Motor parameter calibration method and device, equipment and storage medium

Abstract

The application discloses a motor parameter calibration method and device, equipment and a storage medium, and relates to the technical field of synchronous motors. The method comprises the following steps: generating a current pair matrix; performing torque driving evaluation on each current pair in the current pair matrix according to flux data of a target motor, so as to obtain torque driving evaluation data of each current pair; screening a target pair from the current pairs, and screening a current angle of the target pair according to the torque driving evaluation data, so as to obtain a target current angle; wherein the current values of motor stator currents in the target pair are equal; generating a target vector current according to the motor stator currents and the target current angle; performing inverse interpolation processing on the target vector current according to torque values corresponding to each target vector current, so as to obtain a vector current plane; and performing interpolation processing on the vector current plane, so as to obtain target current parameters of the target motor. The application can improve the motor parameter calibration efficiency and calibration accuracy.

Description

Technical Field

[0001] This application relates to the field of synchronous motor technology, and in particular to a method, apparatus, device, and storage medium for motor parameter calibration. Background Technology

[0002] Currently, most motor parameter calibration algorithms employ explicit calculation and implicit serial iterative calculation algorithms. For example, by taking inputs such as measuring the motor flux linkage, setting the maximum phase current, and setting the maximum speed, the inductance plane Ld, Lq, and permanent magnet flux linkage plane are calculated explicitly, while the torque-current plane, torque-voltage plane, and torque external characteristic plane are calculated implicitly.

[0003] Both the explicit and implicit serial iterative calculations in the above algorithms require multiple calculations, which consumes a lot of time. Moreover, the implicit iterative calculations do not refine the operating points sufficiently, resulting in some distortion points. Therefore, existing motor parameter calibration algorithms suffer from low calibration efficiency and low calibration accuracy. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, apparatus, device, and storage medium for motor parameter calibration, which can improve the efficiency and accuracy of motor parameter calibration.

[0005] To achieve the above objectives, a first aspect of this application proposes a method for calibrating motor parameters, the method comprising:

[0006] Based on the stator current and n current angles of the target motor, generate m×n current pairs, and generate a current pair matrix based on the m×n current pairs; wherein, the stator current of the motor has m current values, any two current values ​​are not equal, any two current angles are not equal, and each current pair includes one current value of the stator current and one current angle.

[0007] Based on the flux linkage data of the target motor, torque drive evaluation is performed on each current binary group in the current binary matrix to obtain torque drive evaluation data for each current binary group.

[0008] Target pairs are selected from the current pairs, and the current angles of the target pairs are further selected based on the torque drive evaluation data to obtain the target current angles; wherein, the stator current values ​​of the motors in the target pairs are equal.

[0009] A target vector current is generated based on the motor stator current and the target current angle;

[0010] The target vector currents are subjected to reverse interpolation based on the torque values ​​corresponding to each target vector current to obtain a vector current plane.

[0011] The target current parameters of the target motor are obtained by interpolating the vector current plane.

[0012] Optionally, the flux linkage data includes first-axis flux linkage data and second-axis flux linkage data. The step of performing torque drive evaluation on each current binary group in the current binary group matrix based on the flux linkage data of the target motor to obtain torque drive evaluation data for each current binary group includes:

[0013] The current binary matrix is ​​decomposed to obtain the first-axis current matrix and the second-axis current matrix;

[0014] Torque is calculated based on the first axis flux linkage data, the second axis flux linkage data, the first axis current matrix, and the second axis current matrix to obtain the torque matrix;

[0015] Based on the torque matrix, the maximum torque-current ratio is evaluated for each current binary in the current binary matrix to obtain the torque drive evaluation data for each current binary.

[0016] Optionally, before evaluating the maximum torque-to-current ratio for each current pair in the current pair matrix based on the torque matrix to obtain the torque drive evaluation data for each current pair, the method further includes:

[0017] The first axis voltage matrix is ​​generated based on the second axis flux linkage data and the first axis current matrix;

[0018] The second axis voltage matrix is ​​generated based on the first axis flux linkage data and the second axis current matrix;

[0019] Get the upper limit of voltage;

[0020] Based on the upper limit of the voltage and the first axis voltage matrix, each first axis current in the first axis current matrix is ​​filtered to obtain the first axis retained current matrix;

[0021] Based on the upper limit of the voltage and the second axis voltage matrix, each second axis current in the second axis current matrix is ​​filtered to obtain the second axis retained current matrix;

[0022] The updated current binary matrix is ​​obtained by merging the first axis retained current matrix and the second axis retained current matrix.

[0023] Optionally, obtaining the upper limit voltage value includes:

[0024] Obtain DC voltage and voltage utilization;

[0025] The voltage utilization value is obtained by multiplying the DC voltage and the voltage utilization rate.

[0026] The upper limit of voltage is obtained by calculating the ratio between the voltage utilization value and the preset voltage conversion coefficient.

[0027] Optionally, the step of filtering each first-axis current in the first-axis current matrix according to the upper limit voltage value and the first-axis voltage matrix to obtain the first-axis retained current matrix includes:

[0028] Based on the upper voltage limit, voltage filtering is performed on each first axis voltage in the first axis voltage matrix to obtain the first axis retained voltage matrix;

[0029] Based on the first axis reserved voltage matrix, each of the first axis currents in the first axis current matrix is ​​matched to obtain the first axis reserved current matrix.

[0030] Optionally, the step of performing reverse interpolation on the target vector currents based on the torque values ​​corresponding to each target vector current to obtain a vector current plane includes:

[0031] The target vector currents are sorted from smallest to largest to obtain an initial current sequence;

[0032] A torque sequence is generated based on the position of each target vector current in the initial current sequence and the torque value corresponding to the target vector current;

[0033] A current-torque mapping function is generated based on the initial current sequence and the torque sequence;

[0034] Generate new torque values ​​within the range of the torque sequence;

[0035] The new vector current is obtained by performing an inverse mapping process based on the current-torque mapping function and the newly added torque value.

[0036] The newly added vector current is inserted into the initial current sequence to obtain the target current sequence;

[0037] The vector current plane is generated based on each vector current in the target current sequence.

[0038] Optionally, the step of interpolating the vector current plane to obtain the target current parameters of the target motor includes:

[0039] Obtain the target required temperature vector and the target required torque vector;

[0040] The target current parameters of the target motor are obtained by interpolating the vector current plane based on the target required temperature vector and the target required torque vector.

[0041] To achieve the above objectives, a second aspect of this application provides a motor parameter calibration device, the device comprising:

[0042] The matrix generation module is used to generate m×n current tuples based on the stator current and n current angles of the target motor, and to generate a current tuple matrix based on the m×n current tuples; wherein the stator current of the motor has m current values, any two current values ​​are not equal, any two current angles are not equal, and each current tuple includes one current value of the stator current and one current angle.

[0043] The evaluation module is used to perform torque drive evaluation on each current binary in the current binary matrix based on the flux linkage data of the target motor, and obtain torque drive evaluation data for each current binary.

[0044] A filtering module is used to filter out target pairs from the current pairs and filter the current angles of the target pairs according to the torque drive evaluation data to obtain target current angles; wherein the current values ​​of the motor stator currents in the target pairs are equal.

[0045] The vector calculation module is used to generate a target vector current based on the motor stator current and the target current angle;

[0046] The reverse interpolation module is used to perform reverse interpolation processing on the target vector current based on the torque value corresponding to each target vector current to obtain a vector current plane;

[0047] An interpolation module is used to perform interpolation processing on the vector current plane to obtain the target current parameters of the target motor.

[0048] To achieve the above objectives, a third aspect of this application provides a motor parameter calibration device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0049] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0050] The motor parameter calibration method, apparatus, device, and storage medium proposed in this application first generate a current binary matrix containing m×n current binary pairs based on the motor stator current and current angle, facilitating subsequent parallel computation based on this matrix without iterative computation. Next, torque drive evaluation data for each current binary pair is calculated based on flux linkage data. This evaluation data represents the torque value brought by the current binary pair and serves as a selection criterion for the current binary pairs. Then, for target binary pairs with the same current value, the current angle of the target binary pair is selected based on the torque drive evaluation data to obtain the target current angle; thus, each current value transforms from corresponding to n current angles to corresponding to a target current angle. Finally, a target vector current is generated based on the motor stator current and the target current angle. This target vector current is equivalent to the data point selected from the m×n current binary pairs for parameter calibration. Finally, the target vector current is first back-interpolated based on the torque value corresponding to the target vector current to obtain the vector current plane; then, the vector current plane is interpolated again to obtain the target current parameters of the target motor. These two steps are essentially fitting the discrete data points to obtain the optimal solution, i.e., the target current parameters. In summary, the principle of this application lies in estimating the solution to the parameter calibration problem through the generation and analysis of a large number of random samples (current pairs). This allows the existing iterative algorithm to be replaced with parallel computing, enabling multi-process, multi-point synchronous calculations, fully utilizing AI computing power for acceleration, and reducing the risk of distortion points. Thus, this application can improve the efficiency and accuracy of motor parameter calibration.

[0051] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0052] Figure 1 This is a flowchart of the motor parameter calibration method provided in the embodiments of this application;

[0053] Figure 2 yes Figure 1 Flowchart for step 102;

[0054] Figure 3 This is a flowchart of a motor parameter calibration method provided in another embodiment of this application;

[0055] Figure 4 yes Figure 3 Flowchart for step 304;

[0056] Figure 5 This is an example flowchart of the motor parameter calibration method provided in the embodiments of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0060] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0061] Electric motor: Commonly known as a "motor", it is an electromagnetic device that converts or transmits electrical energy based on the law of electromagnetic induction. Electric motors include AC synchronous motors.

[0062] AC synchronous motor: This is a constant-speed drive motor whose rotor speed maintains a constant proportional relationship with the power supply frequency. The speed formula is n = 60f / p (where n is the speed, f is the power supply frequency, and p is the number of pole pairs). It has the characteristic of constant speed unaffected by load. This motor belongs to the AC motor category, and its stator structure is the same as that of an asynchronous motor. The rotor types include three main structures: permanent magnet, reluctance, and hysteresis.

[0063] Motor stator current (Is): The current flowing through the stator windings of the motor.

[0064] Current angle (θ): The vector angle of the stator current of the motor.

[0065] First axis (d-axis): Represents the axis where the rotor magnet poles are located, and the direction is from the S pole to the N pole.

[0066] The second axis (q-axis) is perpendicular to the d-axis and rotates counterclockwise by 90 degrees along the d-axis. It is usually related to the rate of change of the motor's kinetic energy.

[0067] Traditional explicit and implicit iterative algorithms suffer from low computational efficiency. For example, with three temperature plateaus and four voltage plateaus, the calculation time is approximately 224 minutes. Furthermore, adjusting the data requires multiple calculations, consuming significant time and causing project delays. In addition, implicit iteration lacks sufficient refinement of operating points, resulting in distortion points and lower accuracy in external characteristics and torque indicators. Repeated adjustments further delay the project. This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a motor parameter calibration method, apparatus, device, and storage medium that can improve the efficiency and accuracy of motor parameter calibration.

[0068] The motor parameter calibration method, motor parameter calibration device, motor parameter calibration equipment, and computer-readable storage medium provided in this application are specifically described through the following embodiments. First, the motor parameter calibration method in this application embodiment is described.

[0069] Please refer to Figure 1 , Figure 1 An optional flowchart of a motor parameter calibration method is disclosed. The method may include, but is not limited to, steps 101 to 106.

[0070] Step 101: Generate m×n current pairs based on the stator current and n current angles of the target motor, and generate a current pair matrix based on the m×n current pairs.

[0071] Step 102: Based on the flux linkage data of the target motor, perform torque drive evaluation on each current binary group in the current binary matrix to obtain torque drive evaluation data for each current binary group.

[0072] Step 103: Select target pairs from the current pairs, and filter the current angles of the target pairs according to the torque drive evaluation data to obtain the target current angles; wherein, the current values ​​of the motor stator currents in the target pairs are equal.

[0073] Step 104: Generate the target vector current based on the motor stator current and the target current angle;

[0074] Step 105: Perform reverse interpolation on the target vector currents based on the torque values ​​corresponding to each target vector current to obtain the vector current plane;

[0075] Step 106: Interpolate the vector current plane to obtain the target current parameters of the target motor.

[0076] Steps 101 to 106, as illustrated in this embodiment, firstly, a current binary matrix containing m×n current binary pairs is generated based on the motor stator current and current angle. This facilitates subsequent parallel computation based on the matrix without iterative computation. Next, torque drive evaluation data for each current binary pair is calculated based on the flux linkage data. This evaluation data represents the torque value brought by the current binary pair and serves as a selection criterion for the current binary pairs. Then, for target binary pairs with the same current value, the current angle of the target binary pair is selected based on the torque drive evaluation data to obtain the target current angle. In this way, each current value transforms from corresponding to multiple current angles to corresponding to a target current angle. Finally, a target vector current is generated based on the motor stator current and the target current angle. This target vector current is equivalent to the data point selected from the m×n current binary pairs for parameter calibration. Finally, the target vector current is first back-interpolated based on the torque value corresponding to the target vector current to obtain the vector current plane; then, the vector current plane is interpolated again to obtain the target current parameters of the target motor. These two steps are essentially fitting the discrete data points to obtain the optimal solution, i.e., the target current parameters. In summary, the principle of this application lies in estimating the solution to the parameter calibration problem through the generation and analysis of a large number of random samples (current pairs). This allows the iterative algorithm of existing technologies to be replaced with parallel computing, enabling multi-process, multi-point synchronous calculations, fully utilizing AI computing power for acceleration, and reducing the risk of distortion points. Thus, this application can improve the efficiency and accuracy of motor parameter calibration.

[0077] Prior to step 101, the motor parameter calibration method also includes: current scanning; flux linkage acquisition; and re-interpolation.

[0078] Current scanning: By gradually applying current vectors of different values ​​and angles to the stator windings, the corresponding terminal voltage, phase current, speed, and other signals are recorded. If the operation is under no-load or low-load conditions, the magnetic circuit is relatively linear, which facilitates parameter fitting.

[0079] Flux acquisition: The flux is derived using an integral method by modeling the stator voltage, current, and inductance.

[0080] Reinterpolation: Reconstructing discretely sampled data points on a uniform, equally spaced grid facilitates subsequent fitting, interpolation, or control design. This is because current scanning and flux linkage acquisition are often performed with unequal-spaced and asynchronous sampling points; reinterpolation can provide consistent input and output points to establish a stable parametric model.

[0081] In step 101, firstly, m×n current pairs are generated based on the stator current and n current angles of the target motor. Then, a current pair matrix is ​​generated based on the m×n current pairs. The motor stator current has m current values, and no two current values ​​are equal. No two current angles are equal. Each current pair includes one current value of the motor stator current and one current angle. m and n are both positive integers.

[0082] In one example, the current binary matrix G is shown below:

[0083] Among them, (I) m ,θ n () refers to the pair consisting of the m-th current value and the n-th current angle of the motor stator current, and the dimension of the current pair matrix is ​​m×n.

[0084] In step 102, torque drive evaluation is performed on each current binary group in the current binary group matrix based on the flux linkage data of the target motor, resulting in torque drive evaluation data for each current binary group. The flux linkage data includes first-axis flux linkage data (d-axis flux linkage data) and second-axis flux linkage data (q-axis flux linkage data). The evaluation data represents the torque value brought by the current binary group and serves as a screening criterion for the current binary group. The torque drive evaluation may include maximum torque-to-current ratio evaluation and maximum torque-to-voltage ratio evaluation.

[0085] In one embodiment, reference is made to Figure 2 Step 102 may include:

[0086] Step 201: Decompose the current binary matrix to obtain the first-axis current matrix and the second-axis current matrix;

[0087] Step 202: Calculate the torque matrix based on the first axis flux linkage data, the second axis flux linkage data, the first axis current matrix, and the second axis current matrix.

[0088] Step 203: Based on the torque matrix, evaluate the maximum torque-current ratio of each current binary in the current binary matrix to obtain torque drive evaluation data for each current binary.

[0089] In step 201, the first axis current matrix refers to the d-axis current matrix, which includes m×n d-axis currents id. The second axis current matrix refers to the q-axis current matrix, which includes m×n q-axis currents iq.

[0090] In one example, the d-axis current matrix ID and the q-axis current matrix IQ are shown below:

[0091]

[0092] In step 202, the torque matrix can be obtained by using the torque formula to calculate the torque of the first axis flux linkage data, the second axis flux linkage data, the first axis current matrix, and the second axis current matrix.

[0093] In one example, the torque formula can be shown below:

[0094] T e =1.5n p (ψ d i q -ψ q i d ), where Te is the calculated torque, n p ψ is the number of pole pairs of the motor. d For d-axis flux linkage data, ψ q For q-axis flux linkage data, i d Let i be the d-axis current. q Let q be the q-axis current. After calculation, the torque values ​​at different angles under different current circular states are obtained, i.e., the torque matrix.

[0095] It should be noted that ψ d with i d and i q Related to, ψ q with i d and i q Related. ψ d and ψ q It can be determined by table lookup and interpolation, and can be represented as ψ. d (i d i q ), ψ q (i d i q ).

[0096] Before step 203, refer to Figure 3 Motor parameter calibration methods may also include:

[0097] Step 301: Generate the first axis voltage matrix based on the second axis flux linkage data and the first axis current matrix;

[0098] Step 302: Generate the second-axis voltage matrix based on the first-axis flux linkage data and the second-axis current matrix;

[0099] Step 303: Obtain the upper voltage limit value;

[0100] Step 304: Based on the upper limit of voltage and the first axis voltage matrix, filter each first axis current in the first axis current matrix to obtain the first axis retained current matrix;

[0101] Step 305: Based on the upper limit of voltage and the second-axis voltage matrix, filter each second-axis current in the second-axis current matrix to obtain the second-axis retained current matrix;

[0102] Step 306: Merge the first axis retained current matrix and the second axis retained current matrix to obtain the updated current binary matrix.

[0103] In step 301, the first-axis voltage matrix refers to the d-axis voltage matrix, which includes m×n d-axis voltages. For example, the d-axis voltage can be expressed as: u d =R s i d -ω r ψ q , where u d R is the d-axis voltage. s i is the phase resistance of the motor. d Let ω be the d-axis current. r Let ψ be the electric angular velocity of the motor. q This is the q-axis flux linkage data.

[0104] In step 302, the second-axis voltage matrix refers to the q-axis voltage matrix, which includes m×n q-axis voltages. For example, the q-axis voltage can be expressed as: u q =R s i q +ω r ψ d , where u q R is the q-axis voltage. s i is the phase resistance of the motor. q Let ω be the q-axis current. r Let ψ be the electric angular velocity of the motor. d This represents the d-axis flux linkage data. ω r = 2*π*RPM / 60*pp, where RPM is the motor speed per minute, pp is the number of rotor pole pairs, and π can be 3.1415926.

[0105] In step 303, the upper limit of voltage refers to the upper limit of the voltage amplitude of the d-axis and the voltage amplitude of the q-axis.

[0106] In one embodiment, step 303 may include: acquiring the DC voltage and voltage utilization rate; multiplying the DC voltage and voltage utilization rate to obtain the voltage utilization value; and calculating the ratio between the voltage utilization value and a preset voltage conversion coefficient to obtain the upper limit voltage value. This allows for flexible determination of the upper limit voltage value, which is beneficial for improving the flexibility and efficiency of motor parameter calibration.

[0107] For example, the upper limit of voltage can be expressed as: V mac =Vdc*ModIdx / sqrt(c), where V macVdc is the upper limit of voltage, ModIdx is the DC voltage, sqrt() is the square root function, and c represents the voltage conversion factor, such as c = 3 or c = 2.

[0108] In step 304, the first axis retained current matrix refers to the d-axis retained current matrix, which includes k d-axis currents. k is less than m×n.

[0109] In one embodiment, reference is made to Figure 4 Step 304 may include:

[0110] Step 401: Based on the upper limit of voltage, perform voltage filtering on each first axis voltage in the first axis voltage matrix to obtain the first axis retained voltage matrix;

[0111] Step 402: Match each first-axis current in the first-axis current matrix according to the first-axis reserved voltage matrix to obtain the first-axis reserved current matrix.

[0112] For example, the d-axis voltage matrix calculated using the upper limit voltage value can be used to mark the points that exceed the voltage limit, and these points can be mapped to the d-axis current matrix to map the d-axis current under the voltage limit circle, thereby obtaining the d-axis reserved current matrix.

[0113] The advantages of the above embodiments are that by using the upper limit of voltage and the voltage matrix to update the d-axis current matrix, d-axis currents that do not meet the voltage conditions can be filtered out, which is beneficial to improving the accuracy of parameter calibration. Furthermore, by using matrix parallel calculation, the efficiency of parameter calibration can be improved.

[0114] In step 305, the second-axis retained current matrix refers to the q-axis retained current matrix, which includes k q-axis currents. k is less than m×n.

[0115] In one embodiment, step 305 may include: performing voltage filtering on each first axis voltage in the first axis voltage matrix according to the upper voltage limit value to obtain a first axis retained voltage matrix; and matching each first axis current in the first axis current matrix according to the first axis retained voltage matrix to obtain a first axis retained current matrix.

[0116] For example, the q-axis voltage matrix calculated using the upper limit voltage value can be used to mark the points that exceed the voltage limit, and these points can be mapped to the q-axis current matrix to map the q-axis current under the voltage limit circle, thereby obtaining the q-axis reserved current matrix.

[0117] The advantages of the above embodiments are that by using the upper limit of voltage and the voltage matrix to update the q-axis current matrix, q-axis currents that do not meet the voltage conditions can be filtered out, which is beneficial to improving the accuracy of parameter calibration. Furthermore, by using matrix parallel computation, the efficiency of parameter calibration can be improved.

[0118] In step 306, the k d-axis currents in the d-axis retained current matrix and the k q-axis currents in the q-axis retained current matrix can be merged to obtain the k retained motor stator currents, thereby generating an updated current binary matrix based on the k retained motor stator currents.

[0119] The advantage of the embodiments of steps 301 to 306 above is that by filtering the current pairs in the current pair matrix through the upper limit of voltage and the voltage matrix, current pairs that do not meet the voltage conditions can be filtered out, which is beneficial to improving the accuracy of parameter calibration. Furthermore, by performing parallel matrix calculations, the efficiency of parameter calibration can be improved.

[0120] In step 203, the maximum torque-current ratio of each current binary in the current binary matrix can be evaluated based on the torque matrix to obtain torque drive evaluation data for each current binary.

[0121] Maximum Torque-to-Current Ratio (MTPA): A current control strategy that maximizes the motor's output torque under given current conditions. It improves the motor's torque output capability and efficiency by optimizing the distribution of the current vector.

[0122] Maximum Torque-to-Voltage Ratio (MTPV): Under given voltage conditions, the control strategy that enables the motor to output maximum torque is used to determine the optimal operating point of the motor in the weak magnetic field region and expand the speed range of the motor.

[0123] The advantage of the embodiments of steps 201 to 203 above is that torque drive evaluation data of multiple current tuples can be obtained simultaneously through matrix parallel computing, which is beneficial to improving the efficiency of motor parameter calibration.

[0124] In step 103, target pairs are first selected from the current pairs based on the equality of the motor stator current values. Then, the current angles of the target pairs are selected based on the torque drive evaluation data to obtain the target current angle. As mentioned above, the torque drive evaluation data can be the torque-to-current ratio. This step mainly involves sorting the torque-to-current ratios corresponding to each current value of the motor stator current IS, finding the current angle with the largest torque-to-current ratio for each current value, and thus obtaining the target current angle.

[0125] For example, the current binary matrix includes nine current binary pairs: (I1, θ1), (I1, θ2), (I1, θ3), (I2, θ1), (I2, θ2), (I2, θ3), (I3, θ1), (I3, θ2), and (I3, θ3). Since all current values ​​are I1, (I1, θ1), (I1, θ2), and (I1, θ3) can be considered as three target binary pairs corresponding to I1. These three target binary pairs are then sorted according to their respective torque-to-current ratios, and the current angle corresponding to the largest torque-to-current ratio is taken as the target angle of I1. For instance, if the angle corresponding to the largest torque-to-current ratio among (I1, θ1), (I1, θ2), and (I1, θ3) is θ1, then the target angle of I1 is θ1. Since the current values ​​are all I2, (I2, θ1), (I2, θ2), and (I2, θ3) can be considered as three target pairs corresponding to I2. Assuming the angle corresponding to the largest torque-current ratio among (I2, θ1), (I2, θ2), and (I2, θ3) is θ3, then the target angle for I2 is θ3. Similarly, since the current values ​​are all I3, (I3, θ1), (I3, θ2), and (I3, θ3) can be considered as three target pairs corresponding to I3. Assuming the angle corresponding to the largest torque-current ratio among (I3, θ1), (I3, θ2), and (I3, θ3) is θ2, then the target angle for I3 is θ2.

[0126] It is understandable that the torque drive evaluation data mentioned above can be not only the torque-current ratio, but also indicators such as the torque-voltage ratio.

[0127] In step 104, the target vector current refers to the current vector i = [id, iq] in a certain reference coordinate system (such as the dq coordinate system in the rotor direction), which describes the components of the stator current in two orthogonal components related to the magnetic field direction. The target vector current can be generated based on the motor stator current and the target current angle.

[0128] In step 105, the target vector currents are subjected to inverse interpolation based on the torque values ​​corresponding to each target vector current to obtain a vector current plane. Inverse interpolation is a mathematical method used to solve for approximate values ​​of independent variables when the function values ​​are known. In this step, the torque values ​​corresponding to each target vector current are used as known function values, and the target vector currents are used as independent variables. The purpose of inverse interpolation is to obtain more vector currents.

[0129] In one embodiment, step 105 may include: sorting the target vector currents from smallest to largest to obtain an initial current sequence; generating a torque sequence based on the position of each target vector current in the initial current sequence and the torque value corresponding to the target vector current; generating a current-torque mapping function based on the initial current sequence and the torque sequence; generating new torque values ​​within the range of the torque sequence; performing inverse mapping processing based on the current-torque mapping function and the new torque values ​​to obtain new vector currents; inserting the new vector currents into the initial current sequence to obtain a target current sequence; and generating a vector current plane based on each vector current in the target current sequence.

[0130] The advantage of the above embodiments is that by using the idea of ​​inverse interpolation to generate a vector current plane, the operating point can be refined and fitted, thereby improving the calculation speed while reducing the current mapping distortion points, and thus improving the efficiency and accuracy of motor parameter calibration.

[0131] In step 106, interpolation is performed on the vector current plane to obtain the target current parameters of the target motor. The target current parameters include the current id-iq plane.

[0132] In one embodiment, step 106 may include: obtaining the target required temperature vector and the target required torque vector; and interpolating the vector current plane based on the target required temperature vector and the target required torque vector to obtain the target current parameters of the target motor. Specifically, the interpolation method solves for the function value by knowing the value of the independent variable. In this embodiment, the target required temperature vector and the target required torque vector are used as independent variables, and the vector current plane is used as the function, with the aim of solving for the function value, i.e., the target current parameters.

[0133] In one example, refer to Figure 5 Motor parameter calibration methods may include:

[0134] The upsampling process includes: generating m×n current tuples based on the stator current and n current angles of the target motor; generating a current tuple matrix based on the m×n current tuples; decomposing the current tuple matrix to obtain the d-axis current matrix ID and the q-axis current matrix IQ; the d-axis current matrix includes the d-axis current id, and the q-axis current matrix includes the q-axis current iq.

[0135] The parallel computing process includes: based on the d-axis flux linkage data ψ d q-axis flux linkage data ψ q The torque value T is obtained by calculating the torque using the d-axis current id and the q-axis current iq. e This generates the torque matrix, where ψ d (i d i q ), ψ q (id i q );u d =R s i d -ω r ψ q The d-axis voltage u is calculated. d This generates the d-axis voltage matrix; u q =R s i q +ω r ψ d The q-axis voltage u is calculated. q This generates the q-axis voltage matrix; under the constraints of DC voltage Vdc and voltage utilization ModIdx, torque drive evaluation (including maximum torque current ratio evaluation (Find MTPA) and maximum torque voltage ratio evaluation (Find MTPV)) is performed to obtain the target vector current.

[0136] The inverse interpolation process includes: sorting and integrating the simultaneously calculated target vector current and torque values ​​for inverse interpolation to form a vector current plane; finally, adding the temperature vector Temp and torque vector to interpolate the vector current plane to obtain the required current id iq plane. For example, inputting the temperature vector Temp, and then using interpolate LUT(vdc, N) e T e The required current id iq plane is obtained using a functional formula. N e This represents the motor speed.

[0137] The benefits of this example include at least the following: (1) Speed ​​gains: Taking a project with 3 temperature points and 4 voltage points as an example, the calculation time for parameters is reduced from 244 minutes to 17 minutes, and the calculation speed is increased by 13 times. Combined with the improvement in torque accuracy and the reduction in parameter adjustment time, the overall project calibration time can be reduced by 3 to 4 days. (2) Quality gains: The weighted average percentage improvement in external characteristics at different speed points is 4.4%, the average efficiency of the motor at different speed points is increased by 0.86%, and the highest efficiency of the motor is increased by 0.73% to 96.94%. (3) Cost gains: The cost of the traditional implicit iterative algorithm is 800,000, while the cost of this application can be less than 100,000, the toolchain cost is reduced by 700,000, the total time is shortened by 3 working days, and the total cost is saved by about 730,000.

[0138] The various technical features in the above embodiments can be combined arbitrarily, as long as there is no conflict or contradiction between the combinations of features. However, due to space limitations, they are not described one by one. Therefore, the arbitrary combination of various technical features in the above embodiments is also within the scope of this specification.

[0139] This application also discloses a motor parameter calibration device, which includes:

[0140] The matrix generation module is used to generate m×n current tuples based on the stator current and n current angles of the target motor, and to generate a current tuple matrix based on the m×n current tuples. The stator current of the motor has m current values, and no two current values ​​are equal, and no two current angles are equal. Each current tuple includes one current value of the stator current and one current angle.

[0141] The evaluation module is used to evaluate the torque drive of each current binary in the current binary matrix based on the flux linkage data of the target motor, and obtain the torque drive evaluation data of each current binary.

[0142] The filtering module is used to filter out target pairs from current pairs and filter the current angles of the target pairs according to torque drive evaluation data to obtain the target current angles; wherein, the current values ​​of the motor stator currents in the target pairs are equal.

[0143] The vector calculation module is used to generate the target vector current based on the motor stator current and the target current angle.

[0144] The reverse interpolation module is used to perform reverse interpolation on the target vector current based on the torque value corresponding to each target vector current to obtain the vector current plane;

[0145] The interpolation module is used to perform interpolation processing on the vector current plane to obtain the target current parameters of the target motor.

[0146] It should be noted that the specific implementation of this motor parameter calibration device is basically the same as the specific implementation of the motor parameter calibration method described above, and will not be repeated here.

[0147] This application also discloses a motor parameter calibration device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the motor parameter calibration method described above. The motor parameter calibration device can be an electronic device, such as an in-vehicle terminal, smartphone, wearable device, or portable computer, etc. The motor parameter calibration device can also be a vehicle. The vehicle can be a new energy vehicle, such as a hybrid vehicle or a pure electric vehicle. For example, the vehicle can be a sedan, SUV, MPV, pickup truck, van, bus, etc.

[0148] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the motor parameter calibration method described above.

[0149] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0150] This application also provides a computer program product, which includes a computer program. The computer program is read and executed by the processor of the motor parameter calibration device, so that the processor implements the motor parameter calibration method described above when executing the computer program.

[0151] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0152] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0153] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0155] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. 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 comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0156] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0158] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0160] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0161] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for calibrating motor parameters, characterized in that, The method further includes: Based on the stator current and n current angles of the target motor, generate m×n current pairs, and generate a current pair matrix based on the m×n current pairs; wherein, the stator current of the motor has m current values, any two current values ​​are not equal, any two current angles are not equal, and each current pair includes one current value of the stator current and one current angle. Based on the flux linkage data of the target motor, torque drive evaluation is performed on each current binary group in the current binary matrix to obtain torque drive evaluation data for each current binary group. Target pairs are selected from the current pairs, and the current angles of the target pairs are further selected based on the torque drive evaluation data to obtain the target current angles; wherein, the stator current values ​​of the motors in the target pairs are equal. A target vector current is generated based on the motor stator current and the target current angle; The target vector currents are subjected to reverse interpolation based on the torque values ​​corresponding to each target vector current to obtain a vector current plane. The target current parameters of the target motor are obtained by interpolating the vector current plane.

2. The method according to claim 1, characterized in that, The flux linkage data includes first-axis flux linkage data and second-axis flux linkage data. The step of performing torque drive evaluation on each current binary group in the current binary group matrix based on the flux linkage data of the target motor to obtain torque drive evaluation data for each current binary group includes: The current binary matrix is ​​decomposed to obtain the first-axis current matrix and the second-axis current matrix; Torque is calculated based on the first axis flux linkage data, the second axis flux linkage data, the first axis current matrix, and the second axis current matrix to obtain the torque matrix; Based on the torque matrix, the maximum torque-current ratio is evaluated for each current binary in the current binary matrix to obtain the torque drive evaluation data for each current binary.

3. The method according to claim 2, characterized in that, Before evaluating the maximum torque-to-current ratio for each current binary in the current binary matrix based on the torque matrix to obtain the torque drive evaluation data for each current binary, the method further includes: The first axis voltage matrix is ​​generated based on the second axis flux linkage data and the first axis current matrix; The second axis voltage matrix is ​​generated based on the first axis flux linkage data and the second axis current matrix; Get the upper limit of voltage; Based on the upper limit of the voltage and the first axis voltage matrix, each first axis current in the first axis current matrix is ​​filtered to obtain the first axis retained current matrix; Based on the upper limit of the voltage and the second axis voltage matrix, each second axis current in the second axis current matrix is ​​filtered to obtain the second axis retained current matrix; The updated current binary matrix is ​​obtained by merging the first axis retained current matrix and the second axis retained current matrix.

4. The method according to claim 3, characterized in that, The process of obtaining the upper limit voltage value includes: Obtain DC voltage and voltage utilization; The voltage utilization value is obtained by multiplying the DC voltage and the voltage utilization rate. The upper limit of voltage is obtained by calculating the ratio between the voltage utilization value and the preset voltage conversion coefficient.

5. The method according to claim 3, characterized in that, The step of filtering each first-axis current in the first-axis current matrix based on the upper limit voltage value and the first-axis voltage matrix to obtain the first-axis retained current matrix includes: Based on the upper voltage limit, voltage filtering is performed on each first axis voltage in the first axis voltage matrix to obtain the first axis retained voltage matrix; Based on the first axis reserved voltage matrix, each of the first axis currents in the first axis current matrix is ​​matched to obtain the first axis reserved current matrix.

6. The method according to any one of claims 1 to 5, characterized in that, The step of performing reverse interpolation processing on the target vector currents based on the torque values ​​corresponding to each target vector current to obtain a vector current plane includes: The target vector currents are sorted from smallest to largest to obtain an initial current sequence; A torque sequence is generated based on the position of each target vector current in the initial current sequence and the torque value corresponding to the target vector current; A current-torque mapping function is generated based on the initial current sequence and the torque sequence; Generate new torque values ​​within the range of the torque sequence; The new vector current is obtained by performing an inverse mapping process based on the current-torque mapping function and the newly added torque value. The newly added vector current is inserted into the initial current sequence to obtain the target current sequence; The vector current plane is generated based on each vector current in the target current sequence.

7. The method according to any one of claims 1 to 5, characterized in that, The step of interpolating the vector current plane to obtain the target current parameters of the target motor includes: Obtain the target required temperature vector and the target required torque vector; The target current parameters of the target motor are obtained by interpolating the vector current plane based on the target required temperature vector and the target required torque vector.

8. A motor parameter calibration device, characterized in that, The device includes: The matrix generation module is used to generate m×n current tuples based on the stator current and n current angles of the target motor, and to generate a current tuple matrix based on the m×n current tuples; wherein the stator current of the motor has m current values, any two current values ​​are not equal, any two current angles are not equal, and each current tuple includes one current value of the stator current and one current angle. The evaluation module is used to perform torque drive evaluation on each current binary in the current binary matrix based on the flux linkage data of the target motor, and obtain torque drive evaluation data for each current binary. A filtering module is used to filter out target pairs from the current pairs and filter the current angles of the target pairs according to the torque drive evaluation data to obtain target current angles; wherein the current values ​​of the motor stator currents in the target pairs are equal. The vector calculation module is used to generate a target vector current based on the motor stator current and the target current angle; The reverse interpolation module is used to perform reverse interpolation processing on the target vector current based on the torque value corresponding to each target vector current to obtain a vector current plane; An interpolation module is used to perform interpolation processing on the vector current plane to obtain the target current parameters of the target motor.

9. A motor parameter calibration device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the method as described in any one of claims 1 to 7.

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