A method for MTPA control based on permanent magnet synchronous motor

Abstract

This invention relates to a MTPA control method based on a permanent magnet synchronous motor (PMSM), and pertains to the field of motor control. The invention addresses the problem in existing MTPA control methods that neglect parameter variations, making precise MTPA control difficult. This invention obtains the direct-axis inductance L based on a model reference adaptive system. d And stator resistance R; based on direct-axis inductance L d A torque equation is established with respect to the stator resistance R; a virtual high-frequency signal is injected to obtain the current angle at the MTPA operating point; based on the current angle at the MTPA operating point and the stator current vector, the quadrature-axis current and direct-axis current during MTPA operation are obtained. This invention constructs a new torque estimation equation with higher accuracy by using these parameters and high-frequency signal injection, thus achieving high-precision motor control.

Description

Technical Field

[0001] This invention relates to the field of motor control, and more particularly to an MTPA control method based on a permanent magnet synchronous motor. Background Technology

[0002] With the rapid development of the automotive industry, environmental protection and resource shortages have become increasingly prominent issues. Therefore, electric vehicles have gained significant attention. In electric vehicles, permanent magnet synchronous motors (MTPAs) offer advantages such as high power density, small size, and light weight, gradually becoming one of the mainstream traction drive motors. MTPAs are widely used as a highly efficient control method for permanent magnet synchronous motors, capable of outputting maximum torque under a certain stator current, thus improving the overall system efficiency. Mainstream MTPA control methods include: formula method, parameter identification method, table lookup method, automatic search method, and high-frequency signal injection method.

[0003] The formula method obtains the MTPA solution by differentiating the partial derivative of the motor torque with respect to the current angle to zero. However, this method cannot obtain an accurate MTPA solution because it ignores parameter changes. The parameter identification method, based on the formula method, combines online parameter identification to obtain accurate motor parameters in real time, improving the accuracy of motor operation. However, this method relies on tedious parameter monitoring and estimation, thus requiring the controller to have high computing power and additional hardware. The table lookup method requires finding the motor's MTPA point in advance through experiments or simulations, and then using a table lookup to make the motor operate at the MTPA point. However, this method consumes a lot of resources and time to find the MTPA point in advance. The automatic search method is a control strategy that adjusts the stator current vector angle by continuously giving small step angles under steady-state operation to achieve the optimal MTPA. However, this method has a slow convergence speed and low torque control accuracy. The high-frequency signal injection method observes the feedback of the high-frequency signal injected into the system, calculates and analyzes it to obtain the optimal operating state. However, the high-frequency current injected by this method increases system torque ripple and additional power loss. Furthermore, the torque equation constructed in the virtual signal injection method in the prior art ignores the internal parameters of the motor, resulting in a large error.

[0004] Existing parameter identification methods include recursive least squares, extended Kalman filtering, and neural networks. Recursive least squares, based on previous model parameter estimates, modifies the data obtained in the previous step using currently available data to obtain the current estimate of the model parameters. However, recursive least squares suffers from data saturation during the recursive calculation process, placing high demands on system hardware and software programming. Extended Kalman filtering integrates the discrete-space model into the filtering algorithm, achieving optimal estimation of the system state by minimizing the estimated covariance. This method is computationally complex when identifying multiple parameters simultaneously, making it quite challenging. Neural networks offer good convergence characteristics, but their algorithms are highly complex, thus limiting their practical applications.

[0005] Therefore, those skilled in the art are dedicated to developing an MTPA control method based on a permanent magnet synchronous motor. Summary of the Invention

[0006] In view of the above-mentioned deficiencies of the prior art, the present invention provides an MTPA control method based on a permanent magnet synchronous motor, which constructs a torque estimation equation with higher accuracy, obtains a more accurate MTPA operating point, and has higher robustness and response speed.

[0007] To achieve the above objectives, the present invention provides an MTPA control method based on a permanent magnet synchronous motor, comprising:

[0008] S1. Obtain the direct-axis inductance L based on the model reference adaptive system. d and stator resistance R;

[0009] S2, according to the direct-axis inductance L d Establish the torque equation with respect to the stator resistance R;

[0010] S3. Inject a virtual high-frequency signal to obtain the current angle of the MTPA operating point;

[0011] S4. Obtain the quadrature-axis current and direct-axis current of the MTPA during operation based on the current angle at the MTPA operating point and the stator current vector.

[0012] Furthermore, the reference model of the model-referenced adaptive system is:

[0013]

[0014] in: i and u are the stator current and voltage, respectively; L d L q These represent the stator d-axis and q-axis inductances, respectively; R is the stator resistance; ψ f Permanent magnet flux linkage; ω is electric angular velocity;

[0015] The adjustable model of the model reference adaptive system is:

[0016]

[0017] in: M is the gain matrix. e represents the error between the adjustable model and the reference model.

[0018] Furthermore, the torque equation described in step S2 is as follows:

[0019]

[0020] in, It is a quadrature axis inductor. For permanent magnet flux linkage, T e Represents electromagnetic torque, where P is the number of pole pairs, and i d i q u d u q These are the stator dq-axis current and voltage, respectively.

[0021] Furthermore, step S3 includes:

[0022] S31. Inject a virtual high-frequency signal Δβ to obtain the torque equation after injecting the virtual high-frequency signal;

[0023] S32. Combine the torque equation described in step S31 with sin(ω) h t) multiply;

[0024] S33. The first-order partial derivative of the electromagnetic torque is obtained after filtering out high-frequency terms using a low-pass filter. make The current angle at the MTPA operating point is obtained by integration.

[0025] Furthermore, the torque equation after the virtual high-frequency signal injection is:

[0026]

[0027] Where, Δβ=Asin(ω h t) is a high-frequency current angle signal; This represents the torque after a high-frequency signal is injected. These are the stator dq-axis currents after high-frequency signal injection.

[0028] Compared with the prior art, the present invention has the following technical effects:

[0029] 1. This invention fully considers the impact of parameter changes during motor operation and adopts a model reference adaptive parameter identification method to obtain the direct-axis inductance L of the motor in real time. dAnd the stator resistance R, and by injecting this parameter with a high-frequency signal, a new torque estimation equation with higher accuracy is obtained;

[0030] 2. This invention uses only a low-pass filter in the signal extraction process to obtain a more accurate MTPA operating point, and has higher robustness and response speed.

[0031] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0032] Figure 1 This is a basic flowchart of a virtual signal injection-based MTPA control method for parameter identification according to the present invention;

[0033] Figure 2 This is a comparison diagram of the traditional and improved signal extraction methods of this invention;

[0034] Figure 3 This is a system control block diagram of the present invention;

[0035] Figure 4 This is a control block diagram of the virtual signal module of the present invention. Detailed Implementation

[0036] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0037] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0038] Some exemplary embodiments of the invention have been described for illustrative purposes. It should be understood that the invention may be implemented in other ways not specifically shown in the accompanying drawings.

[0039] To achieve the above objectives, this invention provides an MTPA control method based on a permanent magnet synchronous motor, such as... Figure 1 and Figure 3 As shown, it includes:

[0040] S1. Obtain the direct-axis inductance L based on the model reference adaptive system. d and stator resistance R;

[0041] Due to the parameter L in the torque equation after the virtual high-frequency signal injection d R and R change with temperature and magnetic saturation, thus affecting the accuracy of the control system. Therefore, L is chosen. d The two parameters, R and R, are used for identification.

[0042] Model reference adaptation (MRA) involves adjusting the parameters of an adjustable model and using a pre-designed adaptive law to gradually converge it to the actual parameters in a reference model. In comparison, MRA is simpler in principle, more accurate in its identification, and converges faster.

[0043] In this embodiment, in the model reference adaptive system, the input quantity u = [u d u q ] T The output of the reference model is y = i = [i d i q ] T The output of the adjustable model Output error

[0044] The reference model for the adaptive system is:

[0045]

[0046] in: i and u are the stator current and voltage, respectively; L d L q These represent the stator d-axis and q-axis inductances, respectively; R is the stator resistance; ψ f The flux linkage of a permanent magnet; ω is the electric angular velocity.

[0047] The adjustable model of the reference adaptive system is:

[0048]

[0049] in: M is the gain matrix.

[0050] The error equation of the model reference adaptive system is obtained by subtracting the adjustable model from the reference model, i.e., the parameter identification model equation is:

[0051]

[0052]

[0053] The method for determining the gain matrix is ​​as follows: select an appropriate gain matrix such that each pole of the adjustable model has a negative real part, so as to ensure that the designed identifier has good asymptotic stability and satisfactory dynamic response.

[0054] make The parameter identification model equation is transformed into:

[0055]

[0056] The Popov integral inequality is:

[0057]

[0058] In the formula, for any t≥0, γ is a finite positive constant that does not depend on t.

[0059] L d Using R as the object to be identified, substituting w into the above formula yields:

[0060]

[0061] Equation (3) can be decomposed into two equations, as shown below:

[0062]

[0063]

[0064] Because of L d The PI adaptive law is usually expressed as:

[0065]

[0066] We will first analyze the adaptive law for direct-axis inductor identification; the derivation of the adaptive law for stator resistance R is similar. Substituting equation (6) into equation (4), η1(0,t1) can be further decomposed into two sub-inequalities:

[0067]

[0068]

[0069] From the above formula, we can obtain the parameter L. d The PI adaptive law is:

[0070]

[0071] Where: k1 and τ1 are respectively L d The proportional and integral coefficients of the adaptive law.

[0072] Similarly, the adaptive law for the stator resistance R obtained through the above calculations is:

[0073]

[0074] Where: k2 and τ2 are the proportional and integral coefficients of the adaptive law of R, respectively.

[0075] S2, according to the direct-axis inductance L d Establish the torque equation with respect to the stator resistance R;

[0076] The torque equation is as follows:

[0077]

[0078] in, It is a quadrature axis inductor. For permanent magnet flux linkage, T e Represents electromagnetic torque, where P is the number of pole pairs, and i d i q u d u q These represent the stator d-axis current and voltage, respectively, which reduces the quadrature-axis inductance parameter L. q With the permanent magnet flux linkage parameter ψ f The use of, and the stator direct-axis inductance L d The stator resistor R is more accurate.

[0079] S3. Inject a virtual high-frequency signal to obtain the current angle of the MTPA operating point;

[0080] The virtual signal injection method is a type of high-frequency signal injection method. Its main characteristic is the reconstruction of the torque equation, followed by the injection of a high-frequency signal into this mathematical model. Closed-loop control is then used to make the first partial derivative of the torque with respect to the current angle zero, thus obtaining the MTPA solution. This avoids the jitter and additional losses caused by high-frequency signal injection. Specifically, it includes:

[0081] S31. Inject a virtual high-frequency signal Δβ to obtain the torque equation after injecting the virtual high-frequency signal, which is:

[0082]

[0083] in,

[0084] Where, Δβ=Asin(ω h t) is a high-frequency current angle signal; This represents the torque after a high-frequency signal is injected. These are the stator dq-axis currents after high-frequency signal injection.

[0085] The torque model containing high-frequency information is expanded using the Taylor formula, i.e.:

[0086]

[0087] S32. Combine the torque equation described in step S31 with sin(ω) h Multiplying t) together, we get:

[0088]

[0089] in,

[0090] S33. The first-order partial derivative of the electromagnetic torque is obtained after filtering out high-frequency terms using a low-pass filter. make The current angle β at the MTPA operating point is obtained by integration. MTPA .

[0091] S4. Based on the current angle at the MTPA operating point and the stator current vector, the quadrature-axis current and direct-axis current of the MTPA during operation are obtained as follows:

[0092] i q-MTPA =I s sinβ MTPA ;

[0093] i d-MTPA =I s cosβ MTPA ;

[0094] Among them, I s i is the stator current vector. q-MTPA i is the quadrature axis current during MTPA operation. d-MTPA The direct-axis current during MTPA operation is given by the electrical angular velocity ω. r The electric angular velocity setpoint is obtained through the speed regulator.

[0095] Figure 2 This is a comparison diagram of the traditional and improved signal extraction methods of this invention. Existing virtual signal injection methods require a bandpass filter and a low-pass filter to extract the amplitude of the first-order partial derivative signal of torque with respect to current angle, which introduces delay and slows down the response speed. This invention utilizes the characteristic of ignoring the bandpass filter, simplifying the calculation and improving the system's response speed.

[0096] Figure 4 This is the control block diagram of the virtual signal module. This invention avoids the problems of traditional torque equations that ignore parameter changes, making it impossible to accurately calculate electromagnetic torque and obtain precise MTPA solutions. This invention is based on the identified stator direct-axis inductance L... d The stator resistance R is used to construct a torque equation with higher accuracy, and the quadrature axis inductance L is utilized. q With permanent magnet magnetic flux ψ fThe torque equation was further simplified, omitting the stator quadrature-axis inductance L. q and permanent magnet flux linkage ψ f The reduced number of parameters in the model allows for accurate estimation of electromagnetic torque, thereby improving the robustness of the system.

[0097] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method of MTPA control based on permanent magnet synchronous motor, characterized in that, The method comprises the following steps: S1, direct axis inductance L obtained from a model reference adaptive system d and stator resistance R; The direct-axis inductance L d The adaptive laws of the stator resistance R and the rotor resistance Rr are respectively ； ； Wherein, k1, τ1 are the proportional, integral coefficients of L d The proportional, integral coefficients of the adaptive law of R; k2, τ2 are the proportional, integral coefficients of the adaptive law of R; S2, the direct axis inductance L d and the stator resistance R to establish a torque equation; S3, injecting a virtual high-frequency signal to obtain a current angle of the MTPA operating point; S4, obtaining a quadrature-axis current and a direct-axis current of the MTPA operation according to the current angle of the MTPA operating point and a stator current vector.

2. The MTPA control method based on permanent magnet synchronous motor according to claim 1, characterized in that, The reference model of the model reference adaptive system is: ； wherein: ; ; ; i, u are stator currents and voltages, respectively; L d , L q are stator d-q axis inductances; R is stator resistance; ψ f is permanent magnet flux linkage; ω is electrical angular velocity; The adjustable model of the model reference adaptive system is: ； wherein: ; ; ; is a gain matrix, .

3. The MTPA control method based on permanent magnet synchronous motor according to claim 2, characterized in that, The torque equation in step S2 is: ； wherein, is the quadrature axis inductance, is the permanent magnet flux linkage, T e represents the electromagnetic torque, P is the number of pole pairs, i d , i q , u d , u q are the stator d-q axis currents and voltages, respectively.

4. The MTPA control method based on permanent magnet synchronous motor according to claim 3, characterized in that, Step S3 comprises: S31, injecting a virtual high-frequency signal ∆β to obtain a torque equation after the virtual high-frequency signal is injected; S32, multiplying the torque equation of step S31 by sin(ω h t). S33, obtain the first order partial derivative of electromagnetic torque ∂T after filtering out the high frequency term by low pass filter e / ∂β, and adjust the first order partial derivative of electromagnetic torque ∂T e / ∂β = 0, and obtain the current angle of MTPA operating point by integration.

5. The MTPA control method based on permanent magnet synchronous motor according to claim 4, characterized in that, The torque equation after the virtual high-frequency signal is injected is: ； wherein, is a high frequency current angle signal; represents a torque after high frequency signal injection; , are stator d-q axis currents after high frequency signal injection, respectively.

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