Permanent magnet synchronous motor parameter identification method, device, equipment and storage medium

Abstract

The invention discloses a permanent magnet synchronous motor parameter identification method, device and equipment and a storage medium, and relates to the technical field of motor parameter tuning, and the method comprises the steps: constructing a voltage equation model under a synchronous rotating coordinate system under a field oriented control (FOC) frame, and injecting a sweep frequency signal to a d-axis voltage Ud and a q-axis voltage Uq of a permanent magnet synchronous motor; converting the sweep frequency signal into a three-phase PWM waveform through Park inverse transformation and space vector pulse width modulation, and applying the three-phase PWM waveform to a three-phase winding of a motor stator; three-phase current of a stator is collected, and d-axis current Id and q-axis current Iq are obtained through Clark conversion and Park conversion; fourier transform is carried out on the input sweep frequency signal and the output current signal, a time domain signal is converted into a frequency domain signal, and amplitude frequency and phase frequency data of the input signal and the output signal are obtained; and establishing an objective function based on amplitude-frequency and phase-frequency data, performing iterative optimization solution on the stator resistance Rs, the d-axis inductance Ld and the q-axis inductance Lq by using a genetic algorithm, and outputting an optimal parameter identification result.

Description

Technical Field

[0001] This invention relates to the field of motor parameter tuning technology, and in particular to a method, device, equipment and storage medium for identifying parameters of a permanent magnet synchronous motor. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in new energy vehicles, industrial automation, home appliances, and aerospace due to their high efficiency, high power density, and excellent control performance. Electric vehicles equipped with PMSMs can achieve energy efficiency of over 93%, approximately 5% to 10% higher than traditional induction motors, significantly improving driving range. In industrial applications, PMSMs typically have a power density of 1.5–3 kW / kg, far exceeding the 0.8–1.2 kW / kg of induction motors, saving space and reducing energy consumption. Furthermore, their torque response time is less than 10 ms, providing high control precision and making them suitable for high-dynamic performance scenarios. According to market research firm Statista, the global PMSM market size is projected to exceed $25 billion by 2027, with a compound annual growth rate exceeding 10%, confirming its continued growth trend.

[0003] Permanent magnet synchronous motors (PMSMs) utilize Field-Oriented Control (FOC) technology to transform three-phase current control into two-phase current control in a rotating coordinate system, thereby improving motor performance and efficiency. However, this also makes it difficult to obtain the motor's physical and electrical parameters, and establish its mathematical model. Although PMSMs, when combined with PID control algorithms that are independent of the model itself, exhibit certain performance in control accuracy and dynamic response, without knowing the model of the controlled object, the tuning of the PID controller and various digital compensators in the motor control loop is based on manual and blind adjustments. If testers lack a theoretical foundation in automatic control, it is difficult to quickly tune the PID controller parameters, and unreasonable parameter settings can lead to irreversible damage to the motor, posing a significant risk. Furthermore, during mass production, the tuning of the PID controller and various compensators involves a huge workload, resulting in extremely low production efficiency.

[0004] To address the issues of high workload and difficulty in achieving convergence in the parameter tuning of various compensators in the control circuit of permanent magnet synchronous motors. Summary of the Invention

[0005] The main objective of this invention is to propose a method, device, equipment, and storage medium for identifying parameters of a permanent magnet synchronous motor, aiming to solve the problems of large workload and difficulty in convergence when setting various compensator parameters in the control circuit of a permanent magnet synchronous motor.

[0006] To achieve the above objectives, the present invention proposes a method, apparatus, device, and storage medium for parameter identification of a permanent magnet synchronous motor, comprising the following steps: S1: Under the framework of field-oriented control (FOC), constructing a voltage equation model of the permanent magnet synchronous motor in a synchronous rotating coordinate system, and directing the voltage U of the permanent magnet synchronous motor along its d-axis. d and q-axis voltage U q Inject frequency sweep signal;

[0007] S2: The sweep frequency signal is converted into a three-phase PWM waveform through Park inverse transform and space vector pulse width modulation (SVPWM) and applied to the three-phase stator windings of the motor;

[0008] S3: Collect the three-phase stator current, and obtain the d-axis current I through Clark and Park transformations. d and q-axis current I q ;

[0009] S4: Perform Fourier transform on the input sweep frequency signal and the output current signal to convert the time domain signal into a frequency domain signal and obtain the amplitude frequency and phase frequency data of the input and output signals;

[0010] S5: Establish an objective function based on amplitude and phase frequency data, and use a genetic algorithm to adjust the stator resistance R. s d-axis inductance L d and q-axis inductance L q Perform iterative optimization to solve the problem and output the optimal parameter identification result.

[0011] In one embodiment, the voltage equation model described in step S1 is:

[0012]

[0013] Among them, U d U q The voltages along the d-axis and q-axis; i d i q R represents the current along the d-axis and q-axis; s Stator resistance; ψ m ω is the magnetic flux generated by a permanent magnet. e It represents the electric angular velocity.

[0014] In one embodiment, the frequency sweep signal mentioned in step S1 is:

[0015]

[0016] Where A is the amplitude and f is the frequency. Let t be the phase and t be the time.

[0017] In one embodiment, the genetic algorithm includes the following steps:

[0018] S51: R s L d and L q Encoded as genes on an individual's chromosome;

[0019] S52: Randomly generate the initial population;

[0020] S53: Calculate the fitness value based on the fitness function;

[0021] S54: Iteratively update the population according to the crossover probability and mutation probability;

[0022] S55: Output the optimal result when the fitness value converges or the termination condition is met.

[0023] In one embodiment, in steps S2 and S3:

[0024] U is transformed by the inverse Park transform d U q Convert to U α U β ;

[0025] The three-phase current I is converted using the Clark transformation. a I b I c Convert to I α I β ;

[0026] I through Park transformation α I β Convert to I d I q .

[0027] In one embodiment, Fourier transform is used in step S4 to perform time-frequency conversion, thereby obtaining the system's amplitude-frequency characteristic L(ω) and phase-frequency characteristic.

[0028] L(ω) = 20 * log 10 |G(jω)|

[0029]

[0030] L(ω): represents the amplitude-frequency gain value, in decibels (dB);

[0031] This represents the phase frequency value, in degrees (°).

[0032] G(jω): Represents the system transfer function.

[0033] A parameter identification device for a permanent magnet synchronous motor includes:

[0034] The sweep frequency signal generation module is used to inject sweep frequency voltage signals into the d-axis and q-axis;

[0035] The signal acquisition module is used to acquire the three-phase stator current and obtain I through Clark and Park transformations. d I q ;

[0036] The signal processing module is used to perform Fourier transform on the input and output signals and generate amplitude and phase frequency data;

[0037] The parameter identification module is used to identify the stator resistance R based on a genetic algorithm. s d-axis inductance L d and q-axis inductance L q Perform iterative optimization to solve the problem and output the optimal parameter result.

[0038] In one embodiment, the parameter identification module includes an objective function generation unit, a genetic operation unit, and an optimal parameter output unit.

[0039] A permanent magnet synchronous motor parameter identification device, characterized in that it includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the permanent magnet synchronous motor parameter identification method according to any one of claims 1-6.

[0040] A storage medium, characterized in that: the computer program stored in the storage medium can be executed by one or more processors, and when the computer program is executed by the processor, it can implement the permanent magnet synchronous motor parameter identification method according to any one of claims 1-6.

[0041] This invention provides a method for parameter identification of a permanent magnet synchronous motor. The technical solution includes: first, injecting sinusoidal sweep signals into the d-axis and q-axis of the permanent magnet synchronous motor; this signal is then converted into U... α / U β The SVPWM module then generates a three-phase PWM waveform to drive the motor; the three-phase current I is acquired in real time. a I b I c I is obtained through Clark transformation α / I β Then, I is obtained through Park transformation. d / I q The response; time-frequency conversion is performed using Fourier transform to calculate the system's amplitude-frequency and phase-frequency characteristics; finally, the stator resistance R... s q-axis inductance L q and d-axis inductance L dFor the parameters to be identified, a fitness function is established, and a genetic algorithm (including encoding, selection, crossover, and mutation operations) is used for iterative optimization to match the theoretical model response with the measured data.

[0042] Select U d / U q The core function of the injection node is that it directly corresponds to the voltage equation model of the permanent magnet synchronous motor in the synchronous rotating coordinate system, avoiding the complex coupling of injection in the stationary coordinate system. It can be integrated with the existing FOC control architecture and achieve efficient data acquisition through software without hardware modification.

[0043] R was successfully identified using this method. s L q and L d The parameters have an identification error of ≤5%, which solves the problem of large workload and difficulty in convergence of controller parameter tuning caused by unclear motor model, and provides an accurate model basis for control system optimization. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0045] Figure 1 A schematic diagram of a parameter identification method for permanent magnet synchronous motors.

[0046] Figure 2 This is a schematic diagram of a parameter identification device for a permanent magnet synchronous motor.

[0047] Figure 3 Schematic diagram of parameter identification process for permanent magnet synchronous motors based on genetic algorithm

[0048] Figure 4 For U q Schematic diagram of the input sweep frequency signal waveform in the first 10 seconds;

[0049] Figure 5 For I q Schematic diagram of the frequency sweep signal waveform output in the first 10 seconds;

[0050] Figure 6 A schematic diagram of the Bode corresponding to the parametric model;

[0051] Figure 7 This is a schematic diagram of the Bode corresponding to the theoretical model;

[0052] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0054] It should be noted that if directional indicators (such as up, down, left, right, front, back, etc.) are involved in the embodiments of this invention, these directional indicators are only used to explain the relative positional relationships and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly. Unless otherwise explicitly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0055] Furthermore, if the embodiments of the present invention involve descriptions using terms such as "first," "second," etc., these descriptions are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Furthermore, the use of "and / or" or "and / or" throughout the text includes three parallel options; for example, "A and / or B" includes option A, option B, or options where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0056] The present invention proposes a method, apparatus, device, and storage medium for parameter identification of a permanent magnet synchronous motor, comprising the following steps: S1: Under the framework of field-oriented control (FOC), constructing a voltage equation model of the permanent magnet synchronous motor in a synchronous rotating coordinate system, and directing the voltage U of the permanent magnet synchronous motor along its d-axis. d and q-axis voltage U q Inject frequency sweep signal;

[0057] S2: The sweep frequency signal is converted into a three-phase PWM waveform through Park inverse transform and space vector pulse width modulation (SVPWM) and applied to the three-phase stator windings of the motor;

[0058] S3: Collect the three-phase stator current, and obtain the d-axis current I through Clark and Park transformations. d and q-axis current I q ;

[0059] S4: Perform Fourier transform on the input sweep frequency signal and the output current signal to convert the time domain signal into a frequency domain signal and obtain the amplitude frequency and phase frequency data of the input and output signals;

[0060] S5: Establish an objective function based on amplitude and phase frequency data, and use a genetic algorithm to adjust the stator resistance R. s d-axis inductance L d and q-axis inductance L q Perform iterative optimization to solve the problem and output the optimal parameter identification result.

[0061] In one embodiment, the voltage equation model described in step S1 is:

[0062]

[0063] Among them, U d U q The voltages along the d-axis and q-axis; i d i q R represents the current along the d-axis and q-axis; s Stator resistance; ψ m ω is the magnetic flux generated by a permanent magnet. e It represents the electric angular velocity.

[0064] In one embodiment, the frequency sweep signal mentioned in step S1 is:

[0065]

[0066] Where A is the amplitude and f is the frequency. Let t be the phase and t be the time.

[0067] In one embodiment, the genetic algorithm includes the following steps:

[0068] S51: R s L d and L q Encoded as genes on an individual's chromosome;

[0069] S52: Randomly generate the initial population;

[0070] S53: Calculate the fitness value based on the fitness function;

[0071] S54: Iteratively update the population according to the crossover probability and mutation probability;

[0072] S55: Output the optimal result when the fitness value converges or the termination condition is met.

[0073] In one embodiment, in steps S2 and S3:

[0074] U is transformed by the inverse Park transform d U q Convert to U α U β ;

[0075] The three-phase current I is converted using the Clark transformation. a I b I c Convert to I α I β ;

[0076] I through Park transformation α I β Convert to I d I q .

[0077] In one embodiment, Fourier transform is used in step S4 to perform time-frequency conversion, thereby obtaining the system's amplitude-frequency characteristic L(ω) and phase-frequency characteristic.

[0078] L(ω) = 20 * log 10 |G(jω)|

[0079]

[0080] L(ω): represents the amplitude-frequency gain value, in decibels (dB);

[0081] This represents the phase frequency value, in degrees (°).

[0082] G(jω): Represents the system transfer function.

[0083] A parameter identification device for a permanent magnet synchronous motor includes:

[0084] The sweep frequency signal generation module is used to inject sweep frequency voltage signals into the d-axis and q-axis;

[0085] The signal acquisition module is used to acquire the three-phase stator current and obtain I through Clark and Park transformations. d I q ;

[0086] The signal processing module is used to perform Fourier transform on the input and output signals and generate amplitude and phase frequency data;

[0087] The parameter identification module is used to identify the stator resistance R based on a genetic algorithm. s d-axis inductance L d and q-axis inductance L q Perform iterative optimization to solve the problem and output the optimal parameter result.

[0088] In one embodiment, the parameter identification module includes an objective function generation unit, a genetic operation unit, and an optimal parameter output unit.

[0089] A permanent magnet synchronous motor parameter identification device, characterized in that it includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the permanent magnet synchronous motor parameter identification method according to any one of claims 1-6.

[0090] A storage medium, characterized in that: the computer program stored in the storage medium can be executed by one or more processors, and when the computer program is executed by the processor, it can implement the permanent magnet synchronous motor parameter identification method according to any one of claims 1-6.

[0091] The following description, using a preferred embodiment, illustrates the content related to the above embodiments:

[0092] Because the physical and electrical parameters of the motor are unclear, the model of the controlled object is unclear. In order to solve the problems of difficult convergence and large workload in the tuning of various compensator parameters in the control loop due to the unclear model of the permanent magnet synchronous motor, this invention designs a permanent magnet synchronous motor system identification algorithm based on frequency sweep signal from the perspective of controller parameter design. This algorithm enables the establishment of the motor model, helps in the design of various compensator parameters in the control loop, and provides a basis for rapid parameter tuning.

[0093] Since permanent magnet synchronous motors (PMSMs) convert three-phase current control into two-phase current control in a rotating coordinate system based on field-oriented control (FOC) technology, it is difficult to obtain the physical and electrical parameters of the motor and establish its mathematical model. This makes it difficult to quickly tune the parameters of various compensators in the motor control circuit. To address this issue, based on the voltage equation of the PMSM, specifically formulas (1) and (2), we consider the sinusoidal sweep frequency signals of the PMSM's Ud and Uq and obtain the amplitude, phase, and frequency data of the motor by real-time detection of the PMSM current. Then, based on the idea of ​​iterative calculation using the genetic algorithm in intelligent algorithms to find the optimal solution, we complete the identification of electrical parameters such as the resistance and inductance of the PMSM stator, and finally realize the mathematical modeling of the motor.

[0094]

[0095] in,

[0096] U d U q Voltages along the d-axis and q-axis;

[0097] i d i q Currents along the d-axis and q-axis;

[0098] R s Stator resistance;

[0099] ψ m : Magnetic flux generated by permanent magnets;

[0100] ω e Electric angular velocity (rad / s), ωe=p·ωm, where p is the number of pole pairs and ωm is the mechanical angular velocity.

[0101] After feedback decoupling, Uq can be simplified to the following formula (3), and then converted into the transfer function form, as shown in (4).

[0102]

[0103] The present invention proposes a permanent magnet synchronous motor U d and U q The electrical parameter identification algorithm for frequency sweep signals mainly includes three stages: system frequency sweep stage, time-frequency domain signal conversion stage, and motor parameter identification stage. The three stages will be described in detail below.

[0104] (1) First stage: U d and U q Frequency sweeping phase.

[0105] This invention selected U d and U qAs the input interface for the swept frequency signal, the signal is converted into U after undergoing the inverse Park transform (specifically, the form is Equation (4)). α and U β After passing through the SVPWM module, the signal is converted into a three-phase PWM waveform. This PWM waveform then enters three complementary channels and subsequently into the three-phase coils of the permanent magnet synchronous motor stator, forming a directional magnetic field that drives the rotor to rotate. The current in the three-phase coils is collected in real time and denoted as I. A I B I C Then, the three-phase current is subjected to Clark transformation (specifically, formula (5)) to obtain I. α and I β Finally, after Park transformation (specifically in the form of formula (6)), I is obtained. d and I q .

[0106] U α =U d cosθ-U q sinθ (4a)

[0107] U β =U d sinθ+U q cosθ (4b)

[0108] in,

[0109] U α U β : Represents the voltage vector in a two-phase stationary coordinate system (α and β axes);

[0110] θ: The angle of rotation of the motor, in degrees (°).

[0111] I d =I α cosθ+I β sinθ (5a)

[0112] I q =-I α sinθ+I β cosθ (5b)

[0113] in,

[0114] I α I β : Represents the current vector in a two-phase stationary coordinate system (α and β axes);

[0115] I d I q : Represents the current vector in a two-phase rotating coordinate system (d and q axes).

[0116]

[0117] in,

[0118] I a I b I c : These represent the three-phase stator current values ​​of the motor, in amperes (A), and satisfy I. a +I b +I c =0.

[0119] (2) Second stage: Time-frequency domain signal conversion stage.

[0120] The input sinusoidal sweep frequency signal and the output Id and Iq signals acquired in the first stage are time-domain signals. To facilitate the parameterized design of the motor's closed-loop controller, the motor's mathematical model often adopts a transfer function form. Therefore, it is necessary to transform the mathematical relationship between the input and output signals into the frequency domain.

[0121] The conversion of time-domain signals into frequency-domain signals is mainly achieved using the Fourier transform method, the main form of which is shown in formula (6). The resulting datasets after performing Fourier transforms on the input and output signals are denoted as Yu and Yo, respectively, with each data point being a complex number. The modulus and angle of the complex numbers are calculated for the Yu and Yo data, and the results are denoted as mag_u, pha_u, mag_o, and pha_o, respectively.

[0122] The amplitude and phase frequencies between the output and input signals are solved using the Bode plot formula (specifically (7)) and the phase formula (specifically (8)), denoted as M and P.

[0123] L(ω) = 20 * log 10 |G(jω)| (7)

[0124]

[0125] in,

[0126] L(ω): represents the amplitude-frequency gain value, in decibels (dB);

[0127] This represents the phase frequency value, in degrees (°).

[0128] G(jω): Represents the system transfer function.

[0129] (3) Third stage: Motor parameter identification stage.

[0130] Motor parameter identification is based on a genetic algorithm. The basic principle is to establish an objective function, initialize the population based on empirical values, and iteratively eliminate individuals with inferior genes while allowing individuals with superior genes to crossbreed. This process uses gene mutation to escape local optima until the optimal gene (optimal solution) is obtained. After determining the objective function (fitness function), the specific steps for motor parameter identification are as follows:

[0131] s1: Select an encoding method for solving the optimization problem;

[0132] s2: Randomly generate an initial population of N chromosomes {pop(k), k=0}, where the chromosome of a single individual is determined by the stator resistance R of a permanent magnet synchronous motor. s q-axis inductance L q and d-axis inductance L d composition;

[0133] s3: Pop each chromosome in the population i (k) Calculate the fitness function, which is in the form shown in (9);

[0134] f i =fitness(pop) i (k)) (9)

[0135] in,

[0136] fi: represents the fitness value of the i-th individual;

[0137] fitness: Initializes the fitness transfer function.

[0138] s4: If the termination rule is met, proceed to s9; otherwise, calculate the probability.

[0139] s5: Randomly select some chromosomes from pop(k) with probability pi to form a new population newpop (where elements in pop(k) can be selected repeatedly), specifically in the form of (10);

[0140] newpop(k+1)={popi(k),i=1,2,···,N} (10)

[0141] s6: Through mating, according to the mating probability p c A mating population with N chromosomes is obtained by crosspop(k+1);

[0142] s7: With a small mutation probability pm, a gene on a chromosome is mutated, forming a mutant population mutpop(k+1);

[0143] s8: Let k = k+1 and popi(k) = mutpop(k+1), then return to s3;

[0144] s9: Terminate the calculation and output the optimal result (R) s L q and L d) .

[0145] After using the permanent magnet synchronous motor parameter identification algorithm designed in this invention, the resistance R in the rotating coordinate system of the permanent magnet synchronous motor is completed. s q-axis inductance L q and d-axis inductance L d By comparing the actual Bode plot of the motor with the theoretical model Bode plot, it can be seen that the identification error is ≤5%.

[0146] Table 1. Results of iterative calculations for permanent magnet synchronous motor identification.

[0147] Number of iterations Value Step length Error value First-order expectation 0 <![CDATA[1.77255×10 -07 ]]> - <![CDATA[1.49×10 4 ]]> 3.07e+05 1 <![CDATA[1.77158×10 -07 ]]> 0.0759 19.1 3.07e+05 2 <![CDATA[1.77158×10 -07 ]]> 0.0108 0.321 11 3 <![CDATA[1.77158×10 -07 ]]> <![CDATA[1.87×10 -5 ]]> 0.00554 0.00329

[0148] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0149] The motor identified in this embodiment is a permanent magnet synchronous motor, and its specific parameters are as follows:

[0150] ●Number of pole pairs: 4 pole pairs

[0151] ● Motor resistance measurement: 2.5Ω

[0152] • Inductance measurements for the Q and D axes: L q ≈L d =0.01H;

[0153] ● Rotor flux linkage measurement value: ψ m =0.175;

[0154] The specific identification method is as follows:

[0155] (1) First, the FOC control framework of the permanent magnet synchronous motor is built. Specifically, the inverse Park transformation and SVPWM module are built in the STM32 chip. The output PWM signal enters the three-phase drive chip to drive the permanent magnet synchronous motor to rotate. The drive chip detects the three-phase current in real time and transmits the current value to the STM32 chip. After Clark transformation and Park transformation, it becomes the q-axis and d-axis current signal values ​​and is recorded and saved.

[0156] (2) A sampling sinusoidal sweep signal is constructed inside the STM32. The specific mathematical form is shown in formula (11). The sweep signal is generated by changing the frequency of the needle signal. In this embodiment, the amplitude of the sweep signal A = 1; the starting frequency is 0Hz, the cutoff frequency is 3000Hz, the sweep time is 100s, and the frequency change rate is 30Hz / s; the phase value is 0°, and the waveform of the signal is as follows: Figure 1 As shown. The constructed sweep frequency signal is input to U. q In the axis, and U d The input is set to 0.

[0157]

[0158] (3) Save the q-axis and d-axis currents collected during the frequency sweep period. According to the Nyquist sampling theorem, and to ensure the subsequent Fourier transform processing, the sampling frequency is set to 10000Hz; the sampled signal dataset is recorded as Iq_o.

[0159] (4) Using Fourier transform to process Uq and Iq_o, the complex form of the data is obtained, denoted as mag_u, pha_u, mag_o, pha_o. According to formulas (6) and (7), the corresponding relationship between the amplitude and phase values ​​of the output signal and the input signal as a function of frequency can be calculated. The amplitude-frequency results are denoted as M and P, and their Bode plots are drawn. Figure 1 As shown.

[0160] (5) Establish the fitness function, specifically as shown in formula (12), and then follow the process. Figure 2 The genetic algorithm is used to optimize the parameters, ultimately achieving accurate parameter identification. In this embodiment, the iteration terminates when the fitness function result is ≤0.01, and the identification result R is output. s = 2.5212Ω (actual value R) s =2.5Ω), L q =0.0099H (actual value L) q =0.01H).

[0161] The iteration results are shown in Table 1.

[0162]

[0163] Note that the above technical features constitute the preferred embodiment of the present invention, which has strong adaptability and the best implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

[0164] It should be understood that the terms "one embodiment" or "one example" throughout the specification mean that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in one example" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also recognize that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to the invention.

[0165] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0166] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It is particularly important to note that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0167] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for identifying parameters of a permanent magnet synchronous motor, characterized in that, The specific steps include: S1: Within the framework of Field Oriented Control (FOC), construct the voltage equation model of the permanent magnet synchronous motor in the synchronous rotating coordinate system, and express the voltage U of the permanent magnet synchronous motor along the d-axis. d and q-axis voltage U q Inject frequency sweep signal; S2: The sweep frequency signal is converted into a three-phase PWM waveform through Park inverse transform and space vector pulse width modulation (SVPWM) and applied to the three-phase stator windings of the motor; S3: Collect the three-phase stator current, and obtain the d-axis current I through Clark and Park transformations. d and q-axis current I q ; S4: Perform Fourier transform on the input sweep frequency signal and the output current signal to convert the time domain signal into a frequency domain signal and obtain the amplitude frequency and phase frequency data of the input and output signals; S5: Establish an objective function based on amplitude and phase frequency data, and use a genetic algorithm to adjust the stator resistance R. s d-axis inductance L d and q-axis inductance L q Perform iterative optimization to solve the problem and output the optimal parameter identification result.

2. The method for parameter identification of a permanent magnet synchronous motor as described in claim 1, characterized in that, The voltage equation model mentioned in step S1 is as follows: Among them, U d U q The voltages along the d-axis and q-axis; i d i q R represents the current along the d-axis and q-axis; s Stator resistance; ψ m ω is the magnetic flux generated by the permanent magnet. e ω is the electric angular velocity.

3. The method for parameter identification of a permanent magnet synchronous motor as described in claim 1, characterized in that, The frequency sweep signal mentioned in step S1 is: Where A is the amplitude and f is the frequency. Let t be the phase and t be the time.

4. The method for parameter identification of a permanent magnet synchronous motor as described in claim 1, characterized in that: The genetic algorithm includes the following steps: S51: with R s L d and L q Encoded as genes on an individual's chromosome; S52: Randomly generate the initial population; S53: Calculate the fitness value based on the fitness function; S54: Iteratively update the population according to the crossover probability and mutation probability; S55: Output the optimal result when the fitness value converges or the termination condition is met.

5. The method for parameter identification of a permanent magnet synchronous motor as described in claim 1, characterized in that, In steps S2 and S3: U is transformed by the inverse Park transform d U q Convert to U α U β ; The three-phase current I is converted using the Clark transformation. a I b I c Convert to I α I β ; I through Park transformation α I β Convert to I d I q .

6. The method for parameter identification of a permanent magnet synchronous motor as described in claim 1, characterized in that: In step S4, Fourier transform is used to perform time-frequency conversion to obtain the system's amplitude-frequency response L(ω) and phase-frequency response. L(ω)=20*log 10 |G(jω)| L(ω): represents the amplitude-frequency gain value, in decibels (dB); This represents the phase frequency value, in degrees (°). G(jω): Represents the system transfer function.

7. A parameter identification device for a permanent magnet synchronous motor, characterized in that: The permanent magnet synchronous motor parameter identification device is used to execute the permanent magnet synchronous motor parameter identification method according to any one of claims 1-6, including: The sweep frequency signal generation module is used to inject sweep frequency voltage signals into the d-axis and q-axis; The signal acquisition module is used to acquire the three-phase stator current and obtain I through Clark and Park transformations. d I q ; The signal processing module is used to perform Fourier transform on the input and output signals and generate amplitude and phase frequency data; The parameter identification module is used to identify the stator resistance R based on a genetic algorithm. s d-axis inductance L d and q-axis inductance L q Perform iterative optimization to solve the problem and output the optimal parameter result.

8. The parameter identification device for a permanent magnet synchronous motor as described in claim 7, characterized in that, The parameter identification module includes an objective function generation unit, a genetic operation unit, and an optimal parameter output unit.

9. A parameter identification device for a permanent magnet synchronous motor, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, which, when executed by the processor, implements the parameter identification method for permanent magnet synchronous motors according to any one of claims 1-6.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors. When the computer program is executed by the processor, it can implement the parameter identification method for permanent magnet synchronous motors as described in any one of claims 1-6.

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