A method and system for online full-rank parameter identification of surface-mounted permanent magnet synchronous motors

Abstract

The present invention provides a method and system for online parameter identification of full rank of surface-mounted permanent magnet synchronous motor, which relates to the technical field of synchronous motors. The present invention first realizes the stator resistance R by injecting low-frequency small current into the d-axis and deforming the motor d-axis voltage equation in this case. s Decoupling from other parameters, on this basis, the resistor R s As a known quantity, the inductance L is realized using the remaining voltage equations s Decoupling from flux linkage f , and finally obtain the identification model of the three parameters; finally, the forgetting factor least squares method recursive formula is combined with the identification model of the three parameters to realize the online dynamic identification of the motor parameters; the present invention can identify the resistance R online s 、Inductor L s 、Magnetic Link Ψ f Three parameters, and parameter identification does not depend on the working state of the motor, realizing real-time identification of dynamically changing motor parameters during the motor operation process.

Description

Technical Field

[0001] The present invention relates to the technical field of synchronous motors, and in particular to a method and system for identifying full-rank online parameters of a surface-mounted permanent magnet synchronous motor. Background Art

[0002] Permanent Magnetic Synchronous Motor (PMSM) is an AC servo motor whose main structure consists of two parts: stator coil and rotor permanent magnet. It is widely used in many industries due to its simple and reliable structure and excellent control characteristics.

[0003] The performance of permanent magnet synchronous motors (PMSMs) depends heavily on the matching between controller and motor parameters. Therefore, identifying motor parameters is crucial for improving performance and ensuring operational stability. Existing online parameter identification techniques can be broadly categorized into two main types:

[0004] The first type of method directly extracts the identification expression θ from the voltage equation and inputs The three formulas for outputting y have a working range covering all the working points of the motor, and there is no need to distinguish the working states. The first type of method does not depend on the special working state of the motor, but the identification model is lacking in rank and the motor parameters are coupled with each other. When the parameters change during the operation of the motor, it is difficult to identify the correct result. In addition, this type of method has the disadvantages of complex identification model and large amount of calculation, and the hardware cost of the algorithm implementation is relatively high.

[0005] The second type of method adds special operating points by injecting voltage / current, decouples the motor parameters, and identifies the corresponding parameters at different operating points. The second type of method has the advantage of a simple identification model, but it relies on the special operating state of the motor or has an impact on the operation of the motor (vibration and noise introduced by the injected current / voltage), and often has parameter coupling problems (the inductance Ls and the magnetic flux ψf are coupled with each other), making it impossible to identify the changing parameters.

[0006] Therefore, in order to solve the problem of parameter changes of surface-mounted permanent magnet synchronous motors during operation, it is necessary to find a reasonable identification model and corresponding identification method to perform real-time identification of the dynamically changing motor parameters during the operation of the motor. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a full-rank identification model suitable for the recursive least squares parameter method, which realizes the motor resistance R s 、Inductor L s 、Magnetic Link Ψ f The decoupling can effectively identify the changing parameters, achieve a certain accuracy, and is simple enough to have engineering application value.

[0008] The purpose of the present invention is achieved through the following technical solutions:

[0009] On the one hand, the present invention provides a full-rank online parameter identification system for a surface-mounted permanent magnet synchronous motor, which is used in a surface-mounted permanent magnet synchronous motor. The surface-mounted permanent magnet synchronous motor is provided with a controller, and the controller is provided with a current control loop.

[0010] It includes a sine wave generator and an identification unit; wherein,

[0011] The sine wave generator is connected to the d-axis current of the current control loop and is used to generate a low-frequency small current and inject it into the d-axis current;

[0012] The identification unit collects required parameter values from the current control loop and performs online parameter identification through the corresponding identification model; wherein the identification model includes a resistance identification model, an inductance identification model and a flux linkage identification model;

[0013] The resistance identification model obtains an identification expression input value and an identification expression output measurement value based on the d-axis current value and the d-axis voltage value;

[0014] The inductance identification model is based on the resistance identification value, combined with the d-axis current value, the d-axis voltage value, the q-axis current value and the motor speed value to obtain the identification expression input value and the identification expression output measurement value;

[0015] The flux linkage identification model obtains an identification expression input value and an identification expression output measurement value based on the resistance identification value, combined with the d-axis current value, the q-axis voltage value, the q-axis current value and the motor speed value.

[0016] As a further solution, the resistance identification model is set by the following formula:

[0017] LPF(u d (k)·sign(i d (k)))=R s LPF(i d (k)·sign(i d (k)));

[0018]

[0019] Among them, k represents the sampling point number of the d axis, u d (k) represents the d-axis voltage value at the k sampling point, i d (k) represents the d-axis current value at the k sampling point, is the input value of the identification expression of k sampling points, y(k) is the output measurement value of the identification expression of k sampling points; θ=R s Indicates that the object to be identified is resistance Rs ; LPF() represents a standard low-pass digital filter; sign() represents a sign function;

[0020] As a further solution, the inductance identification model is set by the following formula:

[0021]

[0022] in, is the resistance identification value, T s is the sampling point time interval value, ω e (k) represents the motor speed value at sampling point k, i q (k) represents the q-axis current value at sampling point k, θ = L s Indicates that the identification object is inductor L s .

[0023] As a further solution, the flux linkage identification model is set by the following formula:

[0024]

[0025] Among them, u q (k) represents the q-axis voltage value at sampling point k, θ = Ψ f Indicates that the identification object is magnetic flux Ψ f .

[0026] On the other hand, the present invention provides a method for online parameter identification of a full-rank surface-mounted permanent magnet synchronous motor, which is used in a full-rank online parameter identification system of a surface-mounted permanent magnet synchronous motor as described in any one of the above items, including a d-axis injection current generation step and a parameter identification step; wherein,

[0027] The d-axis injection current generating step comprises: generating the d-axis injection current by a sine wave generator according to a d-axis injection current calculation function; wherein the d-axis injection current calculation function is based on a cosine function and a time variable;

[0028] The parameter identification step: after injecting the d-axis injection current into the d-axis current, the identification expression input value and the identification expression output measurement value of each identification model of the identification unit are substituted into the least squares recursive formula respectively, so as to predict the identification value corresponding to each identification object.

[0029] As a further solution, the d-axis injection current calculation function is set by the following formula:

[0030]

[0031] t = t + ΔT;

[0032] in, represents the d-axis injection current, I d represents the d-axis injection current amplitude, f0 represents the d-axis injection current frequency, ΔT represents the calculation period, and t represents the cosine function time variable.

[0033] As a further solution, the least squares recursive formula is set by the following formula:

[0034]

[0035] in, represents the identification value corresponding to the object identified at k sampling points, K(k) represents the adaptive coefficient matrix of k sampling points, is the input value of the identification expression of k sampling point, y(k) is the output measurement value of the identification expression of k sampling point, λ is the forgetting factor, and P(k) represents the covariance corresponding to the identification object at k sampling point.

[0036] Compared with related technologies, the method and system for online full-rank parameter identification of a surface-mounted permanent magnet synchronous motor provided by the present invention have the following advantages:

[0037] 1. The present invention can identify the resistance R online during the motor operation process. s 、Inductor L s 、Magnetic Link Ψ f Three parameters.

[0038] 2. The identification method provided by the present invention realizes the resistance R s 、Inductor L s 、Magnetic Link Ψ f The decoupling between them allows the changes of each parameter to be identified independently.

[0039] 3. The identification method provided by the present invention does not depend on the working state of the motor. Changes in the working state of the motor will not affect the identification results, and the identification accuracy can reach 5%.

[0040] 4. The identification model provided by the present invention is simple, the output expression y(k), the input expression φ(k), and the identification expression θ are all scalars, the program has a small amount of computation and is easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a portion of the structure of the controller and sine wave generator provided by the present invention;

[0042] Figure 2 A schematic diagram of another part of the structure of the controller and sine wave generator provided by the present invention;

[0043] Figure 3 Schematic diagram of the structure of the identification unit provided by the present invention

[0044] Figure 4 A schematic diagram of the identification process provided by the present invention;

[0045] Figure 5 Schematic diagram of the steps for generating the d-axis injection current provided by the present invention;

[0046] Figure 6 A schematic diagram of the parameter identification steps of the resistance identification module provided by the present invention;

[0047] Figure 7 A schematic diagram of the parameter identification steps of the inductance identification module provided by the present invention;

[0048] Figure 8 A schematic diagram of the parameter identification steps of the flux identification module provided by the present invention;

[0049] Figure 9 This is a schematic diagram of the d-axis current waveform after the sinusoidal current is injected provided by the present invention;

[0050] Figure 10 A schematic diagram of the motor operation process provided in the first embodiment of the present invention;

[0051] Figure 11 A schematic diagram of the motor operation process provided in the second specific embodiment of the present invention;

[0052] Figure 12 This is a schematic diagram of the dynamic process of parameter identification results provided in the second specific embodiment of the present invention. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0054] Example 1

[0055] See also Figures 1 to 3 The present invention provides a full-rank online parameter identification system for a surface-mounted permanent magnet synchronous motor, which is used in a surface-mounted permanent magnet synchronous motor. The surface-mounted permanent magnet synchronous motor is provided with a controller, and the controller is provided with a current control loop.

[0056] It includes a sine wave generator and an identification unit; wherein,

[0057] The sine wave generator is connected to the d-axis current of the current control loop and is used to generate a low-frequency small current and inject it into the d-axis current;

[0058] The identification unit collects required parameter values from the current control loop and performs online parameter identification through the corresponding identification model; wherein the identification model includes a resistance identification model, an inductance identification model and a flux linkage identification model;

[0059] The resistance identification model obtains an identification expression input value and an identification expression output measurement value based on the d-axis current value and the d-axis voltage value;

[0060] The inductance identification model is based on the resistance identification value, combined with the d-axis current value, the d-axis voltage value, the q-axis current value and the motor speed value to obtain the identification expression input value and the identification expression output measurement value;

[0061] The flux linkage identification model obtains an identification expression input value and an identification expression output measurement value based on the resistance identification value, combined with the d-axis current value, the q-axis voltage value, the q-axis current value and the motor speed value.

[0062] It should be noted that: Figures 1 to 2 Together they represent the inherent current control loop ① of the controller. The newly added module in this embodiment is the sine wave generator ② ( Figure 2 ) and parameter identification unit ③( Figure 3 As shown); the sine wave generator ② is used to inject the excitation current in the q-axis, and the parameter identification unit ③ is composed of the resistance identification module ④, the inductance identification module ⑤ and the flux linkage identification module ⑥; during operation:

[0063] The ④ input of the resistance identification module is the d-axis voltage u d and current i d , the identification output is During the identification process, a sine wave generator is required to generate a low-frequency small current inj(i d ) is injected into the d-axis; the inductance identification module ⑤ input is the d-axis voltage u d , q-axis current i q 、Motor speedω e and resistance identification value The output is the inductance identification value The input of the flux identification module ⑥ is the q-axis voltage u q , d-axis current i d , q-axis current i q 、Motor speedω e and resistance identification value The output is the flux identification value

[0064] Identification of motor parameters can be viewed as a process of finding the minimum value with the deviation between the measured value and the true value as the objective function or fitness function. Figure 4As shown; where f(θ) is the identification expression, φ is the input value of the identification expression, θ0 is the real parameter of the system, is the system prediction parameter obtained by the identification method, y is the output measurement value of the identification expression, e is the measurement error, is the predicted value output by the identification expression, is the difference between the measured and predicted output values.

[0065] The least squares method is a regression estimation algorithm. Its core idea is to calculate the deviation between the predicted value and the actual value. When the deviation is the smallest, the current predicted value is considered to be the optimal result. Among them, the core idea of the recursive least squares method is to convert the current predicted value into the actual value. Represents the last predicted value and the sum of the correction value δ(k) (Formula 1.7):

[0066]

[0067] After deduction, we can get the recursive formula (Formula 1.8):

[0068]

[0069] In the above formula, K is the adaptive coefficient matrix, and the meaning of the formula can be understood as:

[0070] is the current system output prediction value The current system measurement value y(k) minus the current system output prediction value Indicates the deviation of the predicted value of the system output at the current moment

[0071] The deviation of the system output prediction value at the current moment Multiply by the adaptive coefficient matrix K(k) at the current moment, and then add the system parameter prediction value at the previous moment It is the predicted value of the system parameters at the current moment

[0072] The adaptive coefficient matrix K is the deviation of the evaluation prediction value value factor.

[0073] Define the covariance matrix (Equation 1.9):

[0074]

[0075] Continuing to derive the recursive formulas for the coefficient matrix K and the covariance matrix P, we can get the recursive least squares calculation formula (Formula 1.10):

[0076]

[0077] In the above formula, is the predicted value of the system parameter, φ is the system input, and y is the measured value of the system output.

[0078] At the beginning of the recursion: 1) take the initial value P(0) = aI n , a is a large positive real number (10 4 ~10 6 ), n is the matrix order; 2) take the initial value ε is a small positive real number or 0.

[0079] Compared with the classical least squares method, the recursive least squares method has greater practical application value, based on:

[0080] 1. The recursive least squares method only depends on the current state and the previous state of the system, and the amount of data is small;

[0081] 2. Each time, only the iterative formula, coefficient matrix K and covariance matrix P need to be calculated, and the amount of calculation is small.

[0082] However, the classical least squares method and the recursive least squares method are used to identify the constant unknown parameters. For slowly changing motor parameters, such least squares methods will have the problem of "data saturation" due to the increasing amount of collected data. That is, as k continues to increase, P(k) and K(k) will show a trend of gradually decreasing, making The convergence ability of the algorithm becomes weaker and the changed motor parameters cannot be correctly identified.

[0083] In order to solve the data saturation problem, the least squares evaluation formula is modified as (Formula 1.11):

[0084]

[0085] In the above formula, λ is the forgetting factor, 0≤λ≤1.

[0086] By introducing the forgetting factor, the evaluation function weights the collected data over time. The weight of the last collected data is 1, and the weight of the earliest collected data is λ L-1 .

[0087] Refer to the derivation process of the recursive least squares method for derivation. The recursive formula of the forgetting factor recursive least squares method is (Formula 1.12):

[0088]

[0089] The forgetting factor λ is generally a positive number close to 1, usually not less than 0.9. If it is a linear coefficient, it should be selected as 0.95≤λ≤1. When λ=1, the forgetting factor recursive least squares method degenerates into the ordinary recursive least squares method.

[0090] Compared with the recursive least squares method, the forgetting factor recursive least squares method maintains the simplicity of the algorithm itself while introducing a time weighting coefficient to enable the identification model to automatically adjust its own state to adapt to changes in system parameters. Therefore, this method is particularly suitable for identifying the parameters of time-varying systems such as motors.

[0091] The voltage equations of the permanent magnet synchronous motor d and q axes in the rotor synchronous rotating coordinate system are (Equation 1.13):

[0092]

[0093] In the above formula, u d 、u q 、i d 、i q is the d-axis and q-axis voltage and current, ω e is the motor speed. These are system measurement values. Voltage and current can be obtained by sampling the motor controller circuit, and speed can be measured by position / speed sensor or observed by observer; R s , L d , L q , Ψ f These are the motor stator resistance, d-axis stator inductance, q-axis stator inductance, and rotor permanent magnet flux, and are the parameter identification objects.

[0094] For surface mounted permanent magnet synchronous motors, there is L s =L d =Lq, the voltage equation of the surface-mount permanent magnet synchronous motor is (Equation 1.14):

[0095]

[0096] From the voltage equation, the parameter identification expression θ, input expression φ, and output expression y can be extracted as follows:

[0097]

[0098] In the above formula, f y 、f φ 、f θ It is a function of the measured value, the measured value vector, and the motor parameter vector, and has different forms depending on the specific measurement method.

[0099] After obtaining θ, φ, and y, substitute them into the least squares recursive formula and iterate step by step to obtain the identification results of the motor parameters.

[0100] For the resistance identification model:

[0101] The d-axis current waveform after the sinusoidal current is injected is as follows: Figure 9As shown, let the number of sampling points in half a cycle be N, and mark these sampling points in half a cycle.

[0102] Introduce the symbolic function (Equation 4.1):

[0103]

[0104] There is the following relationship (Formula 4.2):

[0105]

[0106] In the above formula, i d (k) represents i d (k) difference.

[0107] Write the d-axis voltage equation of Equation 1.14 in discrete form (Equation 4.3):

[0108] u d (k) = R s i d (k)+L s i d (k)-L s ω e i q

[0109] Multiply both sides by i d (k) the sign function sign(i d (k)), we have (Formula 4.4):

[0110] u d (k)·sign(i d (k))=[R s i d (k)+L s i d (k)-L s ω e i q ]·sign(i d (k))

[0111] The left and right ends of the above formula are averaged for all N sampling points, and we have (Formula 4.5):

[0112]

[0113] Substituting a low-pass filter for the averaging operation in the above formula, we can obtain (Formula 4.6):

[0114] LPF(u d (k)·sign(i d (k)))=R s LPF(i d(k)·sign(i d (k)))

[0115] In the above formula, LPF() is a standard low-pass digital filter.

[0116] The resistance identification model can be extracted as (Equation 4.7):

[0117]

[0118] In the above formula, y(k) and φ(k) are both scalars, and θ is the identification object R s .

[0119] For the inductance identification model:

[0120] Consider the d-axis voltage equation in Equation 1.14 and change the resistance identification value ^R s As a known quantity, the transformation can be obtained (Equation 4.8):

[0121]

[0122] Written in discrete form (Equation 4.9):

[0123]

[0124] The inductance identification model can be obtained (Equation 4.10):

[0125]

[0126] In the above formula, y(k) and φ(k) are both scalars, and θ is the identification object L s .

[0127] For the magnetic flux identification model:

[0128] When the motor is working in steady state, the differential term in Equation 1.14 can be ignored, and the resistance identification value As a known quantity, the deformation can be obtained (Equation 4.11):

[0129]

[0130] Transforming the d-axis voltage equation, we get ω e L s The expression of (Equation 4.12):

[0131]

[0132] Substituting back into the q-axis voltage equation, we have (Equation 4.13):

[0133]

[0134] The deformation can be obtained (Equation 4.14):

[0135]

[0136] Written in discrete form (Equation 4.15):

[0137]

[0138] The magnetic flux identification model (Equation 4.16) can be obtained:

[0139]

[0140] In the above formula, y(k) and φ(k) are both scalars, and θ is the identification object Ψ f .

[0141] Example 2

[0142] A method for online parameter identification of a full-rank surface-mounted permanent magnet synchronous motor is used in a full-rank surface-mounted permanent magnet synchronous motor online parameter identification system described in Example 1, comprising a d-axis injection current generation step and a parameter identification step; wherein,

[0143] The d-axis injection current generating step comprises: generating the d-axis injection current by a sine wave generator according to a d-axis injection current calculation function; wherein the d-axis injection current calculation function is based on a cosine function and a time variable;

[0144] The parameter identification step: after injecting the d-axis injection current into the d-axis current, the identification expression input value and the identification expression output measurement value of each identification model of the identification unit are substituted into the least squares recursive formula respectively, so as to predict the identification value corresponding to each identification object.

[0145] It should be noted that the steps for generating the d-axis injection current are as follows: Figure 5 As shown, generally speaking, in order not to affect the operation of the motor, I d It does not exceed 10% of the given q-axis current, and f0 does not exceed 10Hz. For the parameter identification step, we define the parameter symbols as shown in Table 1:

[0146] Table 1 Parameter symbol definition table

[0147]

[0148] Take the covariance P R 、P L 、P Ψ The initial value> 0, the forgetting factor λ R ,λ L ,λ Ψ ∈(0.9,1.0), the specific value needs to be adjusted according to the application scenario.

[0149] Figure 6 Further expanding the parameter identification steps of the resistance identification module, the newly added symbols are summarized in Table 2:

[0150] Table 2 Definition table of new symbols for resistance identification module

[0151]

[0152] Among them, y and The calculation formula is obtained based on the resistance identification model of formula 4.7, the adaptive coefficient K, covariance P R , identification value The calculation formula of is obtained according to the least squares recursive formula of the forgetting factor in formula 1.12.

[0153] Figure 7 The parameter identification steps of the inductance identification module are further expanded, and the newly added symbols are summarized in Table 3:

[0154] Table 3 New symbol definitions for the inductance identification module

[0155]

[0156] Figure 8 Further expand the parameter identification steps of the flux identification module; where y and The calculation formula is obtained based on the flux identification model of formula 4.16, the adaptive coefficient K, covariance P Ψ , identification value The calculation formula is obtained based on the least squares recursive formula of the forgetting factor in formula 1.12.

[0157] During the specific implementation process, we conducted simulation tests.

[0158] The surface-mounted permanent magnet synchronous motor is selected as the control object, and i d =0 for field oriented control, the control mode is speed servo, and the motor parameters are as shown in Table 4:

[0159] Table 4 Motor parameters

[0160]

[0161] Motor load torque T L (N·m) is about the speed ω e (rad / s), the relationship is as follows (Equation 5.1):

[0162] T L =ω e / 1000 Specific embodiment 1

[0164] The motor runs at a constant speed (1000 rpm, 523.6 rad / s) and the motor parameters are changed during operation:

[0165] Resistor R s It starts changing from 18 seconds, shrinks to 0.5 times the initial value after 2 seconds and remains constant;

[0166] Inductor L s The change starts at 8 seconds, increases to 5 times the initial value after 2 seconds and remains constant;

[0167] Magnetic Link Ψ f It starts changing at 28 seconds, shrinks to 0.5 times the initial value after 2 seconds, and remains constant.

[0168] During the identification process, current is injected into the d-axis, the amplitude of the injected current is 1 / 10 of the given q-axis current, and the frequency is 10 Hz.

[0169] Identification model covariance P R 、P L 、P Ψ The initial value is 10, and the forgetting factor λ R ,λ L ,λ Ψ Both are 0.9999.

[0170] The motor operation process is as follows Figure 10 As shown:

[0171] The three figures above show the motor's d- and q-axis currents, while the two figures on the right are zoomed-in time-scales of the left image. The q-axis current is automatically adjusted by the PI control loop based on load changes. The d-axis current harmonic component is the injection current required for parameter identification, and the DC component is zero. Because the motor flux changes (reduced by half) between 28 and 30 seconds, the speed control loop increases the q-axis current reference by a factor of two to maintain a constant speed.

[0172] The two figures below show the motor speed. The right figure is an amplification of the left figure in the amplitude direction. Between 28 and 30 seconds, the speed fluctuates due to changes in the motor magnetic flux. After the magnetic flux stabilizes, the speed returns to the target speed and remains stable. Specific embodiment 2

[0174] The motor changes its speed during operation while keeping its own parameters unchanged:

[0175] The motor was first run at 1000 rpm (523.6 rad / s) for 10 seconds.

[0176] Starting from 10 seconds, the motor speed gradually increases, reaching 1500 rpm (785.4 rad / s) at 15 seconds, and maintains the current speed for 10 seconds.

[0177] Starting from 25 seconds, the motor speed gradually decreases, reaching 1250rpm (654.5rad / s) at 30 seconds, and continues to run at the current speed.

[0178] During the identification process, current is injected into the d-axis, the amplitude of the injected current is 1 / 10 of the given q-axis current, and the frequency is 10 Hz.

[0179] Identification model covariance P R 、P L 、P Ψ The initial value is 10, and the forgetting factor λ R ,λ L ,λ Ψ Both are 0.9999.

[0180] The motor operation process is as follows Figure 11 As shown:

[0181] On the left are the motor's d- and q-axis currents; the q-axis current is automatically adjusted by the PI regulation loop based on load changes. The d-axis current harmonic component is the injection current required for parameter identification, and the DC component is 0.

[0182] On the right is the motor speed.

[0183] The dynamic process of parameter identification results is as follows Figure 12 As shown, the three figures on the left are the dynamic comparison between the parameter identification value and the actual value, and the three figures on the right are the dynamic comparison between the normalized value of the identification error and the fixed value of 5%.

[0184] It can be seen that:

[0185] The motor parameter identification values can converge to stable values;

[0186] The motor parameter identification values fluctuate slightly as the motor operating point changes;

[0187] During the change of the motor working state, the identification error fluctuates slightly, but remains below 5%.

[0188] The above embodiments merely represent preferred implementations, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art will be able to make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements are all within the scope of protection of the present invention.

Claims

1. A full-rank online parameter identification system for a surface-mounted permanent magnet synchronous motor, used in a surface-mounted permanent magnet synchronous motor, wherein the surface-mounted permanent magnet synchronous motor is provided with a controller, and the controller is provided with a current control loop, characterized in that: It includes a sine wave generator and an identification unit; wherein, The sine wave generator is connected to the d-axis current of the current control loop and is used to generate a low-frequency small current and inject it into the d-axis current; The identification unit collects required parameter values from the current control loop and performs online parameter identification through the corresponding identification model; wherein the identification model includes a resistance identification model, an inductance identification model and a flux linkage identification model; The resistance identification model obtains an identification expression input value and an identification expression output measurement value based on the d-axis current value and the d-axis voltage value; The inductance identification model is based on the resistance identification value, combined with the d-axis current value, the d-axis voltage value, the q-axis current value and the motor speed value to obtain the identification expression input value and the identification expression output measurement value; The flux linkage identification model is based on the resistance identification value, combined with the d-axis current value, the q-axis voltage value, the q-axis current value and the motor speed value to obtain the identification expression input value and the identification expression output measurement value; The resistance identification model is set by the following formula: ； ； ； in, Indicates the d-axis sampling point number, express The d-axis voltage value at the sampling point, express The d-axis current value at the sampling point, for The input value of the identification expression of the sampling point, for The identification expression of the sampling point outputs the measured value; Indicates that the object to be identified is a resistor ; Represents a standard low-pass digital filter; represents a symbolic function; The inductance identification model is set by the following formula: ； ； in, is the resistance identification value, is the sampling point time interval value, express The motor speed value at the sampling point, express The q-axis current value at the sampling point, Indicates that the identification object is an inductor ; The flux linkage identification model is set by the following formula: ； ； in, express The q-axis voltage value at the sampling point, Indicates that the identification object is a magnetic link .

2. A method for online parameter identification of a full-rank surface-mounted permanent magnet synchronous motor, used in a full-rank surface-mounted permanent magnet synchronous motor online parameter identification system according to claim 1, characterized in that: It includes a d-axis injection current generation step and a parameter identification step; wherein, The d-axis injection current generating step comprises: generating the d-axis injection current by a sine wave generator according to a d-axis injection current calculation function; wherein the d-axis injection current calculation function is based on a cosine function and a time variable; The parameter identification step: after injecting the d-axis injection current into the d-axis current, the identification expression input value and the identification expression output measurement value of each identification model of the identification unit are substituted into the least squares recursive formula respectively, so as to predict the identification value corresponding to each identification object.

3. The method for online full-rank parameter identification of a surface-mounted permanent magnet synchronous motor according to claim 2, characterized in that: The d-axis injection current calculation function is set by the following formula: ； ； in, represents the d-axis injection current, represents the d-axis injection current amplitude, represents the d-axis injection current frequency, represents the calculation cycle, Represents the cosine function time variable.

4. The method for online full-rank parameter identification of a surface-mounted permanent magnet synchronous motor according to claim 2, characterized in that: The least squares recursive formula is set by the following formula: ； in, Indicates The identification value corresponding to the identification object of the sampling point, express Adaptive coefficient matrix of sampling points, for The input value of the identification expression of the sampling point, for The identification expression of the sampling point outputs the measured value, For the forgetting factor, Indicates The covariance corresponding to the object identified by the sampling points.

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