Vehicle motor torque ripple suppression method and device

Abstract

The invention relates to the technical field of motors, in particular to a vehicle motor torque ripple suppression method and device.The method comprises the steps that the current torque, the current rotating speed and the current electrical angle of at least one motor of a vehicle are obtained, a relation table of the torque, the rotating speed and the electrical angle and iq compensation values is inquired, and a target iq compensation value for suppressing torque ripple is obtained, wherein the relation table is calculated by a pre-trained neural network model, and the output of the at least one motor is controlled according to the target iq compensation value, so that the actual rotating speed output by the at least one motor is consistent with the target rotating speed. According to the embodiment of the invention, the iq compensation value relation table of the vehicle motor can be obtained based on the neural network model, and the relation table is inquired according to the current torque, the current rotating speed and the current electrical angle so as to obtain the iq compensation value capable of suppressing the torque ripple, so that comprehensive suppression of the torque ripple is realized, the suppression precision of the vehicle motor to the torque ripple is improved, and the suppression efficiency of the vehicle motor is improved. And stability and practicability are realized.

Description

Technical Field

[0001] The present application relates to the field of motor technology, and in particular to a method and device for suppressing torque pulsation of a vehicle motor. Background Art

[0002] Driving permanent magnet synchronous motors has the advantages of high efficiency, high output torque, high power density and good dynamic performance. They are widely used in electric vehicle drive systems. However, the electromagnetic torque output by the motor pulsates, which will cause speed fluctuations, noise and mechanical vibrations of the motor.

[0003] In the related art, the inverter dead zone compensation method is mainly used to compensate the motor phase current to suppress the motor torque pulsation. For example, patent CN113676087A can compensate the quadrature axis current to be compensated according to the electrical angle, speed and current loop modulation frequency of the motor, thereby reducing the pulsation of the quadrature axis current when the load torque of the motor changes to suppress the motor torque pulsation.

[0004] However, in the related technology, the motor test bench will fluctuate at a constant speed, and the torque accuracy cannot be measured in real time, making it difficult to calibrate the iq compensation value. The inverter dead zone compensation method requires the motor load to be constant, and it is difficult to reasonably suppress the load torque and motor phase current when they change, which affects the suppression of motor torque pulsation when the load torque changes, narrows the application scope of motor torque pulsation suppression, and has insufficient practicality, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a method and device for suppressing torque pulsation of a vehicle motor, so as to solve the problems in the related art that the motor test bench will fluctuate at a constant speed, and the torque accuracy cannot be measured in real time, making it difficult to calibrate the current compensation value, and the inverter dead zone compensation method requires the motor load to be constant, which makes it difficult to reasonably suppress when the load torque and the motor phase current change, affecting the suppression of motor torque pulsation when the load torque changes, narrowing the application scope of motor torque pulsation suppression, and insufficient practicality.

[0006] The first aspect of the present application provides a method and device for suppressing torque pulsation of a vehicle motor, comprising the following steps: obtaining the current torque, current speed and current electrical angle of at least one motor of the vehicle; using the current torque, current speed and current electrical angle as indexes, querying a relationship table of torque, speed, electrical angle and IQ compensation value to obtain a target IQ compensation value for suppressing torque pulsation, wherein the relationship table is calculated by a pre-trained neural network model; controlling the output of the at least one motor according to the target IQ compensation value so that the actual speed output by the at least one motor is consistent with the target speed.

[0007] According to the above-mentioned technical means, the embodiment of the present application can obtain the IQ compensation value relationship table of the vehicle motor based on the neural network model, and query the relationship table according to the current torque, current speed and current electrical angle to obtain the IQ compensation value that can suppress torque pulsation, thereby achieving comprehensive suppression of torque pulsation and improving the vehicle motor's suppression accuracy of torque pulsation, which is more stable and practical.

[0008] Optionally, in one embodiment of the present application, before obtaining the target IQ compensation value for suppressing torque pulsation, it also includes: collecting a test data set of the at least one motor; inputting the test data set into a neural network model for training to generate the pre-trained neural network model that meets preset conditions; and using the pre-trained neural network model to generate a relationship table between the torque, speed, electrical angle and IQ compensation value.

[0009] According to the above-mentioned technical means, the embodiment of the present application can collect a test data set of at least one motor, input the test data set into a neural network model for training, generate a pre-trained neural network model that meets preset conditions, and then use the pre-trained neural network model to generate a relationship table between torque, speed, electrical angle and IQ compensation value to obtain the IQ compensation value that meets the actual operating state of the motor, thereby improving the accuracy of motor torque pulsation suppression.

[0010] Optionally, in one embodiment of the present application, the use of the pre-trained neural network model to generate the relationship table of the torque, speed, electrical angle and IQ compensation value includes: based on the pre-trained neural network model, traversing all the speeds, torques and electrical angles of the at least one motor, obtaining the corresponding IQ compensation value, and constructing the relationship table.

[0011] According to the above-mentioned technical means, the present application can traverse all the speeds, torques and electrical angles of at least one motor based on a pre-trained neural network model, obtain the corresponding IQ compensation values, and construct a relationship table, so as to obtain a relationship table database of torque, speed, electrical angle and IQ compensation value through pre-calculation of the neural network, so as to further speed up the calculation speed in the actual application process, thereby improving the efficiency of suppressing motor torque pulsation.

[0012] Optionally, in one embodiment of the present application, the preset condition is that the fluctuation value of the rotation speed is lower than a preset threshold.

[0013] According to the above-mentioned technical means, the preset condition of the embodiment of the present application is that the fluctuation value of the rotational speed is lower than the preset threshold value. By limiting the training results of the neural network model, it is ensured that the obtained pre-trained neural network model conforms to the actual application conditions of the motor, avoiding the existence of redundant data in the training data set, thereby simplifying the model training and making it more efficient, and making the model calculation results more accurate.

[0014] The second aspect of the present application provides a method and device for suppressing torque pulsation of a vehicle motor, including: an acquisition module, used to obtain the current torque, current speed and current electrical angle of at least one motor of the vehicle; a query module, used to query the relationship table of torque, speed, electrical angle and IQ compensation value with the current torque, current speed and current electrical angle as indexes, and obtain a target IQ compensation value for suppressing torque pulsation, wherein the relationship table is calculated by a pre-trained neural network model; a suppression module, used to control the output of the at least one motor according to the target IQ compensation value, so that the actual speed of the at least one motor output is consistent with the target speed.

[0015] Optionally, in one embodiment of the present application, the device also includes: an acquisition module, used to collect a test data set of the at least one motor before obtaining the target IQ compensation value for suppressing pulsation; a generation module, used to input the test data set into a neural network model for training, and generate the pre-trained neural network model that meets preset conditions; a tabulation module, used to use the pre-trained neural network model to generate a relationship table of the speed, torque, electrical angle and IQ compensation value.

[0016] Optionally, in one embodiment of the present application, the tabulation module includes: a construction unit, which is used to traverse all speeds, torques and electrical angles of the at least one motor based on the pre-trained neural network model, obtain corresponding IQ compensation values, and construct the relationship table.

[0017] Optionally, in one embodiment of the present application, the preset condition is that the fluctuation value of the rotation speed is lower than a preset threshold.

[0018] A third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method and device for suppressing vehicle motor torque pulsation as described in the above embodiments.

[0019] A fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the above-mentioned method and device for suppressing vehicle motor torque pulsation.

[0020] Beneficial effects of this application:

[0021] (1) The embodiment of the present application can obtain the IQ compensation value relationship table of the vehicle motor based on the neural network model, and query the relationship table according to the current torque, current speed and current electrical angle to obtain the IQ compensation value that can suppress the torque pulsation, thereby achieving comprehensive suppression of the torque pulsation and improving the suppression accuracy of the vehicle motor on the torque pulsation, which is more stable and practical.

[0022] (2) The embodiment of the present application can collect a test data set of at least one motor, input the test data set into a neural network model for training, generate a pre-trained neural network model that meets preset conditions, and then use the pre-trained neural network model to generate a relationship table between torque, speed, electrical angle and IQ compensation value to obtain an IQ compensation value that meets the actual operating state of the motor, thereby improving the accuracy of motor torque pulsation suppression.

[0023] (3) The present application can be based on a pre-trained neural network model, traverse all the speeds, torques and electrical angles of at least one motor, obtain the corresponding IQ compensation values, and construct a relationship table, so as to obtain a relationship table database of torque, speed, electrical angle and IQ compensation values ​​through pre-calculation of the neural network, so as to further speed up the calculation speed in the actual application process, thereby improving the efficiency of suppressing motor torque pulsation.

[0024] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A flow chart of a method and device for suppressing torque pulsation of a vehicle motor provided according to an embodiment of the present application;

[0027] Figure 2 A schematic diagram of a neural network structure for controlling motor torque pulsation suppression according to an embodiment of the present application;

[0028] Figure 3 A schematic diagram of actual motor speed fluctuations at different electrical angles under constant command torque and speed according to an embodiment of the present application;

[0029] Figure 4 A flowchart of a neural network model training for motor torque pulsation suppression according to an embodiment of the present application;

[0030] Figure 5 Schematic diagram of the structure of a method and device for suppressing torque pulsation of a vehicle motor according to an embodiment of the present application;

[0031] Figure 6 It is a schematic diagram of the structure of a vehicle according to an embodiment of the present application.

[0032] Among them, 10-a method and device for suppressing torque pulsation of a vehicle motor; 100-an acquisition module, 200-a query module and 300-a suppression module; 601-a memory, 602-a processor and 603-a communication interface. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0034] The following describes the method and device for suppressing the torque pulsation of the vehicle motor of the embodiment of the present application with reference to the accompanying drawings. In view of the fact that the motor test bench in the related technology mentioned in the above background technology will fluctuate at a constant speed, and the torque accuracy cannot be measured in real time, making it difficult to calibrate the current compensation value, and the inverter dead zone compensation method requires the motor load to be constant, and it is difficult to reasonably suppress when the load torque and the motor phase current change, which affects the suppression of the motor torque pulsation under the condition of load torque changes, narrows the application scope of the motor torque pulsation suppression, and has insufficient practicality. The present application provides a method and device for suppressing the torque pulsation of the vehicle motor, which can obtain the iq compensation value relationship table of the vehicle motor based on the neural network model, and query the relationship table according to the current torque, current speed and current electrical angle to obtain the iq compensation value that can suppress the torque pulsation, thereby achieving comprehensive suppression of the torque pulsation, and improving the suppression accuracy of the vehicle motor to the torque pulsation, which is more stable and practical. Thereby, the problems in the related technology that the motor test bench will fluctuate at a constant speed and the torque accuracy cannot be measured in real time, making it difficult to calibrate the current compensation value are solved. The inverter dead zone compensation method requires the motor load to be constant, and it is difficult to reasonably suppress the load torque and motor phase current when they change, which affects the suppression of motor torque pulsation when the load torque changes, narrows the application scope of motor torque pulsation suppression, and has insufficient practicality.

[0035] Specifically, Figure 1 A flow chart of a method and device for suppressing torque pulsation of a vehicle motor provided in an embodiment of the present application.

[0036] like Figure 1 As shown, the method and device for suppressing the torque pulsation of the vehicle motor include the following steps:

[0037] In step S101 , the current torque, current speed and current electrical angle of at least one motor of the vehicle are acquired.

[0038] It can be understood that in the embodiments of the present application, the torque, speed and electrical angle of the motor can be collected in real time during the operation of the permanent magnet synchronous motor of the electric vehicle. The permanent magnet synchronous motor uses permanent magnets on the rotor to generate a magnetic field, and generates electromagnetic force between the windings fixed on the stator to achieve power transmission. The torque, speed and electrical angle are key parameters of the motor operation state. Among them, the torque can be the rotational torque applied by the motor, indicating the magnitude of the motor output torque, the speed can be the time required for the motor to rotate one circle, and the electrical angle can be the angle of the motor rotor position relative to the fixed coordinate system. By collecting the above parameters, it can be further used for the control and optimization of the motor.

[0039] In step S102, the relationship table between torque, speed, electrical angle and IQ compensation value is queried with the current torque, current speed and current electrical angle as indexes to obtain the target IQ compensation value for suppressing torque pulsation, wherein the relationship table is calculated by a pre-trained neural network model.

[0040] It can be understood that in the embodiments of the present application, the relationship table between torque, speed, electrical angle and IQ compensation value can be calculated by a pre-trained neural network model, and the current parameter table obtained according to the above steps can be looked up to obtain the target IQ compensation value that can suppress torque pulsation under the current torque, speed and electrical angle.

[0041] Optionally, in one embodiment of the present application, before obtaining the target IQ compensation value for suppressing torque pulsation, it also includes: collecting a test data set of at least one motor; inputting the test data set into a neural network model for training to generate a pre-trained neural network model that meets preset conditions; and using the pre-trained neural network model to generate a relationship table between torque, speed, electrical angle and IQ compensation value.

[0042] During the actual execution process, the target speed and motor target torque can be set on the test bench, and IQ compensation values ​​of different phases and amplitudes can be injected to collect data during the motor operation process in real time, including the motor's real-time target speed, target torque, actual speed, actual torque, motor electrical angle, IQ and IQ compensation value, as a test data set for the vehicle motor.

[0043] Furthermore, if Figure 2As shown, it is a schematic diagram of the neural network structure of the motor torque pulsation suppression control of an embodiment of the present application, in which the collected target speed, target torque, actual speed, actual torque, motor electrical angle and iq are used as input parameters of the input layer of the neural network model, and the iq compensation value in which the target speed is equal to the actual speed and the target torque is equal to the actual torque is selected as the output parameter of the output layer of the neural network model for model training, so as to finally obtain a pre-trained neural network model that meets the preset conditions, and use the pre-trained neural network model for calculation, and finally obtain a relationship table between torque, speed, electrical angle and iq compensation value.

[0044] It should be noted that the preset conditions can be set by those skilled in the art according to actual conditions and are not specifically limited here.

[0045] The embodiment of the present application can collect a test data set of at least one motor, input the test data set into a neural network model for training, generate a pre-trained neural network model that meets preset conditions, and then use the pre-trained neural network model to generate a relationship table between torque, speed, electrical angle and IQ compensation value to obtain an IQ compensation value that meets the actual operating state of the motor, thereby improving the accuracy of motor torque pulsation suppression.

[0046] Optionally, in one embodiment of the present application, a pre-trained neural network model is used to generate a relationship table of torque, speed, electrical angle and IQ compensation value, including: based on the pre-trained neural network model, traversing all speeds, torques and electrical angles of at least one motor, obtaining corresponding IQ compensation values, and constructing a relationship table.

[0047] It can be understood that in the embodiments of the present application, based on the pre-trained neural network model, calculations can be performed within the electrical angle range corresponding to one mechanical angle at the same speed and torque to obtain the iq compensation value with a speed deviation of zero under the electrical angle condition in the current state, and the value can be plotted into a table as shown in Table 1, that is, a table of iq compensation values ​​corresponding to different electrical angles at the same speed and torque.

[0048] Table 1

[0049] Electrical angle 0 1 2 3 4 5 … ig compensation value … … … … … … …

[0050] Furthermore, at the same speed and different torques, neural network calculations are performed within the electrical angle range corresponding to one mechanical angle, and the iq compensation value with zero speed deviation under this series of electrical angle conditions is obtained, which is plotted into a table as shown in Table 2, that is, the iq compensation values ​​corresponding to a certain speed, different torques and different electrical angles.

[0051] Table 2

[0052]

[0053]

[0054] By repeatedly obtaining Table 1 and Table 2, an IQ compensation value table at different speeds, different target torques and electrical angles can be obtained, that is, a relationship table between the torque, speed and electrical angle of the motor and the IQ compensation value.

[0055] The present application can be based on a pre-trained neural network model, traverse all the speeds, torques and electrical angles of at least one motor, obtain the corresponding IQ compensation values, and construct a relationship table, so as to obtain a relationship table database of torque, speed, electrical angle and IQ compensation values ​​through pre-calculation of the neural network, so as to further speed up the calculation speed in the actual application process, thereby improving the efficiency of suppressing motor torque pulsation.

[0056] Optionally, in one embodiment of the present application, the preset condition is that the fluctuation value of the rotation speed is lower than a preset threshold.

[0057] It can be understood that in the embodiments of the present application, when obtaining the test data set of the motor, the test range of the speed can be set, that is, the fluctuation value of the speed can be adjusted under the specified preset threshold conditions to avoid the appearance of test data with excessively high speed, which affects the training process of the neural network model and causes deviations in the calculation of the final trained neural network.

[0058] It should be noted that the preset threshold can be set by those skilled in the art according to actual conditions and is not specifically limited here.

[0059] The preset condition of the embodiment of the present application is that the fluctuation value of the rotational speed is lower than a preset threshold value. By limiting the training results of the neural network model, it is ensured that the obtained pre-trained neural network model conforms to the actual application conditions of the motor, avoiding the existence of redundant data in the training data set, thereby simplifying the model training and making it more efficient, and making the model calculation results more accurate.

[0060] In step S103, the output of at least one motor is controlled according to the target iq compensation value so that the actual rotation speed of the at least one motor output is consistent with the target rotation speed.

[0061] It can be understood that in the embodiments of the present application, the current signal of at least one motor can be controlled by the target iq compensation value, thereby suppressing the generation of harmonics in the current, so as to further suppress the electromagnetic torque and ensure the smooth operation of the motor.

[0062] Specifically, the target iq compensation value that suppresses torque pulsation can make the actual speed of the motor's final output consistent with the target speed and not fluctuate, so as to suppress the motor's torque pulsation. For example, a certain speed can be set to remain constant. Under ideal conditions, when the motor torque command is constant, the output current and torque are consistent, and the motor speed will not fluctuate. However, due to the structural properties of the motor itself, the magnetic field strength distribution inside the motor cannot be completely consistent. Under constant target torque, torque fluctuations occur during the motor's operation. At this time, because the test bench does not control the speed in time, there is a deviation between the motor's target speed and the actual speed, such as Figure 3 As shown in the figure, it is a schematic diagram of the actual speed fluctuation of the motor at different electrical angles under constant command torque and speed. As shown in the figure, the deviation can be obtained by injecting different iq and iq compensation values, collecting relevant data in the operation of the motor for neural network model training, and finally calculating the iq compensation value of the target torque equal to the actual torque under a certain target speed, target torque, and electrical angle through the neural network, so that the actual speed of the output is consistent with the target speed, so that the motor can maintain a safe and stable working state.

[0063] The working content of the embodiment of the present application is described in detail below with a specific embodiment. Figure 4 As shown, it is a flowchart of a neural network model training for motor torque pulsation suppression according to an embodiment of the present application, which is specifically as follows.

[0064] Step S401: Traverse low speed, different speeds, and different torques, inject iq compensation values ​​of different phases and amplitudes in a sinusoidal form, and collect motor data during motor operation: electrical angle, target speed, actual speed, target torque, actual torque, iq, and compensated iq value.

[0065] Specifically, the test bench gives the target speed and the target torque of the motor, and injects iq compensation values ​​of different phases and amplitudes. At this time, data is collected during the operation of the motor: target speed, target torque, actual speed, actual torque, motor electrical angle, iq, and iq compensation value.

[0066] Step S402: Perform model training based on the collected motor data as the input of the neural network model, the neural network inputs the target speed, actual speed, target torque, actual torque, motor electrical angle, iq, and outputs the iq compensation value.

[0067] Specifically, the collected data: target speed, target torque, actual speed, actual torque, motor electrical angle and iq are used as input parameters of the neural network model; the target speed is equal to the actual speed, and the target torque is equal to the iq compensation value of the actual torque is used as the model output parameter for model training.

[0068] Step S403: Based on the trained neural network model, based on the same speed and the same torque, with the torque as the target torque and the speed not fluctuating, the lq compensation value is calculated at different electrical angles, and then different speeds and different torques are traversed to create an iq compensation table.

[0069] Specifically, based on the trained neural network model, calculations are performed within the electrical angle range corresponding to one mechanical angle cycle under the same speed and torque to obtain the IQ compensation value with a speed deviation of zero under this series of electrical angle conditions. Neural network calculations are performed within the electrical angle range corresponding to one mechanical angle cycle under the same speed and different torques to obtain the IQ compensation value with a speed deviation of zero under this series of electrical angle conditions. The calculations are repeated to obtain the IQ compensation values ​​at different speeds, different target torques and electrical angles to create an IQ compensation table.

[0070] According to the method and device for suppressing the torque pulsation of the vehicle motor proposed in the embodiment of the present application, the IQ compensation value relationship table of the vehicle motor can be obtained based on the neural network model, and the relationship table can be queried according to the current torque, current speed and current electrical angle to obtain the IQ compensation value that can suppress the torque pulsation, thereby achieving comprehensive suppression of the torque pulsation and improving the suppression accuracy of the vehicle motor on the torque pulsation, which is more stable and practical. Thus, the problem that the motor test bench in the related technology will fluctuate at a constant speed, and the torque accuracy cannot be measured in real time, making it difficult to calibrate the current compensation value, and the inverter dead zone compensation method requires the motor load to be constant, which makes it difficult to reasonably suppress when the load torque and the motor phase current change, affecting the suppression of the motor torque pulsation under the condition of load torque changes, narrowing the application scope of the motor torque pulsation suppression, and the lack of practicality.

[0071] Next, a method and device for suppressing torque pulsation of a vehicle motor proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0072] Figure 5 It is a structural schematic diagram of a method and device for suppressing torque pulsation of a vehicle motor according to an embodiment of the present application.

[0073] like Figure 5 As shown, the vehicle motor torque pulsation suppression device 10 includes: an acquisition module 100 , a query module 200 and a suppression module 300 .

[0074] The acquisition module 100 is used to acquire the current torque, current speed and current electrical angle of at least one motor of the vehicle.

[0075] The query module 200 is used to query the relationship table of torque, speed, electrical angle and IQ compensation value with the current torque, current speed and current electrical angle as indexes to obtain the target IQ compensation value for suppressing torque pulsation, wherein the relationship table is calculated by a pre-trained neural network model.

[0076] The suppression module 300 is used to control the output of at least one motor according to the target IQ compensation value so that the actual speed of the at least one motor output is consistent with the target speed.

[0077] Optionally, in one embodiment of the present application, the device 10 further includes: a collection module, a generation module and a tabulation module.

[0078] Among them, the acquisition module is used to collect a test data set of at least one motor before obtaining a target iq compensation value for suppressing pulsation.

[0079] The generation module is used to input the test data set into the neural network model for training and generate a pre-trained neural network model that meets preset conditions.

[0080] The tabulation module is used to generate a relationship table of speed, torque and electrical angle with IQ compensation value by using a pre-trained neural network model.

[0081] Optionally, in one embodiment of the present application, the tabulation module includes:

[0082] Among them, the construction unit is used to traverse all speeds, torques and electrical angles of at least one motor based on a pre-trained neural network model, obtain corresponding IQ compensation values, and construct a relationship table.

[0083] Optionally, in one embodiment of the present application, the preset condition is that the fluctuation value of the rotation speed is lower than a preset threshold.

[0084] It should be noted that the aforementioned explanation of the embodiment of the method and device for suppressing vehicle motor torque pulsation is also applicable to the method and device for suppressing vehicle motor torque pulsation of this embodiment, and will not be repeated here.

[0085] According to the method and device for suppressing the torque pulsation of the vehicle motor proposed in the embodiment of the present application, the IQ compensation value relationship table of the vehicle motor can be obtained based on the neural network model, and the relationship table can be queried according to the current torque, current speed and current electrical angle to obtain the IQ compensation value that can suppress the torque pulsation, thereby achieving comprehensive suppression of the torque pulsation and improving the suppression accuracy of the vehicle motor on the torque pulsation, which is more stable and practical. Thus, the problem that the motor test bench in the related technology will fluctuate at a constant speed, and the torque accuracy cannot be measured in real time, making it difficult to calibrate the current compensation value, and the inverter dead zone compensation method requires the motor load to be constant, which makes it difficult to reasonably suppress when the load torque and the motor phase current change, affecting the suppression of the motor torque pulsation under the condition of load torque changes, narrowing the application scope of the motor torque pulsation suppression, and the lack of practicality.

[0086] Figure 6A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0087] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0088] When the processor 602 executes the program, the method and device for suppressing the torque pulsation of the vehicle motor provided in the above-mentioned embodiment are implemented.

[0089] Furthermore, the vehicle also includes:

[0090] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0091] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0092] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0093] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0094] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0095] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0096] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method and device for suppressing torque pulsation of a vehicle motor as described above are implemented.

[0097] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0098] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0099] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0100] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0101] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0102] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0103] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0104] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for suppressing torque pulsation of a vehicle motor, characterized in that: The following steps are involved: Obtaining a current torque, a current speed, and a current electrical angle of at least one motor of the vehicle; Using the current torque, current speed and current electrical angle as indexes, querying a relationship table between torque, speed, electrical angle and IQ compensation value to obtain a target IQ compensation value for suppressing torque pulsation, wherein the relationship table is calculated by a pre-trained neural network model; as well as The output of the at least one motor is controlled according to the target IQ compensation value so that the actual speed of the at least one motor output is consistent with the target speed.

2. The method according to claim 1, characterized in that Before obtaining the target iq compensation value for suppressing torque pulsation, the method further includes: acquiring a test data set of the at least one motor; Inputting the test data set into the neural network model for training to generate the pre-trained neural network model that meets preset conditions; The pre-trained neural network model is used to generate a relationship table between the torque, rotational speed, electrical angle and IQ compensation value.

3. The method according to claim 2, characterized in that The method of using the pre-trained neural network model to generate a relationship table of torque, speed, electrical angle and IQ compensation value includes: Based on the pre-trained neural network model, all rotation speeds, torques and electrical angles of the at least one motor are traversed to obtain corresponding IQ compensation values ​​and construct the relationship table.

4. The method according to claim 2, characterized in that: The preset condition is that the fluctuation value of the rotation speed is lower than a preset threshold.

5. A device for suppressing torque pulsation of a vehicle motor, characterized in that: include: An acquisition module, used to acquire a current torque, a current speed, and a current electrical angle of at least one motor of the vehicle; A query module, used to query a relationship table of torque, speed, electrical angle and IQ compensation value with the current torque, current speed and current electrical angle as indexes, to obtain a target IQ compensation value for suppressing torque pulsation, wherein the relationship table is calculated by a pre-trained neural network model; as well as A suppression module is used to control the output of the at least one motor according to the target IQ compensation value so that the actual speed of the at least one motor output is consistent with the target speed.

6. The device according to claim 5, characterized in that Also includes: an acquisition module, configured to acquire a test data set of the at least one motor before obtaining the target IQ compensation value for suppressing pulsation; A generation module, used for inputting the test data set into the neural network model for training, and generating the pre-trained neural network model that meets the preset conditions; A tabulation module is used to generate a relationship table of the rotational speed, torque, electrical angle and IQ compensation value using the pre-trained neural network model.

7. The device according to claim 6, characterized in that The tabulation module comprises: A construction unit is used to traverse all speeds, torques and electrical angles of the at least one motor based on the pre-trained neural network model, obtain corresponding IQ compensation values, and construct the relationship table.

8. The device according to claim 6, characterized in that The preset condition is that the fluctuation value of the rotation speed is lower than a preset threshold.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method and device for suppressing torque pulsation of a vehicle motor as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method and device for suppressing vehicle motor torque pulsation as described in any one of claims 1-4.

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