than its pole-frequency ωp (i.e., ωz < ωp), which means a positive phase angle and faster response, but reduces steady-state accuracy. Lag compensation refers to the controller's zero-frequency ωz being greater than its pole-frequency ωp (i.e., ωz > ωp), which is typically used to reduce system oscillations and improve stability, but reduces response speed. The lead-lag control algorithm in this example combines both algorithms, possessing the advantages of both, achieving both faster response and improved stability.

[0075] As can be seen, in this example, the temperature prediction model uses a lead-lag control algorithm, which not only speeds up the system response but also improves the system stability and further enhances the temperature estimation efficiency.

[0076] In one possible example, after obtaining the target temperature of the target working device, the method further includes: if the target temperature of the target working device is detected to be greater than a preset temperature threshold corresponding to the target working device, then limiting the output torque of the motor. Specifically, the controller MOSFET 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 construct and train a torque limiting model. Finally, the torque limiting model is fused 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-checking and control, improving safety, versatility, and intelligence.

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

[0078] Please see Figure 3 , Figure 3 This is a simplified example diagram of a temperature estimation control model provided in an embodiment of this 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 limiting model 37. The input data for the temperature estimation control model 30 are the motor feedback current and the electronic control unit temperature, and the output data is the output torque value to be limited. Specifically, the input data for the MOS filter model 31 are the motor feedback current, power coefficient, and MOS filter coefficient, and the output data is the corresponding MOS filter output result; the input data for the MOS temperature prediction model 32 are MOS temperature coefficient 1, MOS temperature compensation coefficient, and MOS temperature coefficient 2, and the output data is the MOS temperature; wherein, the MOS temperature compensation coefficient is the product of the corresponding MOS filter output result and the MOS temperature compensation factor. Similarly, the input data for the winding filtering model 33 are the motor feedback current, power coefficient, and winding filtering coefficient, and the output data is the filtering output result corresponding to the winding; the input data for the winding temperature prediction model 34 are the winding temperature coefficient 1, winding temperature compensation coefficient, and winding temperature coefficient 2, and the output data is the winding temperature; wherein, the winding temperature compensation coefficient is the product of the filtering output result corresponding to the winding and the winding temperature compensation factor. Similarly, the input data for the permanent magnet filtering model 35 are the motor feedback current, power coefficient, and permanent magnet filtering coefficient, and the output data is the filtering output result corresponding to the permanent magnet; the input data for the permanent magnet temperature prediction model 36 are the permanent magnet temperature coefficient 1, permanent magnet temperature compensation coefficient, and permanent magnet temperature coefficient 2, and the output data is the permanent magnet temperature; wherein, the permanent magnet temperature compensation coefficient is the product of the filtering output result corresponding to the permanent magnet and the permanent magnet temperature compensation factor. And, the input data for the torque limiting model 37 are the MOS temperature, winding temperature, and permanent magnet temperature, and the output data is the output torque value to be limited. It is understandable that when the temperature of all three target working devices is less than the threshold, the output data of the torque limiting model 37 is 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 thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as 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, all or part of the processes or functions described in the embodiments of this application are generated. 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. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via 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 includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0080] This application also provides a vehicle, which includes the electronic device 10 described in the above embodiments.

[0081] This application also provides a computer storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements some or all of the steps of any of the methods described in the above method embodiments.

[0082] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0083] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply 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 this application.

[0084] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A temperature estimation method, characterized in that, include: Obtain the motor feedback current and electronic control unit temperature of the motor; 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 devices; The target temperature estimation model includes a filtering model and a temperature prediction model. The step of inputting the motor feedback current and the electronic control unit temperature into the target temperature estimation model yields the target temperatures of multiple target operating devices, including: The motor feedback current of the motor, the power coefficient of the motor, and the filter coefficient corresponding to the target working device are input into the filter model to obtain the filter output result corresponding to the target working device. The temperature compensation coefficient corresponding to the target working device is determined based on the filter output result corresponding to the target working device and the temperature compensation factor corresponding to the target working device. The temperature of the electronic control unit of the motor, the temperature compensation coefficient of the target working device, and the temperature coefficient of the target working device are input into the temperature prediction model to obtain the target temperature of multiple target working devices. The temperature prediction model includes a lead-lag control algorithm, the calculation formula of which is: In the formula, The first temperature coefficient corresponding to the target working device. The second temperature coefficient corresponding to the target working device. This represents the temperature compensation coefficient corresponding to the target working device. The temperature is the temperature of the motor's electronic control unit. The target temperature for the target working device.

2. The method according to claim 1, characterized in that, The acquisition of the motor feedback current and electronic control unit temperature includes: Obtain the three-phase current of the motor; The motor feedback current of the motor is calculated based on the three-phase current. The temperature of the motor's electronic control unit is obtained through a first temperature sensor.

3. The method according to claim 2, characterized in that, The calculation of the motor feedback current based on the three-phase current includes: The three-phase current is subjected to Clark transformation to obtain the quadrature-axis current of the motor, which is the current value of the motor in a two-phase stationary coordinate system. The quadrature-axis current is subjected to Parker transformation to obtain the direct-axis current of the motor, which is the current value of the motor in a two-phase rotating coordinate system. The motor feedback current of the motor is calculated based on the direct-axis current and the quadrature-axis current.

4. The method according to any one of claims 1-3, characterized in that, Before acquiring the motor feedback current and electronic control unit temperature, the method further includes: Acquire multiple test data sets of the motor under multiple operating conditions, the test data sets including motor feedback current, electronic control unit temperature and measured temperature values ​​of the target working device; Construct a data fitting model, which is configured with iterative initial temperature estimation parameters; Based on the measured temperature values ​​of the target working device, the multiple operating conditions are screened to obtain multiple standard test conditions; The motor feedback current and electronic control unit temperature under the same standard test conditions are used as input values, and the measured temperature of the target working device is used as the output value, which are then imported into the data fitting model. Set the iteration target, call the automatic fitting toolbox to perform data fitting operation based on the initial temperature estimation parameters and the iteration target, and obtain the target temperature estimation parameters. 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 MOSFET, and the acquisition of multiple test data sets of the motor under multiple operating conditions includes: Perform the following operations for the aforementioned multiple operating conditions: Obtain the motor feedback current and electronic control unit temperature of the motor under the current operating conditions; The first measured temperature value of the controller MOS transistor under the current operating conditions is obtained by the second temperature sensor. The test data set of the motor under the current operating condition is determined based on the motor feedback current, electronic control unit temperature and the measured value of the first temperature under the current operating condition. Repeat the above operations until all the multiple operating conditions have been processed, and obtain 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 acquisition of multiple test data sets of the motor under multiple operating conditions includes: Perform the following operations for the aforementioned multiple operating conditions: Obtain the motor feedback current and electronic control unit temperature of the motor under the current operating conditions; The second measured temperature value of the winding under the current operating conditions is obtained through the winding temperature test interface; The test data set of the motor under the current operating condition is determined based on the motor feedback current, electronic control unit temperature and the measured value of the second temperature under the current operating condition. Repeat the above operations until all the multiple operating conditions have been processed, and obtain 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 acquisition of multiple test data sets of the motor under multiple operating conditions includes: Perform the following operations for the aforementioned multiple operating conditions: Obtain the motor feedback current and electronic control unit temperature of the motor under the current operating conditions; The measured value of the third temperature of the permanent magnet under the current operating conditions is obtained through the permanent magnet temperature testing interface; The test data set of the motor under the current operating condition is determined based on the motor feedback current, electronic control unit temperature and the measured value of the third temperature under the current operating condition. Repeat the above operations until all the multiple operating conditions have been processed, and obtain multiple test data sets of the motor under multiple operating conditions.

8. The method according to any one of claims 5-7, characterized in that, The process involves filtering the multiple operating conditions based on the measured temperature values ​​of the target working device to obtain multiple standard test conditions, including: Perform the following operations for the aforementioned multiple operating conditions: Determine whether the measured temperature value of the target working device under the current operating condition meets 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 the conditions are met, then the current operating condition is determined to be the standard test operating condition; If not, then the current operating condition is indeed not the standard test condition. Repeat the above operations until all the multiple operating conditions have been processed to obtain the multiple standard test operating conditions.

9. The method according to claim 8, characterized in that, The step of determining the temperature compensation coefficient corresponding to the target working device based on the filter 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 device is obtained by multiplying the filtered output result by the temperature compensation factor corresponding to the target device.

10. An electronic device, characterized in that, It includes a processor, a memory, and one or more programs, said one or more programs being stored in the memory and configured to be executed by the processor, said programs including instructions for performing the steps in the method as claimed in any one of claims 1-9.

11. A vehicle, characterized in that, Including the electronic device as described in claim 10.

12. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-9.

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