Methods, apparatus, electronic devices and storage media for impedance fitting of passive devices

Abstract

This application provides a method, apparatus, electronic device, and storage medium for impedance fitting of passive devices. The method includes: acquiring an impedance characteristic curve obtained through actual measurement; dividing the impedance characteristic curve to obtain M sequentially arranged resonant bands, where the impedance model corresponding to a wide resonant peak is a series-connected RLC parallel circuit of p phases, where p is an integer greater than or equal to 2; based on the initial parameters of the i-th impedance model and its corresponding optimization floating range, iteratively optimizing using a preset optimization algorithm, and using the set of parameters with the smallest error compared to the actual parameters, and whose error is less than a set value, as the model parameters of the i-th impedance model; and obtaining an equivalent impedance model for replacing the passive device based on the model parameters of the impedance models corresponding to the M resonant bands. This enables an accurate expression of the impedance of passive devices, thus facilitating electromagnetic interference suppression design based on the equivalent impedance model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic component modeling, in particular to a passive device impedance fitting method and device, electronic equipment and storage medium. BACKGROUND

[0002] In the field of electronic components, there is a design requirement for electromagnetic compatibility. For example, in the field of new energy vehicles, such as fuel cell vehicles, the use of high-power power electronic devices during operation generates strong electromagnetic interference, which affects the normal operation of surrounding sensitive devices and thus threatens the safety of vehicle operation. From the perspective of electromagnetic compatibility management and design, it is necessary to model the conducted interference of the device to achieve system noise pre-evaluation. Passive device impedance is an important part of conducted interference modeling. Among them, the impedance behavior model selects a reasonable port equivalent model to describe the impedance characteristics, which has the characteristics of high precision and strong applicability in a wide frequency range, and is suitable for conducted noise circuit simulation.

[0003] The impedance behavior model is usually composed of an RLC mixed network. After determining the model topology, accurate model parameters cannot be obtained, so that the impedance characteristic curve of the simulated circuit model and the actual circuit is quite different, and thus the design of electromagnetic interference suppression cannot be effectively performed. At present, the parameter extraction method based on vector fitting needs to solve the least square of the overdetermined equation set, and calculate the conjugate residual of the auxiliary function. This process is prone to non-convergence. In addition, in the parameter extraction based on vector fitting, the result may be negative, which may cause non-convergence and other problems in circuit simulation calculation, and still has great limitations in engineering practice. SUMMARY

[0004] Therefore, the purpose of the embodiments of the present application is to provide a passive device impedance fitting method and device, electronic equipment and storage medium, which can improve the problem that the equivalent impedance model of the passive device is quite different from the impedance characteristic curve of the actual circuit, and cannot effectively suppress electromagnetic interference.

[0005] To achieve the above technical purposes, the technical solutions adopted by the present application are as follows:

[0006] In a first aspect, the embodiments of the present application provide a passive device impedance fitting method, which comprises:

[0007] Obtaining an impedance characteristic curve of a passive device obtained by actual measurement;

[0008] Dividing the impedance characteristic curve to obtain M resonant wave bands arranged in sequence, wherein each resonant wave band of the M resonant wave bands has one normal resonant peak or one wide resonant peak, and M is an integer greater than or equal to 1;

[0009] determining an impedance model corresponding to each of the M resonance bands and initial parameters of the impedance model, wherein the impedance model corresponding to the regular resonance peak is a set of RLC parallel circuits, and the impedance model corresponding to the wide resonance peak is p sets of RLC parallel circuits connected in series, p being an integer greater than or equal to 2;

[0010] For the impedance model corresponding to the i-th resonance band, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, an iteration optimization is performed through a preset optimization algorithm, and a set of parameters with the minimum error to the actual parameters and the error being less than a set value is taken as the model parameters of the i-th impedance model, wherein the actual parameters are RLC parameters of the i-th resonance band, and i is taken in turn as 1 to M;

[0011] Based on the model parameters of the impedance models corresponding to the M resonance bands respectively, an equivalent impedance model for replacing the passive device is obtained.

[0012] In combination with the first aspect, in some optional embodiments, the method further includes:

[0013] Based on the RLC parameters of the equivalent impedance model, a suppression parameter for suppressing electromagnetic interference of the equivalent impedance model is determined, the suppression parameter including a resistance parameter, a capacitance parameter and an inductance parameter;

[0014] The electromagnetic interference of the equivalent impedance model is suppressed through a suppression circuit formed by the suppression parameter.

[0015] In combination with the first aspect, in some optional embodiments, determining the impedance model corresponding to each of the M resonance bands and the initial parameters of the impedance model includes:

[0016] When any one of the resonance bands is a band of a regular resonance peak, the impedance model of the any one of the resonance bands is determined as a set of RLC parallel circuits;

[0017] According to the peak value, the frequency point and a preset algorithm of the any one of the resonance bands, the resistance value, the capacitance value and the inductance value of the RLC parallel circuit of the any one of the resonance bands are determined as the initial parameters.

[0018] In combination with the first aspect, in some optional embodiments, determining the impedance model corresponding to each of the M resonance bands and the initial parameters of the impedance model includes:

[0019] When any one of the resonance bands is a band of a wide resonance peak, the impedance model of the any one of the resonance bands is determined as p sets of RLC parallel circuits;

[0020] Determine resistance value, capacitance value and inductance value of the p groups of RLC parallel circuits in the any one resonant wave band as equivalent parameters according to the peak value, frequency point and preset algorithm of the any one resonant wave band, split the equivalent parameters into p groups of parameters as initial parameters corresponding to each group of RLC parallel circuits, wherein the sum of the same type of parameters in the p groups of initial parameters is equal to the same type of parameters in the equivalent parameters.

[0021] In combination with the first aspect, in some optional embodiments, based on the initial parameters of the i th impedance model and the corresponding optimization floating range, iterative optimization is performed through a preset optimization algorithm, and a group of parameters with minimum error to actual parameters and less than a set value is taken as the model parameters of the i th impedance model, including:

[0022] When the i th resonant wave band is a wave band of one regular resonant peak, based on the initial parameters of the i th impedance model and the corresponding optimization floating range, multiple groups of parameters are selected from the optimization floating range at equal intervals through the optimization algorithm;

[0023] Optimize and fit each group of RLC parameters in the multiple groups of parameters to obtain a group of RLC parameters with minimum error to the i th resonant wave band, wherein the group of RLC parameters with minimum error is taken as the initial parameters for next optimization;

[0024] Repeat the step of selecting multiple groups of RLC parameters from the optimization floating range at equal intervals through the optimization algorithm based on the group of RLC parameters with minimum error obtained by each optimization and the corresponding optimization floating range, until a group of RLC parameters with minimum error is obtained through optimization and fitting of each group of RLC parameters in the multiple groups of RLC parameters to a new fitted curve and the i th resonant wave band, and the error of the group of RLC parameters with minimum error is less than a set value, stop iteration, and determine the group of RLC parameters with minimum error as the model parameters.

[0025] In combination with the first aspect, in some optional embodiments, the method further includes:

[0026] When the i th resonant wave band is the last wave band and a wave band of one regular resonant peak, perform joint optimization on the initial parameters of the last two RLC parallel circuits corresponding to the M resonant wave bands and the corresponding optimization floating range through the optimization algorithm, and take the corresponding RLC parameters with minimum error to actual parameters and less than a set value obtained through optimization as the model parameters of the last two RLC parallel circuits.

[0027] In combination with the first aspect, in some optional embodiments, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, the preset optimization algorithm is used for iterative optimization, and a set of parameters with minimum error to the actual parameters and less than a set value is taken as the model parameters of the i-th impedance model, including:

[0028] When the i-th resonant waveband is a waveband of a wide resonant peak, based on the initial parameters of each set of RLC parallel circuits of the i-th impedance model and the corresponding optimization floating range, a plurality of sets of parameters are selected from the optimization floating range at equal intervals by using the preset optimization algorithm;

[0029] Each set of RLC parameters in the plurality of sets of parameters is optimized and fitted to obtain RLC parameters of each set of RLC parallel circuits with minimum error between the fitted curve and the i-th resonant waveband, wherein the RLC parameters of each set of RLC parallel circuits with minimum error are taken as the initial parameters for the next optimization;

[0030] The step of selecting a plurality of sets of RLC parameters from the optimization floating range at equal intervals by using the optimization algorithm based on the RLC parameters of each set of RLC parallel circuits with minimum error obtained by each optimization and the corresponding optimization floating range is repeatedly performed until the RLC parameters of each set of RLC parallel circuits with minimum error are less than a set value, the iteration is stopped, and the RLC parameters of each set of RLC parallel circuits with minimum error are determined as the model parameters.

[0031] In combination with the first aspect, in some optional embodiments, the passive device includes at least one of a motor, a resistor, a capacitor, an inductor, and a converter.

[0032] In combination with the first aspect, in some optional embodiments, the set of RLC parallel circuits includes a resistor, a capacitor, and an inductor, and the resistor, the capacitor, and the inductor are connected in parallel.

[0033] In the second aspect, the embodiments of the present application further provide a passive device impedance fitting device, and the device includes:

[0034] An acquisition unit is configured to acquire an impedance characteristic curve obtained by measuring a passive device;

[0035] A division unit is configured to divide the impedance characteristic curve to obtain M resonant wavebands arranged in sequence, wherein each resonant waveband in the M resonant wavebands has a normal resonant peak or a wide resonant peak, and M is an integer greater than or equal to 1.

[0036] determining unit configured to determine an impedance model corresponding to each of the M resonance bands and initial parameters of the impedance model, wherein the impedance model corresponding to the regular resonance peak is a group of RLC parallel circuits, and the impedance model corresponding to the wide resonance peak is p groups of RLC parallel circuits connected in series, p being an integer greater than or equal to 2;

[0037] an optimization unit configured to, for the impedance model corresponding to the i-th resonance band, perform iterative optimization based on the initial parameters of the i-th impedance model and the corresponding optimization floating range by using a preset optimization algorithm, and take a group of parameters with the minimum error to the actual parameters and the error being less than a set value as the model parameters of the i-th impedance model, wherein the actual parameters are RLC parameters of the i-th resonance band, and i is sequentially 1 to M;

[0038] a simulation unit configured to obtain an equivalent impedance model for replacing the passive device based on the model parameters of the impedance models corresponding to the M resonance bands respectively.

[0039] In a third aspect, an embodiment of the present application further provides an electronic device, which includes a processor and a memory coupled to each other, and the memory stores a computer program. When the computer program is executed by the processor, the electronic device performs the method described above.

[0040] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a computer program. When the computer program is run on a computer, the computer performs the method described above.

[0041] The application with the technical solution has the following advantages:

[0042] In the technical solution provided in the present application, an impedance model is determined for each resonance band of the impedance characteristic curve of the passive device. The impedance model corresponding to the regular resonance peak is a group of RLC parallel circuits, and the impedance model corresponding to the wide resonance peak is at least two groups of RLC parallel circuits connected in series. Then, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, iterative optimization is performed by using a preset optimization algorithm, and a group of parameters with the minimum error to the actual parameters and the error being less than a set value are taken as the model parameters of the i-th impedance model, wherein the actual parameters are RLC parameters of the i-th resonance band. Based on the model parameters of the impedance models corresponding to the M resonance bands respectively, an equivalent impedance model for replacing the passive device is obtained. In this way, the error between the impedance characteristic curve of the equivalent impedance model and the impedance characteristic curve of the actual circuit of the passive device is small, and the impedance of the passive device can be accurately expressed, thereby facilitating the electromagnetic interference suppression design based on the equivalent impedance model. BRIEF DESCRIPTION OF DRAWINGS

[0043] The application can be further illustrated by the non-limiting embodiments shown in the accompanying drawings. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0044] Figure 1 The circuit principle schematic diagram of the equivalent impedance model provided for the embodiments of the application.

[0045] Figure 2 The flowchart of the passive device impedance fitting method provided for the embodiments of the application.

[0046] Figure 3 One of the schematic diagrams of the fitting optimization results provided for the embodiments of the application.

[0047] Figure 4 The second schematic diagram of the fitting optimization results provided for the embodiments of the application.

[0048] Figure 5 The third schematic diagram of the fitting optimization results provided for the embodiments of the application.

[0049] Figure 6 The block diagram of the passive device impedance fitting device provided for the embodiments of the application.

[0050] Icon: 200-passive device impedance fitting device; 210-acquisition unit; 220-division unit; 230-determination unit; 240-optimization unit; 250-simulation unit. DETAILED DESCRIPTION

[0051] The application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that similar or identical parts are denoted by the same reference numerals in the drawings or description, and the implementation not shown or described in the drawings is known to those skilled in the art. In the description of the application, the terms "first", "second", etc. are only used for differentiation and cannot be understood as indicating or implying relative importance.

[0052] The electronic device provided by the embodiments of the application can include a processing module and a storage module. The storage module stores a computer program, and when the computer program is executed by the processing module, the electronic device can execute the corresponding steps in the passive device impedance fitting method described below. The electronic device can be, but is not limited to, a personal computer, a server, etc.

[0053] In the embodiment, the electronic device can calculate the equivalent impedance model for replacing the passive device and the RLC parameters corresponding to the equivalent impedance model based on the impedance characteristic curve of the passive device.

[0054] Please refer to Figure 1 The circuit of the equivalent impedance model of the passive device can be as shown in Figure 1 The equivalent impedance model can include n RLC parallel circuits in series, and the number of n can be determined according to actual conditions. Each group of RLC parallel circuits can be regarded as an RLC unit, and an RLC unit can include a resistor, an inductor and a capacitor, wherein the resistor, the inductor and the capacitor are in parallel. For example, in Figure 1 , the resistor R1, the inductor L1 and the capacitor C1 form an RLC unit in parallel.

[0055] In the embodiment, the passive device can include, but is not limited to, at least one of a motor, a resistor, a capacitor, an inductor and a converter.

[0056] Please refer to Figure 2 The application also provides a passive device impedance fitting method, which can be applied to the electronic device described above, and each step of the method is executed or implemented by the electronic device. The passive device impedance fitting method can include the following steps:

[0057] Step 110, obtaining an impedance characteristic curve obtained by actually measuring a passive device;

[0058] Step 120, dividing the impedance characteristic curve to obtain M resonant wave bands arranged in sequence, wherein each resonant wave band in the M resonant wave bands has a normal resonant peak or a wide resonant peak, and M is an integer greater than or equal to 1;

[0059] Step 130, determining an impedance model corresponding to each resonant wave band in the M resonant wave bands and initial parameters of the impedance model, wherein the impedance model corresponding to the normal resonant peak is a group of RLC parallel circuits, and the impedance model corresponding to the wide resonant peak is p groups of RLC parallel circuits in series, and p is an integer greater than or equal to 2;

[0060] Step 140, for the impedance model corresponding to the i-th resonant wave band, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, the preset optimization algorithm is iteratively optimized, and a group of parameters with the minimum error and the error less than a set value between the optimized and the actual parameters are taken as the model parameters of the i-th impedance model, wherein the actual parameters are the RLC parameters of the i-th resonant wave band, and i is sequentially taken as 1 to M;

[0061] At step 150, an equivalent impedance model for replacing the passive device is obtained based on the model parameters of the impedance model corresponding to each of the M resonance bands.

[0062] The steps of the passive device impedance fitting method will be described in detail as follows:

[0063] At step 110, the manner in which the electronic device obtains the impedance characteristic curve of the passive device can be flexibly determined according to actual conditions. For example, the impedance characteristic curve is pre-recorded in a storage module of the electronic device, or the electronic device can obtain the impedance characteristic curve of the passive device actually measured by a detection device from the detection device. The detection device is a conventional device for detecting the impedance characteristic curve, and the detection manner is a conventional manner.

[0064] Understandably, the impedance characteristic curve of the passive device usually has multiple resonance bands, and each resonance band can be understood as a band with a resonance peak. For example, in the impedance characteristic curve shown in FIG. 1, the impedance characteristic curve actually measured by the passive device corresponds to the dashed line, and has two resonance bands. Figure 4 Figure 4 In the impedance characteristic curve shown in FIG. 1, the impedance characteristic curve actually measured by the passive device corresponds to the dashed line, and has two resonance bands.

[0065] At step 120, the electronic device can take the valley points of the valleys in the impedance characteristic curve as the division points, and then divide the impedance characteristic curve by using the valley points to obtain M resonance bands arranged in sequence. The number of M can be flexibly determined according to actual conditions.

[0066] At step 130, for each resonance band divided, the electronic device can determine the impedance model corresponding to each resonance band and the initial parameters of the impedance model. The impedance model corresponding to the i th resonance band (or the i th resonance peak) is the i th impedance model. If the resonance band is a band of a conventional resonance peak, the impedance model of the resonance band is a set of RLC parallel circuits. If the resonance band is a band of a wide resonance peak, the impedance model of the resonance band is p sets of RLC parallel circuits connected in series, and p is an integer greater than or equal to 2 and can be flexibly determined according to actual conditions.

[0067] In the impedance characteristic curve, the manner of detecting the conventional resonance peak and the wide resonance peak is a conventional manner. For example, in the two resonance bands shown in FIG. 1, the peak of the wider band (i.e., the flatter peak) is the wide resonance peak, and the peak of the narrower band (i.e., the sharper peak) is the conventional resonance peak. Figure 4

[0068] The initial parameters of the impedance model of each resonance band can be determined by the peak value of the resonance band.

[0069] As an optional implementation, step 130 can include: ​​

[0070] When any resonant band is a band with a conventional resonant peak, the impedance model of the resonant band is determined to be a set of RLC parallel circuits.

[0071] Based on the peak value, frequency point, and preset algorithm of any resonant band, determine the resistance, capacitance, and inductance values ​​of the RLC parallel circuit of any resonant band as initial parameters.

[0072] When determining the initial parameters of the impedance model, the preset algorithm can be flexibly determined according to the actual situation, and can be the corresponding formulas as follows. For example, if the impedance model is a set of RLC parallel circuits, the theoretical expression for the port impedance in the equivalent circuit model is:

[0073]

[0074] In formula (1), Z refers to the impedance of the equivalent circuit model; N refers to the total number of RLC parallel circuits; R i The resistance value of the i-th group of RLC parallel circuits; L i C refers to the inductance value of the i-th group of RLC parallel circuits; i This refers to the capacitance value of the i-th group of RLC parallel circuits.

[0075] Let f be the frequency of the i-th resonant peak in the impedance characteristic curve. pi The peak impedance of this resonance peak is denoted as R. i Initial value, the i-th resonance valley point is denoted as f bi Then, select multiple frequency points in the low-frequency linear segment of the impedance characteristic curve, and calculate L according to the following formula. i The formula is as follows:

[0076] L i =|Z| / (2πf pi (2)

[0077] Take the average value calculated from multiple frequency points as L i Initial value, then calculate C according to the following formula. i initial value and L i+1 .

[0078] C i =1 / (2πf) pi ) 2 L i (3)

[0079] L i+1 =1 / (2πf) bi ) 2 C i (4)

[0080] Based on the above formula, the initial parameters of the impedance model corresponding to the i-th resonant band can be determined. That is, the initial parameters of this set of RLC parameters include R... i initial value, L i Initial value and C i Initial value.

[0081] As an optional implementation, step 130 may include:

[0082] When any resonant band is a band with a wide resonant peak, the impedance model of the resonant band is determined to be a p-group RLC parallel circuit.

[0083] Based on the peak value, frequency point, and preset algorithm of any resonant band, the resistance, capacitance, and inductance values ​​of p groups of RLC parallel circuits in any resonant band are determined as equivalent parameters. The equivalent parameters are then divided into p groups of parameters as initial parameters corresponding to each group of RLC parallel circuits. The sum of similar parameters in the p groups of initial parameters is equal to the similar parameters in the equivalent parameters.

[0084] If the resonant band is a band with a wide resonant peak, the impedance model can be represented by two sets of RLC parallel circuits. The calculation process of the initial parameters is illustrated below: First, using the same calculation method as the initial parameters of the impedance model for the conventional resonant peak, a set of RLC circuits is calculated. i initial value, L i Initial value and C i Initial value, as equivalent parameter [C] i R i L i At this time, R i The initial value is the peak value of the broad resonance peak. L i Initial value and C i The initial values ​​can be calculated using formulas (2) and (3) above, respectively. Then, the calculated equivalent parameter [C] is used... i R i L i The parameters are divided into two sets to serve as the initial parameters for each RLC parallel circuit, denoted as [C]. i1 R i1 L i1 ] and [C i2 R i2 L i2 Let f be the resonant point with a broad resonance peak. wpi The corresponding resonance valley point is f bi The split parameters should satisfy L. i1 +L i2 =L i R i1 +R i2 =Ri And L i1 >L i2 R i1 >R i2 .

[0085] Since the initial parameter values ​​are only used as preliminary reference values ​​for optimization, and the final parameter extraction will be achieved through iterative optimization, the initial values ​​do not need to meet very high precision. After obtaining the initial parameters, the electronic device can perform iterative optimization for the initial parameters of each group of RLC parallel circuits through step 140.

[0086] As an optional implementation, step 140 may include:

[0087] When the i-th resonant band is a band with a conventional resonant peak, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, multiple sets of parameters are selected at equal intervals from the optimization floating range through the optimization algorithm.

[0088] For each of the multiple sets of parameters, perform optimization fitting to obtain the set of RLC parameters with the smallest error between the fitted curve and the i-th resonant band. The set of RLC parameters with the smallest error is used as the initial parameters for the next optimization.

[0089] The repeated execution step is based on the set of RLC parameters with the smallest error obtained in each optimization and the corresponding optimization floating range. Multiple sets of RLC parameters are selected at equal intervals from the optimization floating range by the optimization algorithm. The next step is to optimize and fit each set of RLC parameters in the multiple sets of RLC parameters to obtain the set of RLC parameters with the smallest error between the newly fitted curve and the i-th resonant band. The iteration stops when the error of the set of RLC parameters with the smallest error is less than a set value, and the set of RLC parameters with the smallest error is determined as the model parameters.

[0090] In this embodiment, the optimization floating range is the fluctuation range above and below the initial parameter, which can be flexibly determined according to the actual situation. For example, the optimization floating range can be ±60%, ±80%, or other ranges of the initial parameter.

[0091] For a conventional resonance peak, if the initial parameters are [C] i R i L i If the variables involved in the optimization are [Ci Li], then C i The optimization range can be (1-80%)·C i Up to (1+80%)·C i L i The optimization range can be (1-80%)·L i Up to (1+80%)·L iThe optimization floating range for each type of parameter can be determined by selecting multiple sets of parameters at equal intervals within the optimization floating range to obtain multiple sets of RLC parameters. For example, the number of selected RLC parameters can be 20 sets. Then, for each set of RLC parameters, impedance characteristic curves are fitted using circuit simulation tools to obtain the impedance characteristic curve corresponding to each set of RLC parameters. Next, the fitted impedance characteristic curves are compared with the measured curves for the resonant band to obtain the error of each set of RLC parameters; and the set of RLC parameters with the smallest error is selected. If the error of the set of RLC parameters with the smallest error is less than a set value, then this set of RLC parameters is the model parameter of the i-th impedance model. The set value can be flexibly determined according to the actual situation.

[0092] If the error of the RLC parameter set with the smallest error exceeds a set value, then this set of RLC parameters with the smallest error is used as the initial parameters for the next optimization. Then, based on the new initial parameters and the corresponding optimization float range (the optimization float range of the new initial parameters can be smaller than the previous optimization float range), 20 sets of RLC parameters are selected at equal intervals. For each set of RLC parameters, impedance characteristic curve fitting is performed using circuit simulation tools. Iteration stops when the error of the RLC parameter set with the smallest error is less than the set value, or when the specified number of iterations is reached. This specified number can be flexibly determined according to the actual situation. For example, the specified number of iterations can be 3, 5, etc.

[0093] As an optional implementation, step 140 may include:

[0094] When the i-th resonant band is a band with a wide resonant peak, based on the initial parameters of each group of RLC parallel circuits and the corresponding optimization floating range of the i-th impedance model, multiple groups of parameters are selected at equal intervals from the optimization floating range through a preset optimization algorithm.

[0095] For each of the multiple sets of parameters, perform optimization fitting to obtain the RLC parameters of each RLC parallel circuit with the smallest error between the fitted curve and the i-th resonant band. The RLC parameters of each RLC parallel circuit with the smallest error are used as the initial parameters for the next optimization.

[0096] The repeated execution steps are based on the RLC parameters of each RLC parallel circuit with the smallest error obtained in each optimization and the corresponding optimization floating range. Multiple sets of RLC parameters are selected at equal intervals from the optimization floating range using the optimization algorithm. The next step is to perform optimization fitting on each of the multiple sets of RLC parameters to obtain the RLC parameters of each RLC parallel circuit with the smallest error in the newly fitted curve and the i-th resonant band. The iteration stops when the error of the RLC parameters of each RLC parallel circuit with the smallest error is less than a set value, and the RLC parameters of each RLC parallel circuit with the smallest error are determined as the model parameters.

[0097] For a wide resonance peak, assuming the impedance model corresponding to the wide resonance peak (denoted as the i-th impedance model) is two sets of RLC parallel circuits, if the initial equivalent parameters are [C i R i L i The variables involved in the optimization are [C]. i1 R i1 L i1 C i2 R i2 L i2 Then, based on the optimization float range of each group of RLC parallel circuits, 20 different [C] groups are selected. i1 R i1 L i1 C i2 R i2 L i2 The parameters are jointly optimized. Specifically, for each of the 20 sets of RLC parameters, an impedance characteristic curve is fitted using circuit simulation tools, resulting in the corresponding impedance characteristic curve for each set of RLC parameters. Then, the fitted impedance characteristic curve is compared with the measured curve for the resonant band to determine the error of each set of RLC parameters; the set of RLC parameters with the smallest error is selected. If the error of the set of RLC parameters with the smallest error is less than a set value, then this set of RLC parameters becomes the model parameters for the i-th impedance model. The set value can be flexibly determined according to actual conditions.

[0098] If the error of the RLC parameter set with the smallest error exceeds the set value, then the RLC parameter set with the smallest error is used as the initial parameter for the next optimization, and the iteration optimization continues until the error of the RLC parameter of each RLC parallel circuit with the smallest error is less than the set value, or the number of iterations reaches the specified number, then the iteration stops.

[0099] In step 150, the model parameters of the impedance models for each of the M resonant bands are substituted into their respective impedance models to obtain the equivalent impedance model of the passive device. At this point, the impedance characteristic curve obtained from the simulation based on the equivalent impedance model has a small error compared to the measured impedance characteristic curve of the passive device. (See also...) Figure 3 , Figure 4 and Figure 5 , Figure 3 This is a schematic diagram showing the fitted curve and the measured curve of the wide resonance peak; Figure 4 This is a schematic diagram of the fitted curve and the measured curve when joint optimization is not used; Figure 5 This is a schematic diagram of the fitted curve and the measured curve when using joint optimization.

[0100] In this embodiment, the method may further include:

[0101] Based on the RLC parameters of the equivalent impedance model, suppression parameters for suppressing electromagnetic interference of the equivalent impedance model are determined, including resistance parameters, capacitance parameters and inductance parameters.

[0102] Electromagnetic interference of the equivalent impedance model is suppressed by a suppression circuit formed with the suppression parameters.

[0103] Understandably, after obtaining the equivalent impedance model of the passive device, simulation tests can be performed based on the equivalent impedance model to calculate the suppression parameters used to suppress electromagnetic interference of the equivalent impedance model. The calculation method is conventional and will not be elaborated here.

[0104] After obtaining the suppression parameters, a corresponding suppression circuit can be built based on the suppression parameters. Then, the suppression circuit can be used to suppress the electromagnetic interference of the equivalent impedance model, which is beneficial to achieving electromagnetic compatibility of the circuit.

[0105] Based on the above design, automated optimization calculation of the equivalent circuit model parameters of passive devices can be achieved, with simulation and measured impedance accuracy exceeding 90%. Furthermore, automated iterative optimization replaces traditional manual optimization, saving time spent on manual adjustments. The method provided in this application can predict the noise coupling path effect using the equivalent impedance model, thus avoiding problems such as high prototype rectification costs and long rectification cycles. It achieves accurate expression of the impedance of passive devices, reduces reliance on engineers' experience, lowers labor costs, and provides reliable electromagnetic interference suppression measures for different application environments, thereby avoiding systematic over-design of filtering for electrical components.

[0106] In this embodiment, the method may further include: when the i-th resonant band is the last band and is a band with a conventional resonant peak, the initial parameters and corresponding optimization floating range of the last two RLC parallel circuits corresponding to the M resonant bands are jointly optimized by the optimization algorithm, and the corresponding RLC parameters that have the smallest error with the actual parameters and whose error is less than the set value are used as the model parameters of the last two RLC parallel circuits.

[0107] Due to the cumulative nature of optimization errors, the optimization results of the last two sets of RLC parameters in the M impedance models are not accurate enough, resulting in some errors in the high-frequency impedance. In this case, other RLC parameters (excluding the last two sets of RLC parameters) can be fixed, and joint optimization of the last two sets of RLC parameters can be performed. The variables include [Ci-1 Ri-1 Li-1C]. i R i L i Since there are many variables in the joint optimization, the sampling density can be reduced. That is, the number of RLC parameters selected within the optimization fluctuation range can be set to 5 to 20 sets, with a fluctuation range of ±40%. To ensure parameter accuracy, the result of each joint optimization can be reset as the initial parameter for the next optimization, and then multiple iterations of optimization can be performed until the change in error between the last two iterations is less than the set value, thus completing the optimization process. The implementation process of joint optimization is similar to that when the impedance model corresponding to the wide resonant peak is a parallel circuit of two RLC circuits, and will not be elaborated here.

[0108] Please refer to Figure 6 This application also provides a passive device impedance fitting device 200, which includes at least one software function module that can be stored in a storage module or embedded in an operating system (OS) in the form of software or firmware. A processing module is used to execute executable modules stored in the storage module, such as the software function modules and computer programs included in the passive device impedance fitting device 200.

[0109] The passive device impedance fitting device 200 includes an acquisition unit 210, a division unit 220, a determination unit 230, an optimization unit 240, and a simulation unit 250. The functions of each unit are as follows:

[0110] Unit 210 acquires the impedance characteristic curve obtained by measuring passive devices.

[0111] The dividing unit 220 is used to divide the impedance characteristic curve to obtain M resonant bands arranged in sequence, wherein each of the M resonant bands has a conventional resonant peak or a wide resonant peak, and M is an integer greater than or equal to 1.

[0112] The determining unit 230 is used to determine the impedance model and the initial parameters of the impedance model corresponding to each of the M resonant bands. The impedance model corresponding to the conventional resonant peak is a set of RLC parallel circuits, and the impedance model corresponding to the wide resonant peak is p sets of RLC parallel circuits connected in series, where p is an integer greater than or equal to 2.

[0113] The optimization unit 240 is used to perform iterative optimization for the impedance model corresponding to the i-th resonant band, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, through a preset optimization algorithm, and to take the set of parameters with the smallest error from the actual parameters and the error less than a set value as the model parameters of the i-th impedance model, wherein the actual parameters are the RLC parameters of the i-th resonant band, and i takes values ​​from 1 to M in sequence;

[0114] Simulation unit 250 is used to obtain an equivalent impedance model for replacing the passive device based on the model parameters of the impedance models corresponding to the M resonant bands respectively.

[0115] Optionally, the passive device impedance fitting device 200 may further include a suppression parameter determination unit 230 and a suppression unit. The suppression parameter determination unit 230 is used to determine suppression parameters for suppressing electromagnetic interference of the equivalent impedance model based on the RLC parameters of the equivalent impedance model. The suppression parameters include resistance parameters, capacitance parameters, and inductance parameters.

[0116] The suppression unit is used to suppress electromagnetic interference of the equivalent impedance model by means of a suppression circuit formed with the suppression parameters.

[0117] Optionally, the determining unit 230 can be used for:

[0118] When any resonant band is a band with a conventional resonant peak, the impedance model of the resonant band is determined to be a set of RLC parallel circuits.

[0119] Based on the peak value, frequency point, and preset algorithm of any resonant band, determine the resistance, capacitance, and inductance values ​​of the RLC parallel circuit of any resonant band as initial parameters.

[0120] Optionally, the determining unit 230 can also be used for:

[0121] When any resonant band is a band with a wide resonant peak, the impedance model of the resonant band is determined to be a p-group RLC parallel circuit.

[0122] Based on the peak value, frequency point, and preset algorithm of any resonant band, the resistance, capacitance, and inductance values ​​of p groups of RLC parallel circuits in any resonant band are determined as equivalent parameters. The equivalent parameters are then divided into p groups of parameters as initial parameters corresponding to each group of RLC parallel circuits. The sum of similar parameters in the p groups of initial parameters is equal to the similar parameters in the equivalent parameters.

[0123] Optionally, the optimization unit 240 is used for:

[0124] When the i-th resonant band is a band with a conventional resonant peak, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, multiple sets of parameters are selected at equal intervals from the optimization floating range through the optimization algorithm.

[0125] For each of the multiple sets of parameters, perform optimization fitting to obtain the set of RLC parameters with the smallest error between the fitted curve and the i-th resonant band. The set of RLC parameters with the smallest error is used as the initial parameters for the next optimization.

[0126] The repeated execution step is based on the set of RLC parameters with the smallest error obtained in each optimization and the corresponding optimization floating range. Multiple sets of RLC parameters are selected at equal intervals from the optimization floating range by the optimization algorithm. The next step is to optimize and fit each set of RLC parameters in the multiple sets of RLC parameters to obtain the set of RLC parameters with the smallest error between the newly fitted curve and the i-th resonant band. The iteration stops when the error of the set of RLC parameters with the smallest error is less than a set value, and the set of RLC parameters with the smallest error is determined as the model parameters.

[0127] Optionally, the optimization unit 240 can also be used to: when the i-th resonant band is the last band and is a band with a conventional resonant peak, perform joint optimization on the initial parameters and corresponding optimization floating range of the last two RLC parallel circuits corresponding to the M resonant bands through the optimization algorithm, and use the corresponding RLC parameter with the smallest error from the actual parameters and the error less than the set value as the model parameter of the last two RLC parallel circuits.

[0128] Alternatively, the optimization unit 240 can also be used for:

[0129] When the i-th resonant band is a band with a wide resonant peak, based on the initial parameters of each group of RLC parallel circuits and the corresponding optimization floating range of the i-th impedance model, multiple groups of parameters are selected at equal intervals from the optimization floating range through a preset optimization algorithm.

[0130] For each of the multiple sets of parameters, perform optimization fitting to obtain the RLC parameters of each RLC parallel circuit with the smallest error between the fitted curve and the i-th resonant band. The RLC parameters of each RLC parallel circuit with the smallest error are used as the initial parameters for the next optimization.

[0131] The repeated execution steps are based on the RLC parameters of each RLC parallel circuit with the smallest error obtained in each optimization and the corresponding optimization floating range. Multiple sets of RLC parameters are selected at equal intervals from the optimization floating range using the optimization algorithm. The next step is to perform optimization fitting on each of the multiple sets of RLC parameters to obtain the RLC parameters of each RLC parallel circuit with the smallest error in the newly fitted curve and the i-th resonant band. The iteration stops when the error of the RLC parameters of each RLC parallel circuit with the smallest error is less than a set value, and the RLC parameters of each RLC parallel circuit with the smallest error are determined as the model parameters.

[0132] In this embodiment, the processing module can be an integrated circuit chip with signal processing capabilities. The processing module can be a general-purpose processor. For example, the processor can be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0133] The storage module can be, but is not limited to, random access memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, etc. In this embodiment, the storage module can be used to store the impedance characteristic curves, optimization algorithms, and optimization floating ranges of passive devices. Of course, the storage module can also be used to store programs, which the processing module executes after receiving an execution instruction.

[0134] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the electronic device described above can be referred to the corresponding steps in the aforementioned method, and will not be elaborated further here.

[0135] This application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the passive device impedance fitting method as described in the above embodiments.

[0136] Based on the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, electronic device, or network device, etc.) to execute the methods described in the various implementation scenarios of this application.

[0137] In summary, this application provides a passive device impedance fitting method, apparatus, electronic device, and storage medium. In this solution, an impedance model is determined for each resonant band of the impedance characteristic curve of the passive device. Specifically, the impedance model corresponding to a conventional resonant peak is a set of parallel RLC circuits, while the impedance model corresponding to a wide resonant peak is at least two sets of series-connected parallel RLC circuits. Then, based on the initial parameters of the i-th impedance model and its corresponding optimization range, iterative optimization is performed using a preset optimization algorithm. The set of parameters with the smallest error compared to the actual parameters, and whose error is less than a set value, is used as the model parameters of the i-th impedance model, where the actual parameters are the RLC parameters of the i-th resonant band. Based on the model parameters of the impedance models corresponding to M resonant bands, an equivalent impedance model is obtained to replace the passive device. Thus, the impedance characteristic curve of the equivalent impedance model has a small error compared to the impedance characteristic curve of the actual passive device circuit, enabling accurate expression of the passive device impedance, which is beneficial for electromagnetic interference suppression design based on the equivalent impedance model.

[0138] In the embodiments provided in this application, it should be understood that the disclosed apparatus, systems, and methods can also be implemented in other ways. The apparatus, systems, and methods embodiments described above are merely illustrative. For example, 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, which includes one or more executable instructions for implementing a specified logical function. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0139] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A passive device impedance fitting method, characterized in that, The method comprises: obtaining an impedance characteristic curve obtained by actually measuring a passive device; dividing the impedance characteristic curve to obtain M resonant wave bands arranged in sequence, wherein each of the M resonant wave bands has a normal resonant peak or a wide resonant peak, and M is an integer greater than or equal to 1; determining an impedance model corresponding to each of the M resonant wave bands and initial parameters of the impedance model, wherein the impedance model corresponding to the normal resonant peak is a group of RLC parallel circuits, and the impedance model corresponding to the wide resonant peak is p groups of RLC parallel circuits connected in series, and p is an integer greater than or equal to 2; for the impedance model corresponding to the i-th resonant wave band, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, performing iterative optimization through a preset optimization algorithm, and taking a group of parameters with the minimum error to the actual parameters and the error less than a set value as the model parameters of the i-th impedance model, wherein the actual parameters are RLC parameters of the i-th resonant wave band, and i is sequentially taken as 1 to M; based on the model parameters of the impedance models corresponding to the M resonant wave bands respectively, obtaining an equivalent impedance model for replacing the passive device; wherein, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, performing iterative optimization through a preset optimization algorithm, and taking a group of parameters with the minimum error to the actual parameters and the error less than a set value as the model parameters of the i-th impedance model, comprises: when the i-th resonant wave band is a wave band of a normal resonant peak, based on the initial parameters of the i-th impedance model and the corresponding optimization floating range, selecting a plurality of groups of parameters from the optimization floating range at equal intervals through the optimization algorithm; performing optimization fitting on each group of RLC parameters in the plurality of groups of parameters to obtain a group of RLC parameters with the minimum error to a new fitted curve and the i-th resonant wave band, wherein the group of RLC parameters with the minimum error is taken as the initial parameters for next optimization; repeating the step of selecting a plurality of groups of RLC parameters from the optimization floating range at equal intervals through the optimization algorithm based on the group of RLC parameters with the minimum error obtained by each optimization and the corresponding optimization floating range, until the group of RLC parameters with the minimum error is less than the set value, stopping iteration, and determining the group of RLC parameters with the minimum error as the model parameters; when the i-th resonant wave band is a wave band of a wide resonant peak, based on the initial parameters of each group of RLC parallel circuits of the i-th impedance model and the corresponding optimization floating range, selecting a plurality of groups of parameters from the optimization floating range at equal intervals through a preset optimization algorithm; performing optimization fitting on each set of RLC parameters in the plurality of sets of RLC parameters to obtain RLC parameters of each set of RLC parallel circuits with the minimum error between the fitted curve and the i-th resonant band, wherein the RLC parameters of each set of RLC parallel circuits with the minimum error are taken as initial parameters for the next optimization; repeating the step of performing optimization fitting on each set of RLC parameters in the plurality of sets of RLC parameters to obtain RLC parameters of each set of RLC parallel circuits with the minimum error between the fitted curve and the i-th resonant band, wherein the RLC parameters of each set of RLC parallel circuits with the minimum error are taken as initial parameters for the next optimization, and the optimization algorithm is used to select a plurality of sets of RLC parameters from the optimization floating range at intervals, until the RLC parameters of each set of RLC parallel circuits with the minimum error are less than a set value, the iteration is stopped, and the RLC parameters of each set of RLC parallel circuits with the minimum error are determined as the model parameters.

2. The method of claim 1, wherein, The method further comprises: determining suppression parameters for suppressing electromagnetic interference of the equivalent impedance model based on the RLC parameters of the equivalent impedance model, wherein the suppression parameters include resistance parameters, capacitance parameters, and inductance parameters; suppressing electromagnetic interference of the equivalent impedance model through a suppression circuit formed by the suppression parameters.

3. The method of claim 1, wherein, determining an impedance model corresponding to each resonant band in the M resonant bands and initial parameters of the impedance model, comprising: when any resonant band is a band of a regular resonant peak, determining that the impedance model of the any resonant band is a set of RLC parallel circuits; determining resistance values, capacitance values, and inductance values of the RLC parallel circuits of the any resonant band as initial parameters according to a peak value, a frequency point, and a preset algorithm of the any resonant band.

4. The method of claim 1, wherein, determining an impedance model corresponding to each resonant band in the M resonant bands and initial parameters of the impedance model, comprising: when any resonant band is a band of a wide resonant peak, determining that the impedance model of the any resonant band is p sets of RLC parallel circuits; determining resistance values, capacitance values, and inductance values of the p sets of RLC parallel circuits of the any resonant band as equivalent parameters, and splitting the equivalent parameters into p sets of parameters as initial parameters corresponding to each set of RLC parallel circuits, wherein the sum of the same type of parameters in the p sets of initial parameters is equal to the same type of parameters in the equivalent parameters.

5. The method of claim 1, wherein, The method further comprises: when the i-th resonant band is the last band and is a band of a regular resonant peak, performing joint optimization on initial parameters of the last two RLC parallel circuits corresponding to the M resonant bands and corresponding optimization floating ranges through the optimization algorithm, and taking the corresponding RLC parameters with the minimum error between the actual parameters and less than a set value as the model parameters of the last two RLC parallel circuits.

6. The method according to any one of claims 1-5, characterized in that, The passive device includes at least one of a motor, a resistor, a capacitor, an inductor, and a converter.

7. The method according to any one of claims 1-5, characterized in that, The set of RLC parallel circuits comprises a resistor, a capacitor and an inductor, and the resistor, the capacitor and the inductor are in parallel.

8. A passive device impedance fitting apparatus, characterized by, The device comprises: An acquisition unit configured to acquire an impedance characteristic curve obtained by actually measuring the passive device; A division unit configured to divide the impedance characteristic curve to obtain M resonant wave bands arranged in sequence, wherein each of the M resonant wave bands has one normal resonant peak or one wide resonant peak, and M is an integer greater than or equal to 1; A determination unit configured to determine an impedance model corresponding to each of the M resonant wave bands and initial parameters of the impedance model, wherein the impedance model corresponding to the normal resonant peak is a set of RLC parallel circuits, and the impedance model corresponding to the wide resonant peak is p sets of RLC parallel circuits connected in series, and p is an integer greater than or equal to 2; An optimization unit configured to, for the impedance model corresponding to the i th resonant wave band, perform iterative optimization on the initial parameters of the i th impedance model and a corresponding optimization floating range by a preset optimization algorithm, and take a set of parameters with the minimum error to actual parameters and the error being less than a set value as model parameters of the i th impedance model, wherein the actual parameters are RLC parameters of the i th resonant wave band, and i is sequentially taken as 1 to M; An emulation unit configured to obtain an equivalent impedance model for replacing the passive device based on the model parameters of the impedance models corresponding to the M resonant wave bands respectively.

9. An electronic device, comprising: The electronic device comprises a processor and a memory coupled to each other, and the memory stores a computer program, when the computer program is executed by the processor, the electronic device executes the method as claimed in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, when the computer program is run on a computer, the computer executes the method as claimed in any one of claims 1-5.

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