Parameter on-line identification method, device and equipment of permanent magnet synchronous motor and medium

Abstract

The invention discloses a parameter online identification method, device and equipment of a permanent magnet synchronous motor and a medium. The method comprises the following steps: determining direct-axis inductance and quadrature-axis inductance of the motor by adopting a pulsating high-frequency injection method; obtaining a first stator resistance and a first permanent magnet flux linkage identified by adopting an offline identification method; on the basis of the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, a pre-established adjustable model and an adaptive rate algorithm, identifying the stator resistance of the motor on line; and on the basis of the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux linkage, the identified stator resistance, a pre-established adjustable model and an adaptive rate algorithm, identifying the permanent magnet flux linkage of the motor on line. On-line identification of the inductance of the motor is achieved through a pulsating high-frequency injection method, accurate on-line identification of the stator resistance and the permanent magnet flux linkage in a permanent magnet synchronous motor control system without a temperature sensor is achieved in combination with an off-line identification and self-adaptive algorithm, and the identification accuracy and precision of motor parameters are improved.

Description

Technical Field

[0001] The present invention relates to the field of motor technology, and in particular to a method, device, equipment and medium for online identification of parameters of a permanent magnet synchronous motor. Background Art

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

[0003] In order to achieve high-performance motor control, it is necessary to accurately identify key motor parameters such as the D-axis inductance, Q-axis inductance, stator resistance, and permanent magnet flux. These motor parameters directly affect the steady-state and dynamic performance of the motor and are the basis for building precise control strategies. At present, the identification methods of motor parameters are mainly divided into two categories: online identification and offline identification. The offline identification method is usually performed when the motor is static or under specific working conditions. Although the operation is simple, when there is a significant difference between the actual operating conditions of the motor and the offline identification conditions, the true value of the identification result often deviates, resulting in a decrease in control accuracy. The online identification method can update the parameter value in real time during the operation of the motor to adapt to changes in working conditions. Existing online identification methods include the least squares method and the identification method based on model reference adaptation.

[0004] However, existing online identification methods have the problems of high coupling between parameters and poor identification accuracy and precision. In addition, since the stator resistance among the parameters to be identified 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 inversely calculate the stator resistance, thereby increasing the hardware setting cost of the temperature sensor.

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

[0006] In view of the above problems, the present invention is proposed to provide a method, device, equipment and medium for online parameter identification of a permanent magnet synchronous motor that overcomes the above problems or at least partially solves the above problems. The online identification of the motor inductance is realized by the pulse high-frequency injection method. By combining the offline identification and the adaptive algorithm, the stator resistance and permanent magnet flux are accurately identified online in a permanent magnet synchronous motor control system without a temperature sensor, thereby improving the accuracy and precision of the motor parameter identification and optimizing the control performance to meet the high-performance speed regulation and torque requirements of the motor.

[0007] In a first aspect, a method for online identification of parameters of a permanent magnet synchronous motor is provided, the method comprising:

[0008] The pulse high frequency injection method is used to determine the direct axis inductance and quadrature axis inductance of the motor;

[0009] Acquire a first stator resistance and a first permanent magnet flux linkage identified by an offline identification method;

[0010] Based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, and a pre-established stator resistance bias identification adjustable model and a stator resistance identification adaptive rate algorithm, online identifying the stator resistance of the motor;

[0011] Based on the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux, the identified stator resistance, and the pre-established rotor permanent magnet flux partial identification adjustable model and rotor permanent magnet flux identification adaptive rate algorithm, the permanent magnet flux 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 value of the direct-axis current and the observed value of the quadrature-axis current, and the adjustable parameters include the resistance bias;

[0013] The stator resistance identification adaptive rate algorithm is used to adjust the resistance 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 stator resistance of the motor based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, and a pre-established stator resistance bias identification adjustable model and a stator resistance identification adaptive rate algorithm includes:

[0015] Inputting the direct-axis inductance, the quadrature-axis inductance and the first stator resistance into the stator resistance bias identification adjustable model, and outputting the observed values ​​of the direct-axis current and the quadrature-axis current;

[0016] Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, and determining a first difference between the measured value and the observed value of the direct-axis current, and a 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 resistance identification adaptive rate algorithm, adjusting the stator resistance bias in the stator resistance bias identification adjustable model until the first difference and the second difference are both equal to 0;

[0018] When the first difference and the second difference are both equal to 0, the stator resistance deviation in the stator resistance deviation identification adjustable model is recorded as a target resistance deviation;

[0019] Based on the target resistance deviation and the first stator resistance, a stator resistance of the motor is determined.

[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 value of the direct-axis current and the observed value of the quadrature-axis current, and 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 of the motor based on the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux, the identified stator resistance, and a pre-established rotor permanent magnet flux bias identification adjustable model and a rotor permanent magnet flux identification adaptive rate algorithm includes:

[0023] Inputting the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux linkage and the identified stator resistance into the rotor permanent magnet flux linkage bias identification adjustable model, and outputting the observed values ​​of the direct-axis current and the quadrature-axis current;

[0024] Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, and determining a first difference between the measured value and the observed value of the direct-axis current, and a 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, adjusting the permanent magnet flux linkage bias in the rotor permanent magnet flux linkage bias identification adjustable model until the first difference and the second difference are both equal to 0;

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

[0027] Based on the target flux deviation and the first permanent magnet flux, the permanent magnet flux of the motor is determined.

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

[0029] Injecting a pulse high-frequency voltage into the direct axis of the motor to obtain a maximum value of the direct axis high-frequency current, and determining the direct axis inductance based on the maximum value of the direct axis high-frequency current;

[0030] The pulse 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 acquiring the first stator resistance and the first permanent magnet flux linkage identified by using the offline identification method, the method further includes:

[0032] Performing a stall test on the motor, and identifying the first stator resistance offline based on the stall test parameters, where 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 linkage is offline identified based on the reverse drag test parameters, where the first permanent magnet flux linkage is the permanent magnet flux linkage of the motor rotor at a set temperature.

[0034] In a second aspect, a parameter online identification device for a permanent magnet synchronous motor is provided, the device comprising:

[0035] Inductance identification module, used to determine the direct-axis inductance and quadrature-axis inductance of the motor using the pulse high-frequency injection method;

[0036] A parameter acquisition module, used to acquire a first stator resistance and a first permanent magnet flux linkage identified by an offline identification method;

[0037] A resistance identification module, used for online identification of the stator resistance of the motor based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, and a pre-established stator resistance bias identification adjustable model and a stator resistance identification adaptive rate algorithm;

[0038] The flux linkage identification module is used to online identify 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 rotor permanent magnet flux linkage bias identification adjustable model and a rotor permanent magnet flux linkage identification adaptive rate algorithm.

[0039] In a third aspect, an electronic device is provided, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the online parameter identification method as described in the first aspect by executing the computer instructions.

[0040] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the online parameter identification method as described in the first aspect.

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

[0042] An embodiment of the present invention provides an online identification method, device, equipment and medium for permanent magnet synchronous motor parameters. The direct-axis inductance and quadrature-axis inductance of the motor are determined by adopting a pulse high-frequency injection method. The high-frequency signal injected by the pulse high-frequency injection method has no effect on the fundamental wave control, and the injected voltage and recovery current are independent of the stator resistance and the rotor permanent magnet flux. This characteristic enables the identification results of the current time-varying parameters (inductance) and the temperature time-varying parameters (stator resistance, permanent magnet flux) to be decoupled from each other, significantly improving the accuracy and precision of parameter identification. On this basis, the present invention obtains the first stator resistance and the first permanent magnet flux determined by the offline identification method in advance, and combines the inductance parameters identified by the pulse high-frequency injection method, and the pre-established adjustable model and adaptive rate algorithm for parameter identification, thereby realizing online and accurate identification of the stator resistance and the rotor permanent magnet flux during the operation of the motor. In this process, the first stator resistance and the first permanent magnet flux determined by offline identification are used as the feedforward of the adjustable model. The deviation of the first stator resistance and the first permanent magnet flux caused by the temperature deviation can be adjusted through the adaptive rate algorithm, which not only avoids the problem of deviation of offline identification results caused by changes in working conditions, but also does not require additional temperature sensors, reducing hardware costs. And through this model reference adaptive method, the mutual coupling between the stator resistance and the rotor permanent magnet flux identification results is effectively reduced, further improving the accuracy and precision of the motor parameter identification, optimizing the control performance of the motor, and enabling it to respond more accurately to changes in working conditions to meet the high-performance speed regulation and torque requirements of the motor.

[0043] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0045] Figure 1 This is a flow chart of a method for online identification of parameters of a permanent magnet synchronous motor provided by an embodiment of the present invention;

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

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

[0048] Figure 4 It 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 DESCRIPTION

[0049] In order to better understand the above-mentioned technical scheme, the above-mentioned technical scheme will be described in detail below in combination with the accompanying drawings and specific implementation methods of the specification. It should be understood that the embodiments of the present disclosure and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0050] Figure 1 is a flow chart of a method for online identification of parameters of a permanent magnet synchronous motor provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0051] Step S110: using a pulse 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] Injecting a pulse high-frequency voltage into the direct axis (i.e., D axis) of the motor to obtain a maximum value of the direct-axis high-frequency current, and determining the direct-axis inductance based on the maximum value of the direct-axis high-frequency current;

[0054] A pulse high-frequency voltage is injected into the quadrature axis (ie, Q 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.

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

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

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

[0058] Among them, u dhin is the injected pulse high frequency voltage, u in is the voltage amplitude of the pulse high-frequency voltage, ω in is the voltage frequency of the pulse high-frequency voltage; i dref is the D-axis current, BPF is the bandpass filter, i dhin is the D-axis high frequency current.

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

[0060]

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

[0062] Based on the above formulas (1) to (3), the calculation formula of the D-axis inductance can be obtained as follows:

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

[0064]

[0065] Among them, i dhmax is the maximum value of the high-frequency current on the D axis.

[0066] From the above analysis, it can be seen that in some embodiments, determining the direct-axis inductance based on the direct-axis high-frequency current includes:

[0067] Based on the following formula (5), determine the direct-axis inductance:

[0068]

[0069] Among them, L d is the direct axis inductance, u in is the voltage amplitude of the pulse high-frequency voltage, ω in is the voltage frequency of the pulse high-frequency voltage, i dhmax is the maximum value of the direct-axis high-frequency current.

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

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

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

[0073] Among them, u qhin is the injected Q-axis pulse high-frequency voltage, u in is the voltage amplitude of the pulse high-frequency voltage, ω in is the voltage frequency of the pulse high-frequency voltage; i qref is the Q-axis current, BPF is the bandpass filter, i qhin is the Q-axis high-frequency current.

[0074] From the above formulas (6) and (7), the calculation formula for the Q-axis high-frequency current can be obtained as follows:

[0075]

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

[0077] Based on the above formulas (6) to (8), the calculation formula for the Q-axis inductance can be obtained as follows:

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

[0079]

[0080] Among them, i qhmax It is the maximum value of the high-frequency current on the Q axis.

[0081] From the above analysis, it can be seen that in some embodiments, determining the quadrature-axis inductance based on the quadrature-axis high-frequency current includes:

[0082] Based on the following formula (10), determine the quadrature-axis inductance:

[0083]

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

[0085] Figure 2 Schematic diagram of the principle of a pulse high frequency injection method provided by an embodiment of the present invention. Figure 2 As shown, in this embodiment, a pulse high-frequency voltage u is alternately injected into the D-axis current and the Q-axis current of the motor in different cycles. qhin and u qhin , the motor output current is controlled by the current loop to reach the set value SV, and then the inverter circuit is responsible for converting the DC control signal output by the control system into AC power to drive the motor. By extracting the three-phase current i in the inverter circuit a 、i b and i c , and transform its coordinates to obtain the D-axis current i dref and Q axis current i qref ; Then, the high-frequency current signals of the D-axis and Q-axis are extracted through a bandpass filter, and the maximum value 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 , which can be used to calculate the D-axis inductance and Q-axis inductance respectively.

[0086] Step S120: Acquire a first stator resistance and a first permanent magnet flux linkage identified by using an offline identification method.

[0087] In some implementations, before executing step S120, the method may include:

[0088] Performing a stall test on the motor, and identifying a first stator resistance based on stall test parameters;

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

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

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

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

[0093]

[0094] Among them, R s_65℃ It is the stator resistance of the motor stator in a thermally stable state (65° C.), that is, the first stator resistance.

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

[0096] In some implementations, the reverse drag test of the motor may include: placing the motor in a temperature box at a set temperature (e.g., 65° C.), reverse dragging the motor under test with a dynamometer, and recording the back electromotive force E at the rated speed E_Speed ​​when the motor rotor is in a hot temperature state (e.g., 65° C.) through a temperature sensor arranged in the motor rotor. e_speed , and the back electromotive force E of the motor at the peak speed Max_Speed max_speed .

[0097] The relationship between back electromotive force and magnetic flux is as follows (13):

[0098]

[0099] in, is the permanent magnet flux, E peak is the peak value of the motor's back electromotive force, n is the motor speed, and p is the number of motor pole pairs.

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

[0101]

[0102] in, It is the permanent magnet flux linkage when the motor rotor is in a thermally stable state (65°C), that is, the first permanent magnet flux linkage.

[0103] In the above implementation, the reverse drag test parameters include at least the rated speed and peak speed of the motor, and 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 of the motor; 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 of the motor.

[0104] It should be noted that in the above implementation, the set temperature is 65°C. In the specific setting, technical personnel in this field can also conduct relevant tests according to the actual temperature of the motor in a thermally stable state to obtain the first stator resistance and the first electromagnetic flux. The present invention is not limited to this.

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

[0106] In some implementations, the stator resistance bias identification adjustable model has input parameters, output parameters and adjustable parameters, and 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 value of the direct axis current and the observed values ​​of the quadrature axis current Adjustable parameters include resistance bias Stator resistance identification adaptive rate algorithm is used based on the observed values ​​of direct-axis current and quadrature-axis current and Adjust the resistance bias Make the observed value of the direct-axis current equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current equal to the measured value of the quadrature-axis current.

[0107] For example, the stator resistance deviation (the resistance deviation caused by the stator temperature deviation from 65° C.) identification and adjustable model is as follows:

[0108]

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

[0110] Through the above stator resistance bias identification adjustable model, the first stator resistance (R s_65℃ ) It can be understood that the first stator resistance in the above adjustable model is the stator resistance at 65°C obtained by offline identification. When the offline identified temperature is not 65°C, the expression R corresponding to the first stator resistance in the above model is s_65℃ The expression of stator resistance needs to be modified to correspond to other temperatures.

[0111] In some implementations, the permanent magnet flux linkage σ identified in step S140 may be f As a feedback parameter, it is input into the stator resistance bias identification adjustable model. Based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, the permanent magnet flux linkage identified in step S140, the stator resistance bias identification adjustable model and the stator resistance identification adaptive rate algorithm, the stator resistance bias is identified online. At the same time, the stator resistance identified in step S130 is used as the feedback parameter of the rotor permanent magnet flux linkage bias identification adjustable model, and is input into the rotor permanent magnet flux linkage bias identification adjustable model. That is, the identification value of the other group is used as input in the two sets of adjustable models, so that there is only one unknown parameter to be identified in the two sets of adjustable models, thereby solving the existing problem of lack of rank in the simultaneous identification of four motor parameters (D-axis inductance, Q-axis inductance, stator resistance and permanent magnet flux linkage) using two voltage equations.

[0112] Exemplarily, the stator resistance identification adaptive rate algorithm is as follows:

[0113]

[0114] Among them, K' 11 is the stator resistance identification integral coefficient, K' p1 is the stator resistance identification proportional coefficient, typical parameter K' p1 =15, K' 11=0.5. Δi d The first difference between the measured value and the observed value of the D-axis current, that is, the measured value of the motor D-axis current actually measured and the observed value of the D-axis current output by the stator resistance bias identification adjustable model The difference between q The second difference between the actual measured value and the observed value of the Q-axis current, that is, the measured value of the motor Q-axis current actually measured and the observed value of the Q-axis current output by the stator resistance bias identification adjustable model The difference between .

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

[0116] Inputting the direct-axis inductance, the quadrature-axis inductance and the first stator resistance into the stator resistance bias identification adjustable model, and outputting the observed values ​​of the direct-axis current and the quadrature-axis current;

[0117] Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, determining a first difference between the measured value and the observed value of the direct-axis current, and a second difference between the actual measured value and the observed value of the quadrature-axis current;

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

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

[0120] Based on the target resistance deviation and the first stator resistance, a stator resistance of the motor is determined.

[0121] For example, the target is blocked With the first stator resistance R s_65℃ The sum of the stator resistance R identified in step S130 is s ,Right now

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

[0123] In some implementations, 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 L d , quadrature axis inductance L q and the first permanent magnet flux and the stator resistance R identified in step S130s The output parameters include the observed value of the direct axis current and the observed values ​​of the quadrature axis current Adjustable parameters include permanent magnet flux linkage and flux deviation

[0124] The adaptive rate algorithm for identifying the permanent magnet flux linkage of the rotor is used based on the observed values ​​of the direct-axis current and the quadrature-axis current. and Adjust the permanent magnet flux linkage Make the observed value of the direct-axis current equal to the measured value of the direct-axis current, and the observed value of the quadrature-axis current equal to the measured value of the quadrature-axis current.

[0125] For example, the adjustable model for identifying the rotor permanent magnet flux deviation (the flux deviation caused by the rotor temperature deviation from 65°C) is as follows:

[0126]

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

[0128] In the above implementation, the stator resistance identified in step S130 can be used as a feedback parameter of the rotor permanent magnet flux linkage bias identification adjustable model. Through the above rotor permanent magnet flux linkage bias identification adjustable model, the first permanent magnet flux linkage identified offline at the set temperature (e.g. 65°C) can be identified. The permanent magnet flux is It can be understood that the first permanent magnetic flux linkage in the above adjustable model is the permanent magnetic flux linkage at 65°C obtained by offline identification. When the offline identified temperature is not 65°C, the expression of the first permanent magnetic flux linkage in the above model is The expression of permanent magnet flux linkage needs to be modified to correspond to other temperatures.

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

[0130]

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

[0132] In some implementations, step S140 includes:

[0133] Input the direct-axis inductance, quadrature-axis inductance, first permanent magnet flux linkage and identified stator resistance into the rotor permanent magnet flux linkage partial identification adjustable model, and output the observed values ​​of direct-axis current and quadrature-axis current;

[0134] Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, determining a first difference between the measured value and the observed value of the direct-axis current, and a second difference between the actual measured value and the observed value 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 bias in the rotor permanent magnet flux linkage bias identification adjustable model is adjusted until the first difference and the second difference are both equal to 0;

[0136] When the first difference and the second difference are both equal to 0, the permanent magnet flux linkage deviation of the rotor is identified as the permanent magnet flux linkage deviation in the adjustable model, which is recorded as the target flux linkage deviation;

[0137] The permanent magnet flux of the motor is determined based on the target flux deviation and the first permanent magnet flux.

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

[0139] Figure 3 is a schematic diagram of the online identification principle of stator resistance and permanent magnet flux provided by an embodiment of the present invention, such as Figure 3 As shown in the figure, the output of the motor reference model is i dis the measured value of the D-axis current, i q is the measured value of the Q-axis current. Based on the direct-axis inductance L d , quadrature axis inductance L q , the first stator resistance R s_65℃ 、Identification of permanent magnetic flux Ψ f The pre-established stator resistance bias identification adjustable model and stator resistance identification adaptive rate algorithm can be used to identify the stator resistance of the motor online. d , quadrature axis inductance L q , the first permanent magnet flux Identified stator resistance R s As well as the pre-established rotor permanent magnet flux linkage bias identification adjustable model and rotor permanent magnet flux linkage identification adaptive rate algorithm, online identification of the motor's permanent magnet flux linkage Ψ f .

[0140] Through the above steps, the key parameters of the permanent magnet synchronous motor (including the direct axis inductance L d , quadrature axis inductance L q , stator resistance R s and permanent magnetic flux linkage Ψ f ) to meet the high-performance speed and torque requirements of the motor.

[0141] Based on the same inventive concept, an embodiment of the present invention also provides an online parameter identification device for a permanent magnet synchronous motor, which is used to online identify the motor parameters of the permanent magnet synchronous motor (including direct-axis inductance, quadrature-axis inductance, stator resistance and permanent magnet flux). Figure 4 is a structural block diagram of an online parameter identification device for a permanent magnet synchronous motor provided by an embodiment of the present invention, such as 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 by using a pulse high-frequency injection method;

[0143] A parameter acquisition module 420 is used to acquire a first stator resistance and a first permanent magnet flux linkage identified by an 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, the quadrature-axis inductance, the first stator resistance, and the pre-established stator resistance bias identification adjustable model and the stator resistance identification adaptive rate algorithm;

[0145] The flux linkage identification module 440 is used to online identify the permanent magnet flux linkage of the motor based on the direct-axis inductance, quadrature-axis inductance, 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.

[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 observed values ​​of direct-axis current and observed values ​​of quadrature-axis current, and the adjustable parameters include resistance bias;

[0147] The stator resistance identification adaptive rate algorithm is used to adjust the resistance 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 resistance identification module 430 is further used for:

[0149] Inputting the direct-axis inductance, the quadrature-axis inductance and the first stator resistance into the stator resistance bias identification adjustable model, and outputting the observed values ​​of the direct-axis current and the quadrature-axis current;

[0150] Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, determining a first difference between the measured value and the observed value of the direct-axis current, and a second difference between the actual measured value and the observed value of the quadrature-axis current;

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

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

[0153] Based on the target resistance deviation and the first stator resistance, a stator resistance of the motor is determined.

[0154] Optionally, the rotor permanent magnet flux linkage bias 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 observed values ​​of direct-axis current and observed values ​​of quadrature-axis current, and the adjustable parameters include permanent magnet flux linkage bias;

[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 flux identification module 440 is further used for:

[0157] Inputting the direct-axis inductance, quadrature-axis inductance, the first permanent magnet flux linkage and the identified stator resistance into the rotor permanent magnet flux linkage partial identification adjustable model, and outputting the observed values ​​of the direct-axis current and the quadrature-axis current;

[0158] Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, determining a first difference between the measured value and the observed value of the direct-axis current, and a second difference between the actual measured value and the observed value 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 bias in the rotor permanent magnet flux linkage bias identification adjustable model is adjusted until the first difference and the second difference are both equal to 0;

[0160] When the first difference and the second difference are both equal to 0, the permanent magnet flux linkage deviation of the rotor is identified as the permanent magnet flux linkage deviation in the adjustable model, which is recorded as the target flux linkage deviation;

[0161] The permanent magnet flux of the motor is determined based on the target flux deviation and the first permanent magnet flux.

[0162] Optionally, the inductance identification module 410 is further used for:

[0163] Injecting a pulse high-frequency voltage into the direct axis of the motor to obtain a maximum value of the direct-axis high-frequency current, and determining the direct-axis inductance based on the maximum value of the direct-axis high-frequency current;

[0164] A pulse 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.

[0165] Optionally, the device 400 further includes an offline identification module, which is used to:

[0166] Performing a stall test on the motor, and offline identifying a first stator resistance based on the stall test parameters, where 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 offline identified based on the reverse drag test parameters, where 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 parameter online identification method used in the above-mentioned parameter online identification device can be understood by corresponding to the relevant descriptions and effects in the above-mentioned parameter online identification method embodiment, and will not be repeated here.

[0169] Based on the same inventive concept as the above-mentioned parameter online identification method, the present invention also provides an electronic device, which may include a processor and a memory, wherein the processor and the memory may be connected to each other by 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 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components and other chips, or a combination of the above-mentioned various types of chips. The memory, as a non-transient computer-readable storage medium, may be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the parameter online identification method in the embodiment of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory, that is, realizing the online identification of the motor parameters in the above-mentioned method embodiment.

[0170] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. The one or more modules are stored in the memory, and when executed by the processor, the execution is as follows: Figure 1 The online parameter identification method in the illustrated embodiment.

[0171] For details of the above electronic equipment, please refer to Figure 1 The corresponding related descriptions and effects in the illustrated embodiments can be understood 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, on which computer instructions are stored, and the computer program instructions are used to enable a computer to execute the parameter online identification method in the above embodiment.

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

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

[0175] An embodiment of the present invention provides an online identification method, device, equipment and medium for permanent magnet synchronous motor parameters. The direct-axis inductance and quadrature-axis inductance of the motor are determined by adopting a pulse high-frequency injection method. The high-frequency signal injected by the pulse high-frequency injection method has no effect on the fundamental wave control, and the injected voltage and recovery current are independent of the stator resistance and the rotor permanent magnet flux. This characteristic enables the identification results of the current time-varying parameters (inductance) and the temperature time-varying parameters (stator resistance, permanent magnet flux) to be decoupled from each other, significantly improving the accuracy and precision of parameter identification. On this basis, the present invention obtains the first stator resistance and the first permanent magnet flux determined by the offline identification method in advance, and combines the inductance parameters identified by the pulse high-frequency injection method, and the pre-established adjustable model and adaptive rate algorithm for parameter identification, thereby realizing online and accurate identification of the stator resistance and the rotor permanent magnet flux during the operation of the motor. In this process, the first stator resistance and the first permanent magnet flux determined by offline identification are used as the feedforward of the adjustable model. The deviation of the first stator resistance and the first permanent magnet flux caused by the temperature deviation can be adjusted through the adaptive rate algorithm, which not only avoids the problem of deviation of offline identification results caused by changes in working conditions, but also does not require additional temperature sensors, reducing hardware costs. And through this model reference adaptive method, the mutual coupling between the stator resistance and the rotor permanent magnet flux identification results is effectively reduced, further improving the accuracy and precision of the motor parameter identification, optimizing the control performance of the motor, and enabling it to respond more accurately to changes in working conditions to meet the high-performance speed regulation and torque requirements of the motor.

[0176] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0177] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0178] It should be noted that the above-mentioned embodiments illustrate rather than limit the invention and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims.

Claims

1. A method for online identification of parameters of a permanent magnet synchronous motor, characterized in that: The method comprises: The pulse high frequency injection method is used to determine the direct axis inductance and quadrature axis inductance of the motor; Acquire a first stator resistance and a first permanent magnet flux linkage identified by an offline identification method; Based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, and a pre-established stator resistance bias identification adjustable model and a stator resistance identification adaptive rate algorithm, online identifying the stator resistance of the motor; Based on the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux, the identified stator resistance, and the pre-established rotor permanent magnet flux partial identification adjustable model and rotor permanent magnet flux identification adaptive rate algorithm, the permanent magnet flux 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 and the first stator resistance, the output parameters include the observed value of the direct-axis current and the observed value of the quadrature-axis current, and the adjustable parameters include the resistance bias; The stator resistance identification adaptive rate algorithm is used to adjust the resistance 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 online identification of the stator resistance of the motor based on the direct-axis inductance, the quadrature-axis inductance, the first stator resistance, and a pre-established stator resistance bias identification adjustable model and a stator resistance identification adaptive rate algorithm includes: Inputting the direct-axis inductance, the quadrature-axis inductance and the first stator resistance into the stator resistance bias identification adjustable model, and outputting the observed values ​​of the direct-axis current and the quadrature-axis current; Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, and determining a first difference between the measured value and the observed value of the direct-axis current, and a 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 resistance identification adaptive rate algorithm, adjusting the stator resistance bias in the stator resistance bias identification adjustable model until the first difference and the second difference are both equal to 0; When the first difference and the second difference are both equal to 0, the stator resistance deviation in the stator resistance deviation identification adjustable model is recorded as the target resistance deviation; Based on the target resistance deviation and the first stator resistance, a stator resistance of the motor is determined.

4. The method according to claim 1, characterized in that: 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 value of the direct-axis current and the observed value of the quadrature-axis current, and the adjustable parameters include the permanent magnet flux linkage bias; 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 online identifying the permanent magnet flux of the motor based on the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux, the identified stator resistance, and the pre-established rotor permanent magnet flux partial identification adjustable model and rotor permanent magnet flux identification adaptive rate algorithm includes: Inputting the direct-axis inductance, the quadrature-axis inductance, the first permanent magnet flux linkage and the identified stator resistance into the rotor permanent magnet flux linkage bias identification adjustable model, and outputting the observed values ​​of the direct-axis current and the quadrature-axis current; Obtaining measured values ​​of the direct-axis current and the quadrature-axis current, and determining a first difference between the measured value and the observed value of the direct-axis current, and a 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, adjusting the permanent magnet flux linkage bias in the rotor permanent magnet flux linkage bias identification adjustable model until the first difference and the second difference are both equal to 0; When the first difference and the second difference are both equal to 0, the permanent magnet flux linkage deviation in the rotor permanent magnet flux linkage deviation identification adjustable model is recorded as a target flux linkage deviation; Based on the target flux deviation and the first permanent magnet flux, the permanent magnet flux of the motor is determined.

6. The method according to claim 1, characterized in that The pulse high-frequency injection method is used to determine the direct-axis inductance and quadrature-axis inductance of the motor, including: Injecting a pulse high-frequency voltage into the direct axis of the motor to obtain a maximum value of the direct axis high-frequency current, and determining the direct axis inductance based on the maximum value of the direct axis high-frequency current; The pulse 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 linkage identified by the offline identification method, the method further includes: Performing a stall test on the motor, and identifying the first stator resistance offline based on the stall test parameters, where 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 linkage is offline identified based on the reverse drag test parameters, where the first permanent magnet flux linkage is the permanent magnet flux linkage of the motor rotor at a set temperature.

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

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the parameter online identification method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the parameter online identification method according to any one of claims 1 to 7.

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