Parameter online identification method, device and equipment of permanent magnet synchronous motor and medium

Abstract

The application discloses a kind of permanent magnet synchronous motor parameter online identification method, device, equipment and medium, method includes: using the determination of motor's direct-axis inductance and cross-axis inductance by pulsating high-frequency injection method;Obtain the first stator resistance and first permanent magnet flux linkage identified using offline identification method;Based on direct-axis inductance, cross-axis inductance, first stator resistance, and pre-established adjustable model and adaptive rate algorithm, the stator resistance of motor is identified online;Based on direct-axis inductance, cross-axis inductance, first permanent magnet flux linkage, identified stator resistance and pre-established adjustable model and adaptive rate algorithm, the permanent magnet flux linkage of motor is identified online.Through pulsating high-frequency injection method, realize motor inductance online identification, combined with offline identification and adaptive algorithm, realize in permanent magnet synchronous motor control system without temperature sensor, accurately online identification stator resistance and permanent magnet flux linkage, improve motor parameter identification accuracy and precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric machines, and particularly relates to a parameter online identification method, device and equipment of a permanent magnet synchronous motor and a medium. BACKGROUND

[0002] As a core component of modern electric drive systems, permanent magnet synchronous motors (PMSMs) have been widely used in electric vehicles, industrial automation and aerospace fields due to their high energy efficiency, high power density and excellent speed regulation performance. PMSMs use permanent magnets to generate a magnetic field, which interacts with armature current to produce torque, achieving efficient conversion of electrical energy to mechanical energy. With the rapid development of the electric vehicle industry, higher requirements are placed on the control accuracy and dynamic response capability of permanent magnet synchronous motors, especially in terms of torque control and speed regulation performance.

[0003] To achieve high-performance motor control, key motor parameters such as D-axis inductance, Q-axis inductance, stator resistance and permanent magnet flux linkage need to be accurately identified. These motor parameters directly affect the steady-state and dynamic performance of the motor and are the basis for building an accurate control strategy. Currently, motor parameter identification methods are mainly divided into online identification and offline identification. Offline identification methods are usually performed under motor static or specific operating conditions, although they are simple to operate, but when the actual operating conditions of the motor differ significantly from the offline identification conditions, the true value of the identification result often deviates, resulting in a decrease in control accuracy. Online identification methods can update parameter values in real time during motor operation to adapt to changes in operating conditions. Existing online identification methods include least squares and model reference adaptive identification methods.

[0004] However, existing online identification methods have the problem of high coupling between parameters, poor identification accuracy and precision, and because the stator resistance in the to-be-identified parameters is closely related to the stator temperature, a temperature sensor is usually arranged in the stator of the permanent magnet synchronous motor to collect the stator temperature and calculate the stator resistance, thereby increasing the hardware setup cost of the temperature sensor.

[0005] Therefore, how to reduce the coupling between the to-be-identified motor parameters in a permanent magnet synchronous motor control system without a temperature sensor, improve the accuracy and precision of motor parameter identification, and meet the high-performance speed regulation and torque requirements of the motor is a technical problem that needs to be solved at present. SUMMARY

[0006] In view of the above problems, this invention is proposed to provide a method, apparatus, device, and medium for online parameter identification of a permanent magnet synchronous motor that overcomes or at least partially solves the above problems. The invention achieves online identification of motor inductance through a pulsed high-frequency injection method, and combines offline identification with an adaptive algorithm to accurately identify stator resistance and permanent magnet flux linkage in a permanent magnet synchronous motor control system without temperature sensors. This improves the accuracy and precision of motor parameter identification, optimizes control performance, and meets the high-performance speed regulation and torque requirements of the motor.

[0007] Firstly, an online parameter identification method for a permanent magnet synchronous motor is provided, the method comprising:

[0008] The direct-axis inductance and quadrature-axis inductance of the motor were determined using the pulsed high-frequency injection method.

[0009] Obtain the first stator resistor and the first permanent magnet flux identified using an offline identification method;

[0010] Based on the direct-axis inductor, the quadrature-axis inductor, the first stator resistance, and the pre-established adjustable stator resistance bias identification model and stator resistance identification adaptive rate algorithm, the stator resistance of the motor is identified online.

[0011] Based on the direct-axis inductor, the quadrature-axis inductor, the first permanent magnet flux linkage, the identified stator resistance, and the pre-established adjustable model for rotor permanent magnet flux linkage identification and the adaptive rate algorithm for rotor permanent magnet flux linkage identification, the permanent magnet flux linkage of the motor is identified online.

[0012] Optionally, the stator resistance bias identification adjustable model has input parameters, output parameters, and adjustable parameters. The input parameters include the direct-axis inductance, the quadrature-axis inductance, and the first stator resistance. The output parameters include the observed values ​​of the direct-axis current and the quadrature-axis current. The adjustable parameters include the resistance bias.

[0013] The stator resistor identification adaptive rate algorithm is used to adjust the resistor bias based on the observed values ​​of the direct-axis current and the quadrature-axis current, so that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

[0014] Optionally, the online identification of the motor's stator resistance based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, and a pre-established adjustable stator resistance bias identification model and a stator resistance identification adaptive rate algorithm includes:

[0015] The direct-axis inductor, the quadrature-axis inductor, and the first stator resistor are input into the stator resistor bias identification adjustable model, and the observed values ​​of the direct-axis current and the quadrature-axis current are output.

[0016] Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured value and the observed value of the direct-axis current, and the second difference between the actual measured value and the observed value of the quadrature-axis current;

[0017] When the first difference or the second difference is not equal to 0, based on the stator resistor identification adaptive rate algorithm, the stator resistor bias in the stator resistor bias identification adjustable model is adjusted until the first difference and the second difference are both equal to 0.

[0018] When both the first difference and the second difference are equal to 0, the stator resistance bias in the adjustable model of stator resistance bias identification is denoted as the target bias.

[0019] The stator resistance of the motor is determined based on the target bias and the first stator resistance.

[0020] Optionally, the rotor permanent magnet flux linkage bias identification adjustable model has input parameters, output parameters, and adjustable parameters. The input parameters include the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux linkage, and the identified stator resistance. The output parameters include the observed values ​​of the direct-axis current and the quadrature-axis current. The adjustable parameters include the permanent magnet flux linkage bias.

[0021] The rotor permanent magnet flux linkage identification adaptive rate algorithm is used to adjust the permanent magnet flux linkage bias based on the observed values ​​of the direct-axis current and the quadrature-axis current, so that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

[0022] Optionally, the online identification of the permanent magnet flux linkage of the motor based on the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux linkage, the identified stator resistance, and a pre-established adjustable model for rotor permanent magnet flux linkage identification and an adaptive rate algorithm for rotor permanent magnet flux linkage identification includes:

[0023] The direct-axis inductor, the quadrature-axis inductor, the first permanent magnet flux linkage, and the identified stator resistance are input into the rotor permanent magnet flux linkage bias identification adjustable model, and the observed values ​​of the direct-axis current and the quadrature-axis current are output.

[0024] Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured value and the observed value of the direct-axis current, and the second difference between the actual measured value and the observed value of the quadrature-axis current;

[0025] When the first difference or the second difference is not equal to 0, based on the rotor permanent magnet flux linkage identification adaptive rate algorithm, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is adjusted until both the first difference and the second difference are equal to 0.

[0026] When both the first difference and the second difference are equal to 0, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is denoted as the target flux linkage deviation.

[0027] The permanent magnet flux linkage of the motor is determined based on the target flux linkage bias and the first permanent magnet flux linkage.

[0028] Optionally, the pulsed high-frequency injection method is used to determine the direct-axis inductance and quadrature-axis inductance of the motor, including:

[0029] A pulsating high-frequency voltage is injected into the direct shaft of the motor to obtain the maximum value of the direct shaft high-frequency current, and the direct shaft inductance is determined based on the maximum value of the direct shaft high-frequency current.

[0030] The pulsating high-frequency voltage is injected into the quadrature axis of the motor to obtain the maximum value of the quadrature axis high-frequency current, and the quadrature axis inductance is determined based on the maximum value of the quadrature axis high-frequency current.

[0031] Optionally, before obtaining the first stator resistance and the first permanent magnet flux identified by the offline identification method, the method further includes:

[0032] A stall test is performed on the motor, and the first stator resistance is identified offline based on the stall test parameters. The first stator resistance is the stator resistance of the motor stator at a set temperature.

[0033] A reverse drag test is performed on the motor, and the first permanent magnet flux is identified offline based on the reverse drag test parameters. The first permanent magnet flux is the permanent magnet flux of the motor rotor at a set temperature.

[0034] Secondly, an online parameter identification device for a permanent magnet synchronous motor is provided, the device comprising:

[0035] The inductance identification module is used to determine the direct-axis inductance and quadrature-axis inductance of the motor using the pulsed high-frequency injection method.

[0036] The parameter acquisition module is used to acquire the first stator resistance and the first permanent magnet flux identified by the offline identification method.

[0037] The resistance identification module is used to identify the stator resistance of the motor online based on the direct-axis inductor, the quadrature-axis inductor, the first stator resistance, and a pre-established adjustable stator resistance bias identification model and a stator resistance identification adaptive rate algorithm.

[0038] The flux linkage identification module is used to identify the permanent magnet flux linkage of the motor online based on the direct-axis inductor, the quadrature-axis inductor, the first permanent magnet flux linkage, the identified stator resistance, and a pre-established adjustable model for rotor permanent magnet flux linkage identification and an adaptive rate algorithm for rotor permanent magnet flux linkage identification.

[0039] Thirdly, an electronic device is provided, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the online parameter identification method as described in the first aspect.

[0040] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing the computer to perform the online parameter identification method as described in the first aspect.

[0041] The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0042] This invention provides an online identification method, apparatus, device, and medium for permanent magnet synchronous motor parameters. By employing a pulsed high-frequency injection method, the direct-axis inductance and quadrature-axis inductance of the motor are determined. The high-frequency signal injected by this method has no impact on the fundamental wave control, and the injected voltage and sampling current are independent of the stator resistance and rotor permanent magnet flux linkage. This characteristic decouples the identification results of time-varying current parameters (inductance) from those of time-varying temperature parameters (stator resistance and permanent magnet flux linkage), significantly improving the accuracy and precision of parameter identification. Furthermore, this invention pre-acquires the first stator resistance and first permanent magnet flux linkage determined by an offline identification method, and combines this with the inductance parameters identified by the pulsed high-frequency injection method, along with a pre-established adjustable model and adaptive rate algorithm, to achieve accurate online identification of the stator resistance and rotor permanent magnet flux linkage during motor operation. In this process, the first stator resistance and first permanent magnet flux linkage determined offline are used as feedforwards for the adjustable model. An adaptive rate algorithm adjusts the deviations in the first stator resistance and first permanent magnet flux linkage caused by temperature variations. This not only avoids deviations in offline identification results due to changes in operating conditions but also eliminates the need for additional temperature sensors, reducing hardware costs. Furthermore, this model-referenced adaptive method effectively reduces the coupling between the stator resistance and rotor permanent magnet flux linkage identification results, further improving the accuracy and precision of motor parameter identification, optimizing motor control performance, and enabling it to respond more accurately to changes in operating conditions to meet the high-performance speed regulation and torque requirements of the motor.

[0043] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0045] Figure 1 This is a flowchart of an online parameter identification method for a permanent magnet synchronous motor provided in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the principle of a pulsed high-frequency injection method provided in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram illustrating the online identification principle of stator resistance and permanent magnet flux provided in an embodiment of the present invention;

[0048] Figure 4 This is a structural block diagram of an online parameter identification device for a permanent magnet synchronous motor provided in an embodiment of the present invention. Detailed Implementation

[0049] To better understand the above technical solutions, the following will describe the above technical solutions in detail with reference to the accompanying drawings and specific implementation methods. It should be understood that the embodiments of this disclosure and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. Unless otherwise specified, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0050] Figure 1 This is a flowchart of an online parameter identification method for a permanent magnet synchronous motor provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes:

[0051] Step S110: Use the pulsed high-frequency injection method to determine the direct-axis inductance and quadrature-axis inductance of the motor.

[0052] In some implementations, step S110 includes:

[0053] Inject pulsating high-frequency voltage into the direct axis (i.e., D-axis) of the motor, obtain the maximum value of the direct axis high-frequency current, and determine the direct axis inductance based on the maximum value of the direct axis high-frequency current;

[0054] Inject pulsating high-frequency voltage into the quadrature axis (i.e., Q-axis) of the motor, obtain the maximum value of the quadrature axis high-frequency current, and determine the quadrature axis inductance based on the maximum value of the quadrature axis high-frequency current.

[0055] In some implementations, a high-frequency sinusoidal voltage signal can be injected into the D-axis, and the high-frequency current signal of the D-axis can be extracted through a bandpass filter, with the maximum value of the high-frequency current of the D-axis recorded. The high-frequency voltage injected into the D-axis and the high-frequency current extracted are shown in formulas (1) and (2) below:

[0056] u dhin =u in ·cos(ω in *t); (1)

[0057] i dhin =BPF(i dref (2)

[0058] Among them, u dhin For the injected pulsating high-frequency voltage, u in ω represents the voltage amplitude of the pulsating high-frequency voltage. in i is the voltage frequency of the pulsed high-frequency voltage; dref The current is the D-axis current, BPF is a bandpass filter, and i dhin This is the high-frequency current along the D-axis.

[0059] From the above formulas (1) and (2), the formula for calculating the high-frequency current of the D-axis can be obtained as follows: Formula (3):

[0060]

[0061] Among them, L d It is the D-axis inductance.

[0062] Based on the above formulas (1) to (3), the formulas for calculating the D-axis inductance are as follows: formulas (4) and (5):

[0063] i dhmax =max(i dhin (4)

[0064]

[0065] Among them, i dhmax This represents the maximum value of the high-frequency current along the D-axis.

[0066] As can be seen from the above analysis, in some embodiments, the direct-axis inductance is determined based on the direct-axis high-frequency current, including:

[0067] The direct-axis inductance is determined based on the following formula (5):

[0068]

[0069] Among them, L d For a direct-axis inductor, u in ω represents the voltage amplitude of the pulsating high-frequency voltage. in i is the voltage frequency of the pulsed high-frequency voltage. dhmax This represents the maximum value of the direct-axis high-frequency current.

[0070] In some implementations, a high-frequency sinusoidal voltage signal can be injected into the Q-axis, and the high-frequency current signal of the Q-axis can be extracted through a bandpass filter, with the maximum value of the high-frequency current of the Q-axis recorded. The high-frequency voltage injected into the Q-axis and the high-frequency current extracted are shown in formulas (6) and (7) below:

[0071] u qhin =u in ·cos(ω in *t); (6)

[0072] i qhin =BPF(i qref (7)

[0073] Among them, u qhin For the injected Q-axis pulsating high-frequency voltage, u in ω represents the voltage amplitude of the pulsating high-frequency voltage. in i is the voltage frequency of the pulsed high-frequency voltage; qref i is the Q-axis current, BPF is the bandpass filter, and i qhin This is the high-frequency current along the Q-axis.

[0074] From the above formulas (6) and (7), the formula for calculating the Q-axis high-frequency current is as follows: Formula (8):

[0075]

[0076] Among them, L q It is the Q-axis inductance.

[0077] Based on the above formulas (6) to (8), the formulas for calculating the Q-axis inductance are as follows: formulas (9) and (10):

[0078] i qhmax =max(i qhin (9)

[0079]

[0080] Among them, i qhmax This represents the maximum value of the Q-axis high-frequency current.

[0081] As can be seen from the above analysis, in some embodiments, the quadrature-axis inductance is determined based on the quadrature-axis high-frequency current, including:

[0082] The quadrature-axis inductance is determined based on the following formula (10):

[0083]

[0084] Among them, L q For quadrature axis inductance, u in ω represents the voltage amplitude of the pulsating high-frequency voltage. in i is the voltage frequency of the pulsed high-frequency voltage. qhmax This represents the maximum value of the quadrature-axis high-frequency current.

[0085] Figure 2 This is a schematic diagram illustrating the principle of a pulsed high-frequency injection method provided in an embodiment of the present invention, as shown below. Figure 2 As shown, this embodiment injects pulsating high-frequency voltage u into the D-axis current and Q-axis current of the motor alternately in different cycles. qhin and u qhin The motor output current is controlled by a current loop to reach the set value SV. Then, the inverter circuit converts the DC control signal output by the control system into AC power to drive the motor. This is achieved by extracting the three-phase current i from the inverter circuit. a i b and i c And perform coordinate transformation on it to obtain the D-axis current i dref and Q-axis current i qref Next, high-frequency current signals of the D-axis and Q-axis are extracted using a bandpass filter, and the maximum value i of the high-frequency current of the D-axis is recorded by amplitude extraction. dhmax and the maximum value of the Q-axis high-frequency current i qhmax , to be used to calculate the D-axis inductance and Q-axis inductance respectively.

[0086] Step S120: Obtain the first stator resistor and the first permanent magnet flux identified by the offline identification method.

[0087] In some implementations, the method may include, prior to performing step S120:

[0088] A stall test was performed on the motor, and the first stator resistance was identified based on the stall test parameters;

[0089] A reverse drag test was conducted on the motor, and the first permanent magnet flux linkage was identified based on the reverse drag test parameters.

[0090] In some implementations, performing a stall test on the motor may include: giving a quadrature-axis current equal to 0 and a direct-axis current equal to the motor's rated current, and using a temperature sensor arranged in the motor stator to obtain the corresponding D-axis voltage u when the motor stator is in a hot temperature state (e.g., 65°C). eGiven a quadrature-axis current of 0 and a direct-axis current equal to the motor's peak current, the peak D-axis voltage u is obtained when the motor stator is in a thermally stable state (e.g., 65°C) using a temperature sensor located in the motor stator. max Based on the following formula (11), the calculation formula for the stator resistance can be obtained using the difference method, as shown in the following formula (12):

[0091] u d =R s* i d (11)

[0092] Among them, u d For the D-axis voltage, i d R is the D-axis current. s This is the resistance along the D-axis.

[0093]

[0094] Among them, R s_65℃ The first stator resistance is the stator resistance of the motor when it is in a thermally stable state (65℃).

[0095] In the above implementation, the stall test parameters include at least the rated current and peak current of the motor, as well as the first direct-axis voltage and the second direct-axis voltage; wherein, the first direct-axis voltage is the direct-axis voltage when the motor stator reaches the set temperature during the stall test, when the given quadrature-axis current is equal to 0 and the given direct-axis current is equal to the rated current; the second direct-axis voltage is the direct-axis voltage when the motor stator reaches the set temperature during the stall test, when the given quadrature-axis current is equal to 0 and the given direct-axis current is equal to the peak current.

[0096] In some implementations, performing a reverse-drive test on the motor may include: placing the motor in a temperature chamber at a set temperature (e.g., 65°C), using a dynamometer to reverse-drive the motor under test, and recording the back electromotive force E_Speed ​​at the rated speed E_Speed ​​when the motor rotor is in a hot temperature state (e.g., 65°C) using temperature sensors arranged in the motor rotor. e_speed And the back electromotive force E of the motor at its peak speed Max_Speed. max_speed .

[0097] The relationship between back electromotive force and magnetic flux is given by the following formula (13):

[0098]

[0099] in, For permanent magnet flux linkage, E peak denoted as the peak value of the back electromotive force of the motor, n as the motor speed, and p as the number of pole pairs of the motor.

[0100] Based on the above formula (13), the size of the permanent magnet flux at a set temperature can be calculated using the difference method, as shown in the following formula (14):

[0101]

[0102] in, The permanent magnet flux linkage is the first permanent magnet flux linkage when the motor rotor is in a thermally stable state (65℃).

[0103] In the above implementation, the reverse drag test parameters include at least the rated speed and peak speed of the motor, as well as the first back electromotive force and the second back electromotive force; wherein, the first back electromotive force is the back electromotive force when the motor rotor is at a set temperature and the motor speed is equal to the rated speed during the reverse drag test; the second back electromotive force is the back electromotive force when the motor rotor is at a set temperature and the motor speed is equal to the peak speed during the reverse drag test.

[0104] It should be noted that in the above implementation, the set temperature is 65℃. In specific settings, those skilled in the art can also conduct relevant experiments based on the actual temperature of the motor under thermal stability to obtain the first stator resistance and the first electromagnetic flux. This invention does not limit this.

[0105] Step S130: Based on the direct-axis inductance, quadrature-axis inductance, first stator resistance, and the pre-established adjustable stator resistance bias identification model and stator resistance identification adaptive rate algorithm, the stator resistance of the motor is identified online.

[0106] In some implementations, the stator resistance bias identification adjustable model has input parameters, output parameters, and adjustable parameters. The input parameters include the direct-axis inductance L. d quadrature axis inductance L q and the first stator resistance R s_65℃ The output parameters include the observed values ​​of the direct-axis current. Observations of cross-axis current Adjustable parameters include resistance bias. Stator resistance identification adaptive rate algorithm for observations based on direct-axis and quadrature-axis currents. and Adjusting the resistance bias This ensures that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

[0107] For example, the adjustable model for identifying stator resistance bias (resistance bias caused by stator temperature deviation of 65℃) is shown below:

[0108]

[0109] in, To identify the observed D-axis current of the adjustable model output for stator resistance bias identification, R s_65℃ L is the first stator resistance. d The D-axis inductance identified in step S110, To identify the stator resistance bias obtained by the adaptive rate algorithm, ω e L is the electric angular velocity of the motor. q The Q-axis inductance identified in step S110, To identify the observed Q-axis current of the adjustable model output for stator resistance bias identification, u d The voltage across the D-axis, σ f The permanent magnet linkage identified in step S140.

[0110] Using the aforementioned adjustable stator resistance bias identification model, the first stator resistance (R) identified offline at a set temperature (e.g., 65°C) can be identified. s_65℃ bias resistance It is understandable that the first stator resistance in the above adjustable model is the stator resistance obtained offline at 65°C. When the offline identification temperature is not 65°C, the expression R for the corresponding first stator resistance in the above model will change. s_65℃ The expression for the stator resistance at other temperatures needs to be modified accordingly.

[0111] In some implementations, the permanent magnet flux σ identified in step S140 can be... f The stator resistance bias identification adjustable model is input as a feedback parameter. Based on the direct-axis inductance, quadrature-axis inductance, first stator resistance, permanent magnet flux linkage identified in step S140, and the stator resistance bias identification adjustable model and stator resistance identification adaptive rate algorithm, online identification of stator resistance bias is achieved. Simultaneously, the stator resistance identified in step S130 is used as a feedback parameter in the rotor permanent magnet flux linkage bias identification adjustable model and input into it. That is, each of the two adjustable models uses the identification value of the other as input, ensuring that each of the two adjustable models has only one unknown parameter to be identified. This solves the underrank problem of existing methods that use two voltage equations to simultaneously identify four motor parameters (D-axis inductance, Q-axis inductance, stator resistance, and permanent magnet flux linkage).

[0112] For example, the stator resistance identification adaptive rate algorithm is shown below:

[0113]

[0114] Among them, K' 11 To identify the integral coefficients for the stator resistance, K' p1 To identify the proportional coefficient for the stator resistance, a typical parameter is K'. p1 =15, K' 11=0.5. Δi d This is the first difference between the measured and observed values ​​of the D-axis current, i.e., the difference between the actual measured value of the motor's D-axis current and the observed value of the D-axis current output by the adjustable model for stator resistance bias identification. The difference between them; Δi q This is the second difference between the actual measured value and the observed value of the Q-axis current, i.e., the difference between the actual measured value of the motor's Q-axis current and the observed value of the Q-axis current output by the stator resistance bias identification adjustable model. The difference between them.

[0115] In some implementations, step S130 may include:

[0116] The direct-axis inductance, quadrature-axis inductance, and first stator resistance are input into the stator resistance bias identification adjustable model, and the observed values ​​of the direct-axis current and quadrature-axis current are output.

[0117] Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured and observed values ​​of the direct-axis current, and the second difference between the actual measured and observed values ​​of the quadrature-axis current;

[0118] When the first difference or the second difference is not equal to 0, the stator resistance bias in the adjustable model of stator resistance bias identification is adjusted based on the stator resistance identification adaptive rate algorithm until the first difference and the second difference are both equal to 0.

[0119] When both the first and second differences are equal to 0, the stator resistance bias identification in the adjustable model is recorded as the target bias.

[0120] The stator resistance of the motor is determined based on the target bias and the first stator resistance.

[0121] For example, target bias With the first stator resistor R s_65℃ The sum of these values ​​is the stator resistance R identified in step S130. s ,Right now

[0122] Step S140: Based on the direct-axis inductance, quadrature-axis inductance, first permanent magnet flux linkage, identified stator resistance, and pre-established rotor permanent magnet flux linkage bias identification adjustable model and rotor permanent magnet flux linkage identification adaptive rate algorithm, the permanent magnet flux linkage of the motor is identified online.

[0123] In some implementations, the rotor permanent magnet flux linkage deflection identification adjustable model has input parameters, output parameters, and adjustable parameters. The input parameters include the direct-axis inductance L. d quadrature axis inductance L q and the first permanent magnet chain And the stator resistance R identified in step S130s The output parameters include the observed values ​​of the direct-axis current. Observations of cross-axis current Adjustable parameters include permanent magnet flux linkage and flux deflection.

[0124] The rotor permanent magnet flux linkage identification adaptive rate algorithm is used based on observations of direct-axis and quadrature-axis currents. and Adjust permanent magnet flux linkage This ensures that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

[0125] For example, the adjustable model for identifying rotor permanent magnet flux linkage deviation (flux linkage deviation caused by rotor temperature deviating from 65℃) is shown below:

[0126]

[0127] in, The observed value of the D-axis current output by the rotor permanent magnet flux linkage deflection identification adjustable model, L d R is the D-axis inductance identified in step S110. s For the stator resistance identified in step S130, ω e L is the electric angular velocity of the motor. q The Q-axis inductance identified in S110, The observed value of the Q-axis current output by the rotor permanent magnet flux linkage bias identification adjustable model, u q This is the Q-axis voltage. For the first permanent magnet chain, The permanent magnet flux linkage bias is obtained by the adaptive rate algorithm for identifying the permanent magnet flux linkage of the rotor.

[0128] In the above implementation, the stator resistance identified in step S130 can be used as a feedback parameter for the adjustable rotor permanent magnet flux linkage identification model. Through this adjustable rotor permanent magnet flux linkage identification model, the first permanent magnet flux linkage identified offline at a set temperature (e.g., 65°C) can be identified. Permanent magnet flux linkage It is understandable that the first permanent magnet flux linkage in the above adjustable model is the permanent magnet flux linkage obtained offline at 65°C. When the offline identification temperature is not 65°C, the expression for the corresponding first permanent magnet flux linkage in the above model will change. The expression for the permanent magnet flux linkage needs to be modified accordingly to match the values ​​for other temperatures.

[0129] For example, the rotor permanent magnet flux linkage identification adaptive rate algorithm is as follows:

[0130]

[0131] Among them, K' 12 K' is the integral coefficient of the rotor permanent magnet flux linkage. p2 K' is the rotor permanent magnet flux linkage proportional coefficient, with a typical value of K'. p2 =15, K' 12 =0.1. Δi d This is the first difference between the measured and observed values ​​of the D-axis current, i.e., the difference between the actual measured value of the motor's D-axis current and the observed value of the D-axis current output by the adjustable model for stator resistance bias identification. The difference between them; Δi q This is the second difference between the actual measured value and the observed value of the Q-axis current, i.e., the difference between the actual measured value of the motor's Q-axis current and the observed value of the Q-axis current output by the stator resistance bias identification adjustable model. The difference between them.

[0132] In some implementations, step S140 includes:

[0133] The direct-axis inductance, quadrature-axis inductance, first permanent magnet flux linkage, and identified stator resistance are input into the rotor permanent magnet flux linkage bias identification adjustable model, and the observed values ​​of direct-axis current and quadrature-axis current are output.

[0134] Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured and observed values ​​of the direct-axis current, and the second difference between the actual measured and observed values ​​of the quadrature-axis current;

[0135] When the first difference or the second difference is not equal to 0, based on the rotor permanent magnet flux linkage identification adaptive rate algorithm, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is adjusted until the first difference and the second difference are both equal to 0.

[0136] When both the first and second differences are equal to 0, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is denoted as the target flux linkage deviation.

[0137] Based on the target flux deflection and the first permanent magnet flux, the permanent magnet flux of the motor is determined.

[0138] For example, the target magnetic flux deflection With the first permanent magnet chain The sum of these is the permanent magnet flux chain Ψ identified in step S140. f ,Right now

[0139] Figure 3 This is a schematic diagram illustrating the online identification principle of stator resistance and permanent magnet flux provided in an embodiment of the present invention, as shown below. Figure 3 As shown in the figure, the output i of the motor reference model di represents the measured value of the D-axis current. q This is the measured value of the Q-axis current. Based on the direct-axis inductance L. d quadrature axis inductance L q First stator resistance R s_65℃ Identification of permanent magnet chain Ψ f With the pre-established adjustable stator resistance bias identification model and stator resistance identification adaptive rate algorithm, the stator resistance of the motor can be identified online. Based on the direct-axis inductance L... d quadrature axis inductance L q First permanent magnet chain Identified stator resistance R s In addition, a pre-established adjustable model for rotor permanent magnet flux linkage identification and an adaptive rate algorithm for rotor permanent magnet flux linkage identification are used to identify the permanent magnet flux linkage Ψ of the motor online. f .

[0140] The key parameters of the permanent magnet synchronous motor (including the direct-axis inductance L) can be obtained by following the above steps. d quadrature axis inductance L q Stator resistance R s and permanent magnet chain Ψ f Online accurate identification of motor speed regulation and torque requirements to meet the high-performance speed regulation and torque requirements of motors.

[0141] Based on the same inventive concept, this embodiment of the invention also provides an online parameter identification device for a permanent magnet synchronous motor, used to identify the motor parameters (including direct-axis inductance, quadrature-axis inductance, stator resistance, and permanent magnet flux linkage) of the permanent magnet synchronous motor online. Figure 4 This is a structural block diagram of an online parameter identification device for a permanent magnet synchronous motor provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the device 400 includes:

[0142] The inductance identification module 410 is used to determine the direct-axis inductance and quadrature-axis inductance of the motor using the pulse high-frequency injection method.

[0143] The parameter acquisition module 420 is used to acquire the first stator resistance and the first permanent magnet flux identified by the offline identification method.

[0144] The resistance identification module 430 is used to identify the stator resistance of the motor online based on the direct-axis inductance, quadrature-axis inductance, first stator resistance, and a pre-established adjustable stator resistance bias identification model and stator resistance identification adaptive rate algorithm.

[0145] The flux linkage identification module 440 is used to identify the permanent magnet flux linkage of the motor online based on the direct-axis inductance, quadrature-axis inductance, first permanent magnet flux linkage, identified stator resistance, and a pre-established rotor permanent magnet flux linkage bias identification adjustable model and rotor permanent magnet flux linkage identification adaptive rate algorithm.

[0146] Optionally, the stator resistance bias identification adjustable model has input parameters, output parameters and adjustable parameters. The input parameters include direct-axis inductance, quadrature-axis inductance and first stator resistance. The output parameters include the observed values ​​of direct-axis current and quadrature-axis current. The adjustable parameters include resistance bias.

[0147] The stator resistor identification adaptive rate algorithm is used to adjust the resistor bias based on the observed values ​​of the direct-axis current and the quadrature-axis current, so that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

[0148] Optionally, the resistor identification module 430 is also used for:

[0149] The direct-axis inductance, quadrature-axis inductance, and first stator resistance are input into the stator resistance bias identification adjustable model, and the observed values ​​of the direct-axis current and quadrature-axis current are output.

[0150] Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured and observed values ​​of the direct-axis current, and the second difference between the actual measured and observed values ​​of the quadrature-axis current;

[0151] When the first difference or the second difference is not equal to 0, the stator resistance bias in the adjustable model of stator resistance bias identification is adjusted based on the stator resistance identification adaptive rate algorithm until the first difference and the second difference are both equal to 0.

[0152] When both the first and second differences are equal to 0, the stator resistance bias identification in the adjustable model is recorded as the target bias.

[0153] The stator resistance of the motor is determined based on the target bias and the first stator resistance.

[0154] Optionally, the rotor permanent magnet flux linkage deflection identification adjustable model has input parameters, output parameters and adjustable parameters. The input parameters include direct-axis inductance, quadrature-axis inductance and first permanent magnet flux linkage. The output parameters include the observed values ​​of direct-axis current and quadrature-axis current. The adjustable parameters include permanent magnet flux linkage deflection.

[0155] The rotor permanent magnet flux linkage identification adaptive rate algorithm is used to adjust the permanent magnet flux linkage bias based on the observed values ​​of the direct-axis current and the quadrature-axis current, so that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

[0156] Optionally, the magnetic link identification module 440 is also used for:

[0157] The direct-axis inductance, quadrature-axis inductance, first permanent magnet flux linkage, and identified stator resistance are input into the rotor permanent magnet flux linkage bias identification adjustable model, and the observed values ​​of direct-axis current and quadrature-axis current are output.

[0158] Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured and observed values ​​of the direct-axis current, and the second difference between the actual measured and observed values ​​of the quadrature-axis current;

[0159] When the first difference or the second difference is not equal to 0, based on the rotor permanent magnet flux linkage identification adaptive rate algorithm, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is adjusted until the first difference and the second difference are both equal to 0.

[0160] When both the first and second differences are equal to 0, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is denoted as the target flux linkage deviation.

[0161] Based on the target flux deflection and the first permanent magnet flux, the permanent magnet flux of the motor is determined.

[0162] Optionally, the inductor identification module 410 is also used for:

[0163] Inject pulsating high-frequency voltage into the direct shaft of the motor, obtain the maximum value of the direct shaft high-frequency current, and determine the direct shaft inductance based on the maximum value of the direct shaft high-frequency current;

[0164] Inject pulsating high-frequency voltage into the quadrature axis of the motor, obtain the maximum value of the quadrature axis high-frequency current, and determine the quadrature axis inductance based on the maximum value of the quadrature axis high-frequency current.

[0165] Optionally, the device 400 also includes an offline identification module for:

[0166] A stall test is performed on the motor, and the first stator resistance is identified offline based on the stall test parameters. The first stator resistance is the stator resistance of the motor stator at a set temperature.

[0167] A reverse drag test is performed on the motor, and the first permanent magnet flux linkage is identified offline based on the reverse drag test parameters. The first permanent magnet flux linkage is the permanent magnet flux linkage of the motor rotor at a set temperature.

[0168] The specific details of the online parameter identification method used in the above-mentioned online parameter identification device can be understood by referring to the relevant descriptions and effects in the above-described online parameter identification method embodiments, and will not be repeated here.

[0169] Based on the same inventive concept as the online parameter identification method described above, this invention also provides an electronic device, which may include a processor and a memory, wherein the processor and memory can communicate with each other via a bus or other means. The processor may be a Central Processing Unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the online parameter identification method in this embodiment. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby realizing the online identification of motor parameters in the above method embodiments.

[0170] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the 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. The one or more modules are stored in the memory and, when executed by the processor, perform actions such as... Figure 1 The online parameter identification method in the illustrated embodiment.

[0171] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figure 1 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0172] Based on the same inventive concept as the parameter calibration method, the present invention also provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the online parameter identification method in the above embodiments.

[0173] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0174] The technical solutions provided in the above embodiments of this application have at least the following technical effects or advantages:

[0175] This invention provides an online identification method, apparatus, device, and medium for permanent magnet synchronous motor parameters. By employing a pulsed high-frequency injection method, the direct-axis inductance and quadrature-axis inductance of the motor are determined. The high-frequency signal injected by this method has no impact on the fundamental wave control, and the injected voltage and sampling current are independent of the stator resistance and rotor permanent magnet flux linkage. This characteristic decouples the identification results of time-varying current parameters (inductance) from those of time-varying temperature parameters (stator resistance and permanent magnet flux linkage), significantly improving the accuracy and precision of parameter identification. Furthermore, this invention pre-acquires the first stator resistance and first permanent magnet flux linkage determined by an offline identification method, and combines this with the inductance parameters identified by the pulsed high-frequency injection method, along with a pre-established adjustable model and adaptive rate algorithm, to achieve accurate online identification of the stator resistance and rotor permanent magnet flux linkage during motor operation. In this process, the first stator resistance and first permanent magnet flux linkage determined offline are used as feedforwards for the adjustable model. An adaptive rate algorithm adjusts the deviations in the first stator resistance and first permanent magnet flux linkage caused by temperature variations. This not only avoids deviations in offline identification results due to changes in operating conditions but also eliminates the need for additional temperature sensors, reducing hardware costs. Furthermore, this model-referenced adaptive method effectively reduces the coupling between the stator resistance and rotor permanent magnet flux linkage identification results, further improving the accuracy and precision of motor parameter identification, optimizing motor control performance, and enabling it to respond more accurately to changes in operating conditions to meet the high-performance speed regulation and torque requirements of the motor.

[0176] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0177] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0178] It should be noted that the above embodiments are illustrative of the invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims.

Claims

1. A method for online parameter identification of a permanent magnet synchronous motor, characterized in that, The method includes: The direct-axis inductance and quadrature-axis inductance of the motor were determined using the pulsed high-frequency injection method. Obtain the first stator resistor and the first permanent magnet flux identified using an offline identification method; Based on the direct-axis inductor, the quadrature-axis inductor, the first stator resistance, the online identified permanent magnet flux linkage, and the pre-established adjustable stator resistance bias identification model and stator resistance identification adaptive rate algorithm, the stator resistance of the motor is identified online. Based on the direct-axis inductor, the quadrature-axis inductor, the first permanent magnet flux linkage, the identified stator resistance, and the pre-established rotor permanent magnet flux linkage bias identification adjustable model and rotor permanent magnet flux linkage identification adaptive rate algorithm, the permanent magnet flux linkage of the motor is identified online.

2. The method according to claim 1, characterized in that, The stator resistance bias identification adjustable model has input parameters, output parameters, and adjustable parameters. The input parameters include the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, and the online identified permanent magnet flux linkage. The output parameters include the observed values ​​of the direct-axis current and the quadrature-axis current. The adjustable parameters include the resistance bias. The stator resistor identification adaptive rate algorithm is used to adjust the resistor bias based on the observed values ​​of the direct-axis current and the quadrature-axis current, so that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

3. The method according to claim 2, characterized in that, The method of online identification of the motor's stator resistance based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, the online identified permanent magnet flux linkage, and the pre-established adjustable stator resistance bias identification model and stator resistance identification adaptive rate algorithm includes: The direct-axis inductor, the quadrature-axis inductor, and the first stator resistor are input into the stator resistor bias identification adjustable model, and the observed values ​​of the direct-axis current and the quadrature-axis current are output. Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured value and the observed value of the direct-axis current, and the second difference between the actual measured value and the observed value of the quadrature-axis current; When the first difference or the second difference is not equal to 0, based on the stator resistor identification adaptive rate algorithm, the stator resistor bias in the stator resistor bias identification adjustable model is adjusted until the first difference and the second difference are both equal to 0. When both the first difference and the second difference are equal to 0, the stator resistance bias in the adjustable model of stator resistance bias identification is denoted as the target bias. The stator resistance of the motor is determined based on the target bias and the first stator resistance.

4. The method according to claim 1, characterized in that, The rotor permanent magnet flux linkage deflection identification adjustable model has input parameters, output parameters, and adjustable parameters. The input parameters include the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux linkage, and the identified stator resistance. The output parameters include the observed values ​​of the direct-axis current and the quadrature-axis current. The adjustable parameters include the permanent magnet flux linkage deflection. The rotor permanent magnet flux linkage identification adaptive rate algorithm is used to adjust the permanent magnet flux linkage bias based on the observed values ​​of the direct-axis current and the quadrature-axis current, so that the observed value of the direct-axis current is equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current is equal to the measured value of the quadrature-axis current.

5. The method according to claim 4, characterized in that, The method of identifying the permanent magnet flux linkage of the motor online, based on the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux linkage, the identified stator resistance, and a pre-established adjustable model for rotor permanent magnet flux linkage identification and an adaptive rate algorithm for rotor permanent magnet flux linkage identification, includes: The direct-axis inductor, the quadrature-axis inductor, the first permanent magnet flux linkage, and the identified stator resistance are input into the rotor permanent magnet flux linkage bias identification adjustable model, and the observed values ​​of the direct-axis current and the quadrature-axis current are output. Obtain the measured values ​​of the direct-axis current and the quadrature-axis current, and determine the first difference between the measured value and the observed value of the direct-axis current, and the second difference between the actual measured value and the observed value of the quadrature-axis current; When the first difference or the second difference is not equal to 0, based on the rotor permanent magnet flux linkage identification adaptive rate algorithm, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is adjusted until both the first difference and the second difference are equal to 0. When both the first difference and the second difference are equal to 0, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is denoted as the target flux linkage deviation. The permanent magnet flux linkage of the motor is determined based on the target flux linkage bias and the first permanent magnet flux linkage.

6. The method according to claim 1, characterized in that, The direct-axis and quadrature-axis inductances of the motor are determined using the pulsed high-frequency injection method, including: A pulsating high-frequency voltage is injected into the direct shaft of the motor to obtain the maximum value of the direct shaft high-frequency current, and the direct shaft inductance is determined based on the maximum value of the direct shaft high-frequency current. The pulsating high-frequency voltage is injected into the quadrature axis of the motor to obtain the maximum value of the quadrature axis high-frequency current, and the quadrature axis inductance is determined based on the maximum value of the quadrature axis high-frequency current.

7. The method according to claim 1, characterized in that, Before obtaining the first stator resistance and the first permanent magnet flux identified by the offline identification method, the method further includes: A stall test is performed on the motor, and the first stator resistance is identified offline based on the stall test parameters. The first stator resistance is the stator resistance of the motor stator at a set temperature. A reverse drag test is performed on the motor, and the first permanent magnet flux is identified offline based on the reverse drag test parameters. The first permanent magnet flux is the permanent magnet flux of the motor rotor at a set temperature.

8. An online parameter identification device for a permanent magnet synchronous motor, characterized in that, The device includes: The inductance identification module is used to determine the direct-axis inductance and quadrature-axis inductance of the motor using the pulsed high-frequency injection method. The parameter acquisition module is used to acquire the first stator resistance and the first permanent magnet flux identified by the offline identification method. The resistance identification module is used to identify the stator resistance of the motor online based on the direct-axis inductor, the quadrature-axis inductor, the first stator resistance, the online identified permanent magnet flux linkage, and the pre-established adjustable stator resistance bias identification model and stator resistance identification adaptive rate algorithm. The flux linkage identification module is used to identify the permanent magnet flux linkage of the motor online based on the direct-axis inductor, the quadrature-axis inductor, the first permanent magnet flux linkage, the identified stator resistance, and a pre-established adjustable model for rotor permanent magnet flux linkage identification and an adaptive rate algorithm for rotor permanent magnet flux linkage identification.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the online parameter identification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the online parameter identification method according to any one of claims 1 to 7.

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