Permanent magnet synchronous motor rotor zero initial angle self-learning method and system

Abstract

The application discloses a permanent magnet synchronous motor resolver zero initial angle self-learning method and system, which comprises the following steps: sending a resolver zero initial angle self-learning instruction to a motor controller; after the motor controller receives the self-learning instruction, starting the self-learning function, and setting a target direct-axis current and a target quadrature-axis current to 0; the motor controller sends a rotating speed request instruction to a rack, and the rack drags the motor to a preset rotating speed after receiving the rotating speed request of the motor controller; after the motor controller identifies that the rotating speed is stable at the preset rotating speed, PI closed loop control is started on a direct-axis voltage, and a phase-locked loop calculates a resolver zero initial angle. The algorithm is simple, does not need a complex arctangent function, reduces CPU resource consumption, has PI closed loop control, greatly reduces calculation errors caused by input signal disturbance, has good algorithm robustness, saves manual operation, and is high in efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of motor control, and in particular to a self-learning method and system for the initial angle of the zero position of a permanent magnet synchronous motor resolver. Background Technology

[0002] Currently, there are several methods for calibrating and detecting the initial zero-position angle of a resolver. One method involves recording the calibration using instruments after the resolver is installed; this method is labor-intensive, inefficient, and has poor accuracy. Another method uses a power analyzer and oscilloscope to analyze the waveform and record the zero-crossing point of the back electromotive force to obtain the initial zero-position angle; this method has a relatively large error. Yet another method uses the relationship between current and torque to obtain the initial zero-position angle; this method is complex to implement and highly dependent on the hardware detection system. A third method uses the arctangent of the direct-axis voltage and quadrature-axis voltage to calculate the average of the values; this method is greatly affected by input signal disturbances, has a large error, poor accuracy, and involves division and arctangent operations, making calculation complex and consuming significant CPU resources. Summary of the Invention

[0003] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the purpose of this invention is to provide a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver, which solves the problems of large errors, poor accuracy, and complex calculations involving division and arctangent operations, as well as high CPU resource consumption in existing methods for calculating the initial zero-position angle of a resolver.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a self-learning method for the initial angle of the zero position of a permanent magnet synchronous motor resolver, comprising:

[0008] Send the resolver zero-position initial angle self-learning command to the motor controller;

[0009] After receiving the self-learning command, the motor controller activates the self-learning function and sets the target direct-axis current and the target quadrature-axis current to 0.

[0010] The motor controller sends a speed request command to the test bench. Upon receiving the speed request from the motor controller, the test bench drives the motor to a preset speed.

[0011] After the motor controller detects that the speed has stabilized at the preset speed, it performs PI closed-loop control on the direct shaft voltage and calculates the initial angle of the resolver zero position using a phase-locked loop.

[0012] As described in this invention, a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver is provided, wherein: after the motor controller detects that the rotational speed has stabilized at a preset speed, it performs PI closed-loop control on the direct-axis voltage, and the phase-locked loop calculates the initial zero-position angle of the resolver, including the closed-loop deviation Δu of the direct-axis voltage detected by the motor controller. d If the value is less than the preset value, the PI closed-loop control system is stable, and the ε value at this time is... PLL The initial angle of the zero position of the resolver is determined and stored in non-volatile memory.

[0013] As described in this invention, a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver is provided, wherein: after the motor controller has stored the initial zero-position angle of the resolver, it sends a 0 speed request to the test bench, the test bench drives the motor to decelerate to 0, the motor controller closes the self-learning function, and sends a self-learning completion signal back to the test bench, thus completing the self-learning.

[0014] As described in this invention, a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver includes: based on the zero-position deflection angle when the resolver is not zeroed, the analysis of the measured values ​​and true values ​​of the direct-axis voltage and quadrature-axis voltage includes...

[0015] Let the d0q0 axis system be the true dq axis without zero position deviation, and the d1q1 axis system be the measured dq axis with zero position deviation;

[0016] The actual dq axis voltage is only u q0 The measurable dq-axis voltage has a zero-point deviation in the direct-axis voltage u. d1 and quadrature axis voltage u q1 Therefore, the resulting zero-position deflection angle of the refractive index is as follows:

[0017]

[0018] Where ε is the zero-position deflection angle of the resolver.

[0019] As described in this invention, the method for self-learning the initial angle of the resolver zero position of a permanent magnet synchronous motor includes: calculating the resolver zero position deflection angle ε using a phase-locked loop.

[0020] As a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver according to the present invention, the calculation of the resolver zero-position angle ε using a phase-locked loop includes,

[0021] Based on the principle of phase-locked loops, u is obtained. d The closed-loop analysis of =0 is as follows:

[0022] The specific process of transforming the measurable d1q1 axis system to the d0q0 axis system with a transformation angle of -ε is shown below:

[0023]

[0024] Based on the above formula derivation, we can obtain:

[0025] u d0 =cos(-ε)·u d1 +sin(-ε)·u q1 =cos(ε)·u d1 -sin(ε)·u q1

[0026] Among them, u d0 The actual direct-axis voltage calculated for the phase-locked loop.

[0027] As described in this invention, a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver is further comprising:

[0028] Let t be time, and based on the convergence of the PI closed-loop control system, we find that as t→∞, the PI closed-loop control system stabilizes as follows:

[0029] (ε-ε PLL → 0, ε PLL =ε

[0030] Where, ε PLL The initial angle of the resolver zero position calculated for the phase-locked loop.

[0031] Secondly, embodiments of the present invention provide a self-learning system for the initial zero-position angle of a permanent magnet synchronous motor resolver, comprising,

[0032] The sending module sends the resolver zero-position initial angle self-learning command to the motor controller;

[0033] The receiving module, after receiving the self-learning instruction, enables the self-learning function and sets the target direct-axis current and target quadrature-axis current to 0;

[0034] The calculation module is used to send a speed request command from the motor controller to the test bench. Upon receiving the speed request from the motor controller, the test bench drives the motor to a preset speed.

[0035] After the motor controller detects that the speed has stabilized at the preset speed, it performs PI closed-loop control on the direct shaft voltage and calculates the initial angle of the resolver zero position using a phase-locked loop.

[0036] Thirdly, embodiments of the present invention provide a computing device, including:

[0037] Memory and processor;

[0038] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement a self-learning method for the zero-position initial angle of a permanent magnet synchronous motor resolver as described in any embodiment of the present invention.

[0039] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned method for self-learning the initial angle of a resolver zero position in a permanent magnet synchronous motor.

[0040] The beneficial effects of this invention are: the algorithm is simple and does not require a complex arctangent function, thus reducing CPU resource consumption; the PI closed-loop control greatly reduces the calculation error caused by input signal disturbances, and the algorithm has good robustness; the signals used for calculation are the variables calculated by the basic FOC algorithm of permanent magnet synchronous motor, without the need to add equipment, lines, or measure more other signals; manual operation only requires connecting the equipment, thus improving efficiency. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0042] Figure 1 This is a flowchart illustrating the self-learning process of the resolver zero-position initial angle of the permanent magnet synchronous motor according to the present invention.

[0043] Figure 2 This is an electrical connection diagram of the self-learning method for the zero-position initial angle of the permanent magnet synchronous motor resolver according to the present invention.

[0044] Figure 3 This is a diagram of the PI closed-loop control system for the self-learning method of the zero-position initial angle of the permanent magnet synchronous motor resolver in this invention.

[0045] Figure 4 This is a schematic diagram illustrating the analysis of the measured and true values ​​of the direct-axis voltage and quadrature-axis voltage in the self-learning method for the zero-position initial angle of the permanent magnet synchronous motor resolver according to the present invention.

[0046] Figure 5 The diagrams of the initial zero-position angle of the resolver calculated by the two methods are shown for the resolver zero-position angle self-learning method of the permanent magnet synchronous motor of the present invention when the resolver angle offset is -π.

[0047] Figure 6The diagrams of the initial zero-position angle of the resolver calculated by the two methods are shown for the resolver zero-position angle self-learning method of the permanent magnet synchronous motor of the present invention when the resolver angle offset is -π.

[0048] Figure 7 The diagrams of the initial zero-position angle of the resolver calculated by the two methods are shown for the resolver zero-position angle self-learning method of the permanent magnet synchronous motor of the present invention when the resolver angle offset is -π.

[0049] Figure 8 The diagrams of the initial zero-position angle of the resolver calculated by the two methods are shown for the resolver zero-position angle self-learning method of the permanent magnet synchronous motor of the present invention when the resolver angle offset is -2 / 3π.

[0050] Figure 9 The diagrams of the initial zero-position angle of the resolver calculated by the two methods are shown for the resolver zero-position angle self-learning method of the permanent magnet synchronous motor of the present invention when the resolver angle offset is -1 / 3π.

[0051] Figure 10 The diagrams of the initial zero-position angle of the resolver calculated by the two methods are shown for the resolver zero-position angle self-learning method of the permanent magnet synchronous motor of the present invention when the resolver angle offset is 0.

[0052] Figure 11 The diagrams of the initial zero-position angle of the resolver calculated by the two methods are shown for the resolver zero-position angle self-learning method of the permanent magnet synchronous motor of the present invention when the resolver angle offset is 1 / 3π. Detailed Implementation

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0056] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include the three-dimensional spatial dimensions of length, width, and depth.

[0057] Example 1

[0058] Reference Figures 1-4 As an embodiment of the present invention, a self-learning method for the initial angle of zero position of a permanent magnet synchronous motor resolver is provided, comprising:

[0059] S1: Sends the resolver zero-position initial angle self-learning command to the motor controller. It should be noted that:

[0060] like Figure 3 As shown, the algorithm of this invention is based on u d A closed-loop PI-controlled phase-locked loop (PLL). The PLL input is a variable in the electronic control unit's (ECU) FOC module, specifically the target direct-axis voltage. Target quadrature-axis voltage These are the basic variables used in vector control; in the diagram, cos and sin are trigonometric function operations, kp is the proportional coefficient of pi control, and ki is the integral coefficient of pi control. This is the integral element in the PI controller. 1 / s acts as an operator, the symbol represents integration, ki is the integration coefficient, and the final output is ε. PLL That is, the initial angle of the zero position of the resolver.

[0061] S2: After receiving the self-learning command, the motor controller activates the self-learning function and sets the target direct-axis current and target quadrature-axis current to 0. It should be noted that:

[0062] like Figure 2 As shown, after connecting the motor, motor controller, and test bench, the engineer sends a resolver zero-position initial angle self-learning command to the motor controller from the test bench. Upon receiving the self-learning command, the motor controller activates the self-learning function, targeting the direct-axis current. and target cross-axis current It was set to 0.

[0063] The electronic controller sends a speed request command to the test bench. Upon receiving the speed request from the electronic controller, the test bench drives the motor to the preset speed.

[0064] The system controls the motor to run stably at a preset speed. The motor controller sends commands to the test bench or engine to drive the motor to rotate, without any restrictions.

[0065] S3: The motor controller sends a speed request command to the test bench. Upon receiving the speed request, the test bench drives the motor to the preset speed. It should be noted that:

[0066] S4: After the motor controller detects that the speed has stabilized at the preset speed, it begins PI closed-loop control of the direct-axis voltage, and the phase-locked loop calculates the initial angle of the resolver zero position. It should be noted that:

[0067] After the motor controller detects that the speed has stabilized at the preset speed, it begins PI closed-loop control of the direct-axis voltage. The phase-locked loop calculates the resolver zero-position initial angle, including the closed-loop deviation Δu of the direct-axis voltage detected by the motor controller. d If the value is less than the preset value, the PI closed-loop control system is stable, and the ε value at this time is... PLL The initial angle of the resolver zero position is calculated for the phase-locked loop (PLL) and stored in non-volatile memory.

[0068] In practical engineering applications, it is not appropriate to immediately determine stability as soon as the value is less than the preset value. Therefore, a certain time is set when the value is less than the preset value to determine the stability of the closed-loop system.

[0069] After the motor controller has stored the initial zero-position angle of the resolver, it sends a 0 speed request to the test bench. The test bench drives the motor to decelerate to 0. The electronic controller then turns off the self-learning function and sends a self-learning completion signal back to the test bench. Once the self-learning is complete, the engineer observes that the self-learning is complete and the process ends.

[0070] Among these, the analysis of the measured values ​​and true values ​​of the direct-axis voltage and quadrature-axis voltage, based on the zero-position deflection angle when the resolver is not zeroed, includes...

[0071] When both id and iq are 0, the real value is u. d =0, u q =ω r ψ f

[0072] Where, ω r ψ is the rotor speed. f For stator flux linkage.

[0073] However, considering that the resolver was not zeroed and had a zero-position deflection, the measured u... d u q The measured value is not one of the values ​​mentioned above. The analysis of the measured value and the true value is as follows:

[0074] like Figure 4 As shown, the d0q0 axis system is the true dq axis without zero deviation, and the d1q1 axis system is the measured dq axis with zero deviation.

[0075] The actual dq axis voltage is only u q0 The measurable dq-axis voltage has a zero-point deviation in the direct-axis voltage u. d1 and quadrature axis voltage u q1 Therefore, the resulting zero-position deflection angle of the refractive index is as follows:

[0076]

[0077] Here, ε is the zero-position deflection angle of the resolver.

[0078] The calculation of the resolver zero-position deflection angle ε using a phase-locked loop includes,

[0079] Based on the principle of phase-locked loops, u is obtained. d The closed-loop analysis of =0 is as follows:

[0080] The specific process of transforming the measurable d1q1 axis system to the d0q0 axis system with a transformation angle of -ε is shown below:

[0081]

[0082] Based on the above formula derivation, we can obtain:

[0083] u d0 =cos(-ε)·u d1 +sin(-ε)·u q1 =cos(ε)·u d1 -sin(ε)·u q1

[0084] Among them, u d0 The actual direct-axis voltage calculated for the phase-locked loop (PLL); the actual direct-axis voltage should be 0. The ud0 calculated by the PLL initially has a value, but eventually this ud0 will close to 0.

[0085] Let t be time, and based on the convergence of the PI closed-loop control system, we find that as t→∞, the PI closed-loop control system stabilizes as follows:

[0086] (ε-ε PLL → 0, ε PLL =ε

[0087] It should be noted that the above-mentioned determination of the direct-axis voltage closed-loop deviation Δu identified by the PI closed-loop control system and the above-mentioned motor controller... d If the value is less than the preset value, the closed-loop control system is stable and equivalent, because the closed-loop calculation reaches the final ε-ε. PLL ε tends to 0 PLL Since the initial angle is almost correct, the calculated ud (also used for feedback) will be the actual ud0 in the analyzed d0q0 axis system. If ud0 = 0, then ud = ud0 = 0. The target of the closed loop is 0, the feedback is ud, and the closed loop deviation = target - feedback = 0 - 0.

[0088] Where, ε PLL This is the initial angle of the zero position of the resolver.

[0089] Because the resolver deflection angle is fixed, ud1 and u q1 It remains almost unchanged, which is equivalent to a step input to the system; the step is the rotational deflection angle. The real u d =0, so u can be achieved using PI control. d0 =0 closed loop.

[0090] Therefore, it can be seen that the measured u d1 and u q1 Calculated u d0 =0 to close the loop, and the angle can be calculated.

[0091] This embodiment also provides a self-learning system for the initial zero-position angle of a permanent magnet synchronous motor resolver, including:

[0092] The sending module sends the resolver zero-position initial angle self-learning command to the motor controller;

[0093] After receiving the self-learning command, the receiving module and the motor controller enable the self-learning function and set the target direct-axis current and the target quadrature-axis current to 0.

[0094] The calculation module sends a speed request command to the test bench. Upon receiving the speed request from the motor controller, the test bench drives the motor to the preset speed.

[0095] After the motor controller detects that the speed has stabilized at the preset speed, it performs PI closed-loop control on the direct shaft voltage and the phase-locked loop calculates the initial angle of the resolver zero position.

[0096] This embodiment also provides a computing device applicable to a self-learning method for the initial angle of a resolver zero position in a permanent magnet synchronous motor, including:

[0097] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver, as proposed in the above embodiments.

[0098] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0099] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver as proposed in the above embodiment.

[0100] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0101] Example 2

[0102] This is the second embodiment of the present invention. Unlike the first embodiment, this embodiment provides a verification test of a self-learning method for the initial angle of the resolver zero position of a permanent magnet synchronous motor, and verifies and explains the technical effects used in this method.

[0103] This embodiment uses a traditional method to calculate the initial zero-position angle of a resolver as an example: With the permanent magnet synchronous motor (PMSM) under no-load conditions, the motor controller operates in torque mode; the motor is driven by an engine and stabilized at a predetermined speed; at the predetermined speed, the rotor voltage ud on the d-axis and the voltage uq on the q-axis of the PMSM are calculated multiple times to obtain the ud dataset {ud1, ud2, ..., udi, ..., udn} and the uq dataset {uq1, uq2, ..., uqi, ..., uqn}, where udi represents the ud value obtained in the i-th calculation, and uqi represents the uq value obtained in the i-th calculation; according to the formula θi = tan -1 (udi / uqi) calculates the initial zero-position angle θi, and performs multiple calculations to obtain the θ dataset {θ1, θ2, ... θi}. i , …, θ n}, where θi represents the initial angle value at zero position obtained in the i-th calculation; according to the formula θ=(θ1+θ2+…+θ i +…+θ n The initial angle θ at the zero position is obtained by calculating ) / n.

[0104] And such Figures 5-11 As shown, the horizontal axis is the time axis, the vertical axis is the angle value, and the dense curve is the initial angle of the zero position of the resolver calculated by the arctangent method. In this embodiment, the resolver angle offset is manually set to -π, -2 / 3π, -1 / 3π, 0, 1 / 3π, 2 / 3π, and π respectively. In addition, the initial angle of the zero position of the resolver calculated by the method of this invention is summarized in the following table.

[0105] Table 1: Data Summary Table

[0106]

[0107] The traditional method involves rotating the engine motor and calculating the angle using the arctangent method. Our algorithm, however, is simpler, eliminating the need for complex arctangent functions and reducing CPU resource consumption. PI closed-loop control significantly reduces calculation errors caused by input signal disturbances, resulting in better algorithm robustness. The calculations use variables calculated by the basic FOC algorithm for permanent magnet synchronous motors, eliminating the need for additional equipment, wiring, or measuring other signals. After connecting the motor, motor controller, and test bench, the engineer sends a resolver zero-position initial angle self-learning command from the test bench to the motor controller. Upon receiving the self-learning command, the controller activates its self-learning function and sends a speed request command to the test bench. The test bench, upon receiving the speed request, moves the motor to the preset speed. Manual operation only requires connecting the equipment, improving efficiency.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A self-learning method for the initial zero-position angle of a permanent magnet synchronous motor resolver, characterized in that: include, Send the resolver zero-position initial angle self-learning command to the motor controller; After receiving the self-learning command, the motor controller activates the self-learning function and sets the target direct-axis current and the target quadrature-axis current to 0. The motor controller sends a speed request command to the test bench. Upon receiving the speed request from the motor controller, the test bench drags the motor to a preset speed. After the motor controller detects that the speed has stabilized at the preset speed, it performs PI closed-loop control on the direct shaft voltage and the phase-locked loop calculates the initial angle of the resolver zero position. After the motor controller detects that the rotational speed has stabilized at the preset speed, it performs PI closed-loop control on the direct-axis voltage. The phase-locked loop calculates the initial angle of the resolver zero position, including: the motor controller detects the direct-axis voltage closed-loop deviation. If the value is less than the preset value, the PI closed-loop control system is stable, and at this time... The initial angle of the zero position of the resolver is determined and stored in non-volatile memory; Calculating the zero-position deflection angle of the resolver using a phase-locked loop ; Calculating the zero-position deflection angle of the resolver using a phase-locked loop include, Based on the principle of phase-locked loops, The closed-loop analysis is as follows: Measurable Axis system transformation The axis system, the angle of transformation is The specific process is as follows: Based on the above formula derivation, we can obtain: in, The actual direct-axis voltage calculated for the phase-locked loop; Let t be time, and based on the convergence of the PI closed-loop control system, we find that as t→∞, the PI closed-loop control system stabilizes as follows: ， in, The initial angle of the resolver zero position calculated for the phase-locked loop.

2. The self-learning method for the initial angle of zero position of a permanent magnet synchronous motor resolver as described in claim 1, characterized in that: After the motor controller has stored the initial zero-position angle of the resolver, it sends a 0 speed request to the test bench. The test bench drives the motor to decelerate to 0. The motor controller then disables the self-learning function and sends a self-learning completion signal back to the test bench, indicating that self-learning is complete.

3. The self-learning method for the initial angle of zero position of a permanent magnet synchronous motor resolver as described in claim 2, characterized in that: Based on the zero-position deflection angle when the resolver is not zeroed, the analysis of the measured values ​​and true values ​​of the direct-axis voltage and quadrature-axis voltage includes: set up The axis system is a true dq axis with no zero-position deviation. The axis system is the dq axis with zero-position deviation; The actual dq axis voltage is only The measurable dq-axis voltage has a zero-point deviation in the direct-axis voltage. and quadrature axis voltage Therefore, the resulting zero-position deflection angle of the refractive index is as follows: in, The value is the zero-position deflection angle of the resolver.

4. A self-learning system for the initial zero-position angle of a permanent magnet synchronous motor resolver, using the method described in any one of claims 1-3, characterized in that, include: The sending module sends the resolver zero-position initial angle self-learning command to the motor controller; The receiving module, after receiving the self-learning instruction, enables the self-learning function and sets the target direct-axis current and target quadrature-axis current to 0; The calculation module is used to send a speed request command from the motor controller to the test bench. Upon receiving the speed request from the motor controller, the test bench drives the motor to a preset speed. After the motor controller detects that the speed has stabilized at the preset speed, it performs PI closed-loop control on the direct shaft voltage and calculates the initial angle of the resolver zero position using a phase-locked loop.

5. A computing device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the self-learning method for the zero-position initial angle of a permanent magnet synchronous motor resolver as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the self-learning method for the zero-position initial angle of a permanent magnet synchronous motor resolver as described in any one of claims 1 to 3.

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