Model optimization method based on brushless direct current motor product dynamic parameter identification

Abstract

The invention provides a model optimization method based on brushless direct current motor product dynamic parameter identification, and belongs to the field of electronic product reliability simulation, and the method comprises the following steps: S1, collecting the basic information of a brushless direct current motor; s2, constructing a simulation model; s3, constructing an agent model; s4, acquiring reliability enhancement test data; s5, performing dynamic parameter identification by using a reverse optimization algorithm to obtain a dynamic parameter combination; and S6, optimizing and verifying the simulation model by using the dynamic parameter combination. According to the dynamic parameter identification and calibration method for the simulation model, the trueness of the simulation model can be greatly improved, and powerful support is provided for performance evaluation and reliability prediction of products under complex working conditions. According to the method, the response precision and model stability of the brushless direct current motor under complex working conditions are effectively improved, and a solid foundation is provided for reliability evaluation of avionic products.

Description

Technical Field

[0001] The invention belongs to the field of electronic product reliability simulation, and in particular relates to a model optimization method based on dynamic parameter identification of brushless DC motor products. Background Art

[0002] Brushless DC motors are widely used in avionics products to provide efficient motor drive support for the system. However, in complex and changing environmental conditions, the key parameters of this type of motor (such as resistance, inductance and motor constant) will change significantly with the environmental conditions, thus affecting its overall performance and reliability. Some key parameters of avionics products will change significantly with environmental conditions. Since avionics equipment usually works in extreme and changing environments such as temperature, humidity, air pressure, and electromagnetic interference, these external conditions not only affect product performance, but also accelerate component aging and performance degradation. Therefore, it is crucial to establish a high-fidelity simulation model that can truly represent the performance changes of the product under complex environmental conditions for reliability design and performance evaluation.

[0003] In the current process of building a high-fidelity model, key parameters need to be effectively identified and calibrated to ensure the accuracy of the model under different working conditions. This process faces the following three main challenges:

[0004] First, the parameters are highly dependent on and diverse working conditions, and the traditional fixed parameter assumptions cannot adapt to the actual working conditions of avionics products. For example, when a certain model of flight computer works in a high temperature environment, the temperature of the core electronic components will rise significantly, resulting in a decrease in data processing speed and an increase in storage unit leakage current; in a low-pressure and high-humidity environment, the signal transmission speed and electromagnetic compatibility may also change, directly affecting the overall reliability of the system. For this reason, the simulation model needs to be dynamically adjusted according to different working conditions in order to truly reflect the performance changes of the product in a specific environment. For brushless DC motors, ambient temperature, humidity, air pressure and other working conditions will affect key parameters such as motor constants, resistance and inductance, and the existing fixed parameter assumptions cannot accurately describe these dynamic changes.

[0005] Second, the identification and calibration of operating parameters are difficult, and there is currently a lack of multi-operating condition identification technology suitable for complex environments. The operating parameters of avionics products are complex and diverse, including temperature, pressure, humidity, electromagnetic interference, vibration, etc. These variables do not exist independently, but are interrelated. For example, in a high-frequency vibration environment, the effects of temperature and humidity on circuit boards will be superimposed, resulting in a significant increase in the computational complexity of parameter identification. Existing calibration methods usually rely on experimental data, and the variation patterns of experimentally obtained parameters under various operating conditions are often difficult to accurately establish, making the operating condition identification and calibration accuracy unable to meet the requirements of high-fidelity models.

[0006] Third, high-fidelity model calibration experiments are expensive and time-consuming. Avionics products usually have high reliability requirements. The traditional model calibration process requires a large amount of actual test data to support parameter identification, which takes a long time and is costly. In particular, multivariate calibration experiments in complex environments are difficult to implement. As a result, in actual applications, parameter adjustments can only be made based on data from small samples or simplified working conditions, resulting in insufficient adaptability of the model to working conditions.

[0007] To solve the above problems, it is necessary to further develop efficient and accurate multi-condition identification and calibration technology, and combine simulation data and experimental data to support the construction of high-fidelity models of avionics products in complex environments, and achieve accurate prediction and effective optimization of product performance. Summary of the invention

[0008] In view of the deficiencies of the prior art, the present invention provides a model optimization method based on the dynamic parameter identification of brushless DC motor products. The method samples the response of the product under different environmental conditions, combines the proxy model and the optimization algorithm, realizes the accurate calibration of the dynamic parameters, obtains the real-time and accurate dynamic parameters, and optimizes the model using the obtained dynamic parameters, thereby constructing a simulation model that highly reflects the product performance. The dynamic parameter identification method of the present invention is specifically used for brushless DC motors in avionics systems. The dynamic parameters of the motor system change significantly in complex environments, and the fidelity of the existing model is poor. Therefore, special calibration technology is required to identify the dynamic parameters and calibrate and optimize the simulation model.

[0009] To achieve the above object, the present invention discloses the following technical solution:

[0010] A model optimization method based on dynamic parameter identification of brushless DC motor products comprises the following steps:

[0011] S1. Collect basic information of brushless DC motor: The basic information of brushless DC motor includes CAD model of the motor, structural parameters of the motor, key performance parameters, power and control parameters of the motor and typical working environment;

[0012] S2. Build simulation model: Based on the CAD model of the brushless DC motor, combined with the motor structural characteristics and electromagnetic dynamics characteristics, build a preliminary simulation model of the motor;

[0013] S3. Constructing a proxy model: Taking the response data of the initial simulation model as the basis, a proxy model is constructed through data fitting technology. The input of the proxy model is a dynamic parameter combination, and the output is a key performance indicator;

[0014] S4. Obtain reliability enhancement test data: In a laboratory environment, the actual response data of the motor under different working conditions is tested through reliability enhancement tests. The actual response data includes performance response and motor health status data;

[0015] S5. Use the reverse optimization algorithm to identify dynamic parameters: Based on the proxy model, the reverse optimization algorithm is applied to find the dynamic parameter combination that makes the simulation model response most consistent with the actual data using the experimental data under different working conditions. This step specifically includes the following sub-steps:

[0016] S51, determine the optimization variables and optimization targets of the reverse optimization algorithm, the optimization variables are the resistance value, the motor constant and the sensitivity coefficient K of the inductance value R , K Ce , K L ,The optimization goal is to minimize the error between the motor response output by the proxy model and the measured data under all temperature conditions;

[0017] S52, import the maximum torque and no-load speed data measured under different temperature conditions, construct the objective function, use the particle swarm algorithm to collaboratively search through multiple particles in the solution space, continuously iterate and update, and finally converge to the global optimal dynamic parameter combination; the dynamic parameter combination includes the resistance value R, the motor constant Ce and the inductance value L of the brushless DC motor; the objective function is specifically:

[0018] The objective function is as follows:

[0019]

[0020] Where x = [K R ,K Ce ,K L ] is the sensitivity coefficient to be optimized, T model,j and n model,j are the maximum torque and no-load speed calculated by the proxy model, T meas,j and n meas,j is the corresponding measured data, M is the number of temperature conditions;

[0021] S6. Use dynamic parameter combination to optimize and verify the simulation model.

[0022] Preferably, in step S1, the structural parameters of the motor include winding structure, permanent magnet material, stator and rotor structure dimensions and air gap length; the key performance parameters of the motor include torque output, speed, efficiency and temperature change; the power and control parameters of the motor include voltage, current, controller setting value and excitation mode; typical working environment conditions include temperature, humidity, vibration and load conditions.

[0023] Preferably, in step S4, the performance response is temperature, no-load speed and maximum torque.

[0024] Preferably, in step S5, the relationship between the resistance value R, the motor constant Ce and the inductance value L of the brushless DC motor and the temperature is as follows:

[0025] R=R0+K R (T-T0)

[0026] Ce=Ce0+K Ce (T-T0)

[0027] L=L0+k L (T-T0)

[0028] Among them, R0, Ce0, L0 are dynamic parameter values ​​under T0 condition, and T and T0 are time.

[0029] Preferably, in step S52, each particle represents a possible sensitivity coefficient combination, and the position and speed of the particle are dynamically adjusted according to the individual optimal solution and the global optimal solution. The speed update formula is:

[0030] v i,d (t+1)=ωv i,d (t)+c1r1(p i,d -x i,d (t))+c2r2(g d -x i,d (t));

[0031] The position update formula is:

[0032] x i,d (t+1)=x i,d (t)+v i,d (t+1)

[0033] Among them, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p i is the individual optimal position of the particle, g is the global optimal position, and the algorithm gradually converges through multiple iterations and finally finds the optimal sensitivity coefficient combination at the global optimal point.

[0034] Preferably, step S6 specifically includes, after determining the dynamic parameters of the simulation model, embedding the relationship between the corresponding dynamic parameters and temperature into the simulation model, and applying it to the resistance value R, motor constant Ce and inductance value L of the motor, so that it can be adaptively adjusted with the change of temperature L, so as to simulate the motor response under different temperature conditions.

[0035] Preferably, the simulation model in step S2 is constructed using MATLAB Simulink simulation software, and the simulation model includes an input signal, a load signal, feedback control, a motor principle model, and a reduction device.

[0036] Preferably, the proxy model in step S3 is a neural network framework model.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The simulation model dynamic parameter identification and calibration method proposed in this invention can greatly improve the authenticity of the simulation model, and provide strong support for product performance evaluation and reliability prediction under complex working conditions. This method effectively improves the response accuracy and model stability of the brushless DC motor under complex working conditions, and provides a solid foundation for the reliability evaluation of avionics products.

[0039] (2) This invention proposes a brushless DC motor simulation model calibration method based on dynamic parameter identification under environmental conditions. Aiming at the performance and reliability evaluation requirements of aviation airborne brushless DC motors, a technical process for dynamic parameter identification and calibration of simulation models is established. The study proposes key technical implementation methods such as operating condition parameter combination sampling, proxy model construction, reliability enhancement test data acquisition, and dynamic parameter calibration of reverse optimization algorithm. The dynamic parameter calibration experiment of the simulation model is carried out with a certain type of aviation brushless DC motor as the object, verifying the accuracy and practicality of the model calibration based on this method. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0041] Figure 2 It is a functional block diagram of the brushless DC motor of the present invention;

[0042] Figure 3 It is a simulation model diagram of the present invention;

[0043] Figure 4 It is a schematic diagram of the working circuit of the brushless DC motor of the present invention;

[0044] Figure 5 The no-load input step signal output signal curve of the present invention;

[0045] Figure 6 The motor speed curve of the no-load input step signal of the present invention;

[0046] Figure 7 is the input torque variation curve of the present invention;

[0047] Figure 8a , Figure 8b and Figure 8c The resistance, motor constant and inductance of the present invention are sampled and distributed in each interval respectively;

[0048] Figure 9a and Figure 9b They are the idling speed and maximum torque distribution characteristics of the present invention respectively;

[0049] Fig.10a and Fig.10b The proxy model prediction data of the present invention is compared with the measured data respectively;

[0050] Fig.11a and Fig.11b The motor responses under different temperature conditions are measured by the step test of the present invention respectively;

[0051] Fig.12 It is a reverse optimization flow chart of the dynamic parameter relationship coefficients of the present invention;

[0052] Fig.13 It is a schematic diagram for comparing the measured results and the simulation results of the present invention. DETAILED DESCRIPTION

[0053] The exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0054] The overall method of the present application is based on the simulation model, which changes the model dynamic parameters respectively, compares the simulation response parameters under different model dynamic parameters, establishes an association relationship between the model dynamic parameters and the simulation response parameters that have an influence relationship, and provides constraints for the establishment of the overall mapping relationship between the two. Secondly, due to the large number of model parameters of the product performance model and the wide range of parameter values, parameter identification requires a lot of time and data. The method of using artificial neural network to construct a proxy model to establish a method for the mapping relationship between model dynamic parameters and simulation response parameters can quickly obtain the simulation response parameters corresponding to different model dynamic parameter values. Based on the value constraints of the model dynamic parameters, multiple groups of dynamic parameter values ​​are generated, and the parameter values ​​are respectively input into the simulation model to obtain the simulation response parameter values. The dynamic parameter values ​​are used as the input of the proxy model neural network, and the simulation response parameter values ​​are used as the output of the proxy model neural network. Based on multiple groups of dynamic parameters and corresponding simulation response parameters, the neural network is trained. After the model training is completed, the various stress conditions of the strengthening test and the model dynamic parameters generated according to the constraints are input into the neural network model to obtain multiple groups of simulation outputs. Finally, a suitable optimization objective function is determined to represent the model output error under multiple stress conditions. A suitable optimization algorithm is used to find a set of model dynamic parameters that minimize the objective function, complete parameter identification, and use the identified dynamic parameters to optimize the simulation model to improve the accuracy of the model.

[0055] Specifically, the present invention provides a model optimization method based on dynamic parameter identification of brushless DC motor products, such as Figure 1 As shown, it includes the following steps:

[0056] S1. Collect basic information of brushless DC motor: The basic information of brushless DC motor includes the CAD model of the motor, the structural parameters of the motor, key performance parameters, power and control parameters of the motor and the typical working environment.

[0057] In step S1, the structural parameters of the motor include winding structure, permanent magnet material, stator and rotor structure dimensions and air gap length; the key performance parameters of the motor include torque output, speed, efficiency and temperature change; the power and control parameters of the motor include voltage, current, controller setting value and excitation mode; typical working environment conditions include temperature, humidity, vibration and load conditions.

[0058] S2. Build a simulation model: Based on the CAD model of the brushless DC motor, combined with the motor's structural characteristics and electromagnetic dynamics characteristics, build a preliminary simulation model of the motor.

[0059] S3. Constructing a proxy model: Taking the response data of the initial simulation model as the basis, a proxy model is constructed through data fitting technology. The input of the proxy model is a dynamic parameter combination, and the output is a key performance indicator.

[0060] The proxy model is a data-driven approximate model that constructs a sample data set by combining dynamic parameters and simulation model output responses into an 'input-output' array pair. Next, the proxy model is used to construct the mapping relationship between data groups, thereby replacing the simulation model based on physical mechanisms. The proxy model establishes an approximate mathematical model with high computational efficiency and accuracy by acquiring characteristic information from the sample. In a specific embodiment of the present application, an artificial neural network method is selected to construct the proxy model. The structure of the artificial neural network can capture the complex nonlinear relationships in the data, and increase the model complexity by adjusting the network architecture, thereby improving the adaptability and generalization ability of the model. The construction of the proxy model of the present application mainly includes the following steps:

[0061] Determine the neural network structure:

[0062] First, determine the structure of the neural network. The number of nodes in the input layer of the neural network is usually the same as the number of features, and the number of nodes in the input layer is the same as the number of features in the output. Through multiple attempts or hyperparameter search strategies such as grid search, random search, or Bayesian optimization, find the optimal number of layers and nodes within a defined range.

[0063] Network weight initialization: After determining the structure of the neural network, initialize the weights of the neural network.

[0064] Data input and forward propagation process:

[0065] The data first enters the input layer of the network. The input layer usually contains a number of neurons equal to the number of features. Suppose there is a data set of shape m, n, where m is the number of samples and n is the number of features. Each sample is a 1×n vector, which will be input into the n neurons of the input layer.

[0066] Before the data enters the hidden layer or output layer from the input layer, it will be linearly transformed through the weight matrix and bias vector. Assuming the weight matrix is ​​w and the bias vector is b, the net input z of each neuron j is j It can be calculated as:

[0067]

[0068] Among them, x i is the i-th element of the input vector, w ij is the weight matrix (i,j) element, b j is the bias term of the jth neuron. Next, the net input z j It will be passed to an activation function (ReLU, sigmoid or tanh) to calculate the activation value a of the jth neuron j :

[0069] a j=f(z j )

[0070] Among them, f is the activation function, and the activation value will be used as the input of the next layer for similar linear transformation and nonlinear activation. The activation value is passed to the next hidden layer after being processed by the above weighting, bias and activation function, and then the data is passed to the output layer through the last hidden layer. Secondly, the back propagation method is used to calculate the gradient of the loss function with respect to the network weights. Through the chain rule, it is calculated layer by layer from the output layer to the input layer. The optimization algorithm is used to update the weights according to the calculated gradients. The weights are usually adjusted to reduce the prediction error. The above steps are repeated iteratively until the performance of the network meets the predetermined criteria or the pre-set maximum number of iterations is reached.

[0071] Finally, the coefficient of determination R 2 , root mean square error RMSE, mean square error MRAE, and maximum absolute error MAE are used to evaluate the performance of the proxy model.

[0072] S4. Obtain reliability enhancement test data: In a laboratory environment, the actual response data of the motor under different working conditions is tested through reliability enhancement tests. The actual response data includes performance response and motor health status data; the performance response is temperature, no-load speed and maximum torque.

[0073] S5. Use the reverse optimization algorithm to identify dynamic parameters: Based on the proxy model, the reverse optimization algorithm is applied to find the dynamic parameter combination that makes the simulation model response most consistent with the actual data using the experimental data under different working conditions. This step specifically includes the following sub-steps:

[0074] S51, determine the optimization variables and optimization targets of the reverse optimization algorithm, the optimization variables are the resistance value, the motor constant and the sensitivity coefficient K of the inductance value R , K Ce , K L The optimization goal is to minimize the error between the motor response output by the proxy model and the measured data under all temperature conditions.

[0075] S52, import the maximum torque and no-load speed data measured under different temperature conditions, construct the objective function, use the particle swarm algorithm to collaboratively search through multiple particles in the solution space, continuously iterate and update, and finally converge to the global optimal dynamic parameter combination; the dynamic parameter combination includes the resistance value R, the motor constant Ce and the inductance value L of the brushless DC motor; the objective function is specifically:

[0076] The objective function is as follows:

[0077]

[0078] Where x = [KR ,K Ce ,K L ] is the sensitivity coefficient to be optimized, T model,j and n model,j are the maximum torque and no-load speed calculated by the proxy model, T meas,j and n meas,j is the corresponding measured data, and M is the number of temperature conditions.

[0079] The relationship between the resistance value R, the motor constant Ce and the inductance value L of the brushless DC motor and the temperature in step S5 is as follows:

[0080] R=R0+K R (T-T0

[0081] Ce=Ce0+K Ce (T-T0)

[0082] L=L0+k L (T-T0)

[0083] Among them, R0, Ce0, L0 are dynamic parameter values ​​under T0 condition, and T and T0 are time.

[0084] Preferably, in step S52, each particle represents a possible sensitivity coefficient combination, and the position and speed of the particle are dynamically adjusted according to the individual optimal solution and the global optimal solution. The speed update formula is:

[0085] v i,d (t+1)=ωv i,d (t)+c1r1(p i,d -x i,d (t))+c2r2(g d -x i,d (t)).

[0086] The position update formula is:

[0087] x i,d (t+1)=x i,d (t)+v i,d (t+1)

[0088] Among them, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p i is the individual optimal position of the particle, g is the global optimal position, and the algorithm gradually converges through multiple iterations and finally finds the optimal sensitivity coefficient combination at the global optimal point.

[0089] S6. Optimize and verify the simulation model using a combination of dynamic parameters. Specifically, after determining the dynamic parameters of the simulation model, embed the relationship between the corresponding dynamic parameters and temperature into the simulation model, and apply it to the resistance value R, motor constant Ce and inductance value L of the motor, so that it can be adaptively adjusted with the change of temperature L, so as to simulate the motor response under different temperature conditions. In this way, the simulation model can be optimized and the fidelity of the simulation model can be improved, and then verification is performed to ensure the effectiveness of the method.

[0090] The present invention is described in detail below by way of examples.

[0091] Taking a certain type of aviation airborne brushless DC motor as the object, through dynamic parameter identification and calibration, a simulation model is constructed to improve the performance prediction accuracy of the motor under different working conditions, reliability simulation analysis is carried out, and an integrated model of motor performance and reliability is established to truly reflect the performance changes and reliability evolution process of the product under different working conditions. In order to further improve the accuracy of the simulation model, this study identified and calibrated the key dynamic parameters of the simulation model based on the measurement data obtained from the reliability enhancement test (HALT). By comparing the experimentally measured response data with the simulation results, the reverse optimization algorithm is used to optimize the key dynamic parameters, so that the simulation model can more accurately represent the performance and degradation laws of the motor under different working conditions, thereby significantly improving the authenticity and applicability of the model, and providing effective support for the reliability assessment and life prediction of aviation airborne motors.

[0092] The key performances of an aircraft-mounted brushless DC motor include no-load speed and output torque, and the output torque is characterized by selecting the maximum output torque. Specifically, the method of this embodiment includes the following steps:

[0093] S1. Product information collection.

[0094] A certain type of aircraft-mounted brushless DC motor consists of three major components: stator, rotor, and rear cover. This DC motor is used to receive position commands from the controller and drive the output shaft of the servo to rotate. The composition and functions of the brushless DC motor are shown in Table 1, the basic parameters are shown in Table 2, the motor parameters are shown in Table 3, and the actual measured performance data of the brushless DC motor is shown in Table 4. The functional block diagram of the brushless DC motor is shown in Figure 2 .

[0095] Table 1 Composition and function of brushless DC motor

[0096]

[0097] Table 2 Basic parameters

[0098]

[0099]

[0100] Table 3 Motor parameters

[0101] Serial number Parameter name unit Reference value tolerance 1 Stator coil resistance Ω 1.582 ±0.15 2 Stator coil inductance mh 0.479 ±0.045 3 Motor torque coefficient Nm*s 0.01979 ±0.0018

[0102] Table 4 Brushless DC motor performance measured data

[0103] Serial number No-load speed (rpm) Maximum torque(Nm) 1 13080 0.2058 2 12978 0.2205

[0104] S2. Preliminary construction of the simulation model.

[0105] According to the motor structure and working principle, a certain type of aviation brushless DC motor model is established using MATLAB Simulink simulation software. Figure 3 The basic parameters are shown in Table 2. The model consists of: (1) input signal, (2) load signal, (3) feedback control, (4) motor principle model, and (5) reduction device. The model provides data support for subsequent dynamic parameter identification and calibration, ensuring that the simulation model can accurately predict the dynamic response of the motor under different working conditions.

[0106] According to the principle of brushless DC motor, its working circuit diagram is shown in Figure 4 The six drive elements in the drive circuit operate according to the control algorithm, connecting the corresponding stator windings in turn, generating a rotating magnetic field on the stator, and driving the rotor embedded with permanent magnets to rotate. When the rotor rotates, the stator windings relatively cut the magnetic flux lines, generating back electromotive force.

[0107] For a brushless DC permanent magnet synchronous motor, the voltage balance equation of the stator three-phase winding is:

[0108]

[0109] Where e a 、e b 、e c is the stator back electromotive force of each phase,

[0110] R a , R b , R c , is the stator winding resistance of each phase,

[0111] L a , L b , L c is the inductance of each phase winding of the stator,

[0112] L ab , L ac , L ba , L bc , L ca , L cb is the phase-to-phase inductance of the stator winding,

[0113] i a 、i b 、i c is the stator current of each phase.

[0114] Assuming that the three-phase windings of the brushless DC permanent magnet synchronous motor are symmetrical and ignoring the influence between magnetic groups, the mutual inductance between the stator phase windings is considered to be a constant. That is:

[0115] L a =L b =L c =L s , R a =R b =R c =R,L ab =L ba =L ac =L ca =L bc =L cb =M.

[0116] Since the three-phase winding is symmetrical, i a +i b +i c =0, Mi a +Mi b +Mi c =0, and we get:

[0117]

[0118] Where, L = L i -M.

[0119] For Y-type windings without neutral point lead, the phase voltage is difficult to measure, and the line voltage equation is more practical. When the power tube is turned on, the line voltage is approximately equal to the DC side voltage of the inverter bridge.

[0120]

[0121] For the line voltage of the Y-connected winding, the transfer function of the line voltage and the output speed is similar to that of the brushed DC motor.

[0122] When the brushless DC motor is working, the three phases A, B, and C are connected in pairs. When any two phases are turned on, the phase currents are equal in magnitude and opposite in direction, taking phases A and B as an example.

[0123] i A =-i B =i

[0124]

[0125] When phases A and B are connected, the relationship between the line voltage and current is:

[0126]

[0127] Where, Ω is the motor speed, unit is rad / s, C e It is the torque coefficient of the motor, that is, the ratio of the back electromotive force of the motor winding to the speed. It is also the ratio of the motor electromagnetic torque (Nm) to the current (A).

[0128] At this time, the motor mechanical model is:

[0129]

[0130] Where, T L is the load torque, J is the rotational inertia of the motor shaft and rotor as a whole, and T is L =0, the relationship between line current and speed is obtained:

[0131]

[0132] Substituting the line voltage and current relationship into the equation, we get the motor input voltage and speed relationship:

[0133]

[0134] When the load torque is not 0, the load torque can be used as the system input, and its transfer function is:

[0135]

[0136] According to the linear system superposition principle, the output response is equal to U when there is load torque. d (s) and T L (s) The sum of their respective actions.

[0137] According to the relationship between the motor input voltage and output speed, the motor model is established in MATLAB Simulink. The overall input signal of the model is the position signal, which is converted into the motor input voltage. The motor parameters are marked in the model. The motor parameters are shown in Table 3:

[0138] The measured data of the brushless DC motor are shown in Table 4. The no-load speed and maximum torque represent the two characteristic points of the motor characteristic curve, namely, zero load and stalled rotor. The relationship between the remaining speed and torque can be represented by the straight line between these two points.

[0139] The no-load speed and maximum torque of the motor model are simulated. The load torque is set to 0Nm, and a step signal is input. The time domain diagram of the input signal and the output signal and the no-load speed change curve are shown in Figure 5 and Figure 6 .

[0140] When the motor is unloaded, the speed increases rapidly after the input signal changes; when the motor approaches the input required servo position, the speed gradually decreases and finally becomes 0. The maximum speed in the process is 130004. When the model load torque is increased to make the motor stall, and the drive signal is still input in the 1st second, the output torque change curve is shown in Figure 7 After the motor is started, due to stalling, its output electromagnetic torque increases rapidly, and the maximum torque is 0.2127Nm.

[0141] S3. Build an agent model.

[0142] Before establishing the proxy model, the data needs to be preprocessed. In this study, the data used as input and output only contain numerical data, so data preprocessing only includes data normalization and data segmentation. Through normalization, all parameters used as input are scaled to the range of [0,1]. Then, the data is segmented. The obtained sample data is divided into a training set and a test set in an 80 / 20 ratio. The training set is used to train the neural network, and the test set is used to evaluate the performance of the trained neural network. After obtaining the preprocessed data, the neural network proxy model is established. The number of neurons in the input layer is consistent with the dimension of the dynamic parameters used as input. There are three hidden layers, and the number of neurons is 128, 64 and 32 respectively. The number of neurons in the output layer is the same as the output response dimension. The activation function is selected as the ReLU function.

[0143] As environmental stress changes, the dynamic parameter values ​​in the simulation model will change. In the process of dynamic parameter selection, parameters that change greatly with environmental stress (temperature, load) conditions are generally selected. At the same time, the fluctuation of the selected dynamic parameter values ​​also has a more obvious impact on the output results of the motor. Under the above selection criteria, the resistance value R, the motor constant Ce and the inductance value L are selected as the dynamic parameters for research, and the no-load speed of the motor under zero load conditions and the maximum torque under stall conditions are used as responses.

[0144] After determining the input and output data of the proxy model, the dynamic parameter combination is generated by Latin hypercube. First, the dynamic parameter space is defined to determine the output parameters and their ranges of the dynamic parameters. Then, Latin hypercube is used to generate samples to ensure that the samples cover the entire range of the parameters. The frequency of each parameter in the dynamic parameter group [R, Ce, L] in the sampling space is as follows: Figure 8a-8c As shown, the frequencies in each slice are basically the same in the entire value range, ensuring the randomness and uniformity of the distribution of data samples used for training.

[0145] After obtaining the dynamic parameters obtained by Latin hypercube sampling, the dynamic parameter combination is used as the parameter setting of the simulation model. The output response of the corresponding simulation model, namely the idling speed and maximum torque data, is obtained by calculation. The distribution of the two dynamic response data is as follows: Figure 9a and Figure 9b The idling speed distribution is basically uniform within the value range, and the maximum torque presents a quasi-normal distribution feature.

[0146] After obtaining the simulation output data under different dynamic parameter combinations, the surrogate modeling technology is used to build a simplified model to efficiently simulate the dynamic response of the motor. First, according to the distribution characteristics of no-load speed and maximum torque, a suitable fitting method is selected. In this case, a neural network is selected to build the surrogate model. Through training and cross-validation of simulation data, the prediction accuracy of the model in the entire parameter space is ensured.

[0147] To improve the reliability of the model, error analysis is performed and model parameters are adjusted when necessary to reduce the deviation, so that the proxy model can achieve high accuracy in predicting no-load speed and maximum torque. In addition, the verification results of the proxy model are as follows Fig.10a As shown in the figure, the measured data is Fig.10b As shown in the figure, the comparison shows that the proxy model can stably predict the response characteristics of the motor under different dynamic parameter combinations, providing an accurate and fast-responding proxy model for subsequent parameter optimization and reliability evaluation.

[0148] S4. Acquisition of reliability enhancement test (HALT) data.

[0149] In the embodiment, in order to obtain the response data of the brushless DC motor under different temperature conditions, a reliability enhancement test (HALT) was carried out. This test mainly uses a temperature step test to gradually increase or decrease the temperature to simulate the operation of the motor under extreme temperatures, so as to obtain the key response characteristics of the motor under these conditions. Since the resistance value, motor constant and inductance value are temperature-sensitive dynamic parameters, they will affect the performance of the motor with temperature changes, so the response data under these temperature conditions are particularly important for model calibration. In this case, the motor response signal in the range of 5°C-65°C was mainly measured, with a step size of 5°C.

[0150] Through temperature testing, the HALT test effectively captures the response characteristics of the motor under different temperature conditions. Fig.11a and Fig.11b As shown in the figure, the no-load speed and maximum torque value at each temperature are specifically recorded. These data reflect the direct impact of temperature on motor performance, provide a basis for the parameter identification of the simulation model, and make the response prediction of the model under different temperature conditions closer to the actual situation.

[0151] S5. Dynamic parameter identification of reverse optimization algorithm.

[0152] First, determine the optimization variables and optimization targets of the reverse optimization algorithm. Dynamic parameters are usually adjusted as environmental conditions change. For example, the relationship between the resistance value R, motor constant Ce and inductance value L of a brushless DC motor and temperature is as follows:

[0153] R=R0+K R (T-T0)

[0154] Ce=Ce0+K Ce (T-T0)

[0155] L=L0+k L (T-T0)

[0156] Among them, R0, Ce0, and L0 are known values, which are the dynamic parameter values ​​under T0 conditions, and the coefficients represent the sensitivity coefficients of each parameter with temperature changes. In order to ensure the physical rationality of the optimization results and avoid non-physical dynamic parameter combinations, these sensitivity coefficients K R , K Ce , K L Set as optimization variables as the adjustment object of the reverse optimization algorithm. Ultimately, by adjusting these coefficients, the error between the motor response (such as no-load speed and maximum torque) output by the proxy model and the measured data under all temperature conditions is minimized, achieving accurate identification and calibration of dynamic parameters.

[0157] In the dynamic parameter calibration, the particle swarm optimization algorithm (PSO) is used to perform reverse optimization to obtain the optimal dynamic parameter combination. First, the maximum torque and no-load speed data measured under different temperature conditions are imported to define the optimization objective function and the sensitivity coefficient K to be optimized. R , K Ce , K L The optimization goal is to minimize the error between the output response of the proxy model and the measured data. The particle swarm algorithm searches multiple particles in the solution space in a coordinated manner, continuously iterates and updates, and finally converges to the global optimal dynamic parameter combination, ensuring that the adjustment of dynamic parameters can accurately reflect the motor performance response under different temperature conditions. This process ensures the accuracy of the calibration results under multiple working conditions and achieves efficient optimization of dynamic parameters. Fig.12 This is a flowchart of reverse optimization, which explains the steps and iterative process of the optimization algorithm. The specific steps are as follows:

[0158] 1. Based on the relationship between dynamic parameters and stress, the position of each particle is determined as [p1, p2, …, p n ], where p i =[p i1 ,pi2 ,…,p ij ].

[0159] 2. Set the sample size of the particle swarm to N, and use Latin hypercube sampling technology to determine the position of the particles in the initial particle swarm sample within the value range of the coefficient of the dynamic parameter relationship.

[0160] 3. Through x i =f(C) and the position of each particle [p1,p2,…,p n ]Determine the dynamic parameter values ​​under various stress conditions.

[0161] 4. Call the aforementioned neural network proxy model, input the proxy model with the dynamic parameter combination under all stress conditions of each particle, and obtain the output response under the corresponding stress conditions.

[0162] 5. The fitness function is calculated based on the sum of the differences between the response outputs produced by the surrogate model under all stress conditions and the measured responses of the step test

[0163] 6. Update the historical optimal position of each particle and the global optimal position of the group.

[0164] 7. The algorithm ends based on whether the number of iterations or the fitness function reaches a predetermined standard. If the termination condition is met, the algorithm ends; otherwise, return to step 3 and continue iterating.

[0165] S6. Optimization and verification of simulation model.

[0166] After the dynamic parameters of the simulation model are determined based on the reverse optimization algorithm, the relationship between the corresponding dynamic parameters and temperature is embedded in the simulation model to simulate the motor response under different temperature conditions. Specifically, the updated relationship is applied to the motor resistance value R, motor constant Ce and inductance value L, so that it can be adaptively adjusted with the change of temperature L.

[0167] Next, the optimized and calibrated simulation model is simulated and verified under multiple temperature conditions, and the response data such as no-load speed and maximum torque at different temperatures are calculated. Then, these simulation results are compared with the experimental measured data, and the accuracy of the calibration results is further verified by comparative analysis. In this embodiment, the comparison results show that the response values ​​of the simulation model under various temperature conditions are highly consistent with the measured data, and the root mean square errors of no-load speed and maximum torque are 3.03×10 -7 and 1.16×10 -15 The measured response and the response obtained by the simulation model are as follows Fig.13As shown, through this verification step, the effectiveness and reliability of the calibrated simulation model under multiple working conditions are proved, and the performance response of the motor under different temperature conditions can be accurately predicted. Compared with the existing methods, the accuracy of the model is greatly improved. This high-precision model provides data support for the dynamic performance analysis and reliability evaluation of the brushless DC motor. At the same time, the overall method of the proxy model in the present invention is also more reliable. The construction of the proxy model makes the overall method have higher execution degree, and can quickly and accurately identify dynamic parameters. The optimized high-precision model can better perform dynamic performance analysis and reliability evaluation on the brushless DC motor.

[0168] Specifically, the application effect of this technology in aviation brushless DC motor simulation and reliability analysis is mainly reflected in the following three aspects:

[0169] (1) In the initial design stage of the brushless DC motor, the combination sampling of operating conditions and the rapid response prediction of the surrogate model can be used to simulate the changes in the performance of the brushless DC motor under different environmental stress conditions, achieve preliminary optimization of key dynamic parameters, and help identify potential risks of the motor under extreme operating conditions during design.

[0170] (2) During the verification phase of the brushless DC motor, dynamic parameter calibration and reverse optimization algorithm based on reliability enhancement test data can achieve high-precision calibration of temperature-sensitive parameters, effectively solving the error accumulation problem of traditional static parameter models in a changing environment, and ensuring that the response of the simulation model during the verification phase is consistent with the actual working conditions.

[0171] (3) During the use phase of the brushless DC motor, this method can support field fault cause analysis and rapid verification and optimization. The deviation between the dynamic response measured in the field and the predicted response of the proxy model can be verified by simulation methods, and the impact of different environmental factors on motor performance can be quantified, thereby providing reliable simulation data support for design improvement and maintenance, ensuring the dynamic adaptability of the model under changing environmental and load conditions, and realizing real-time monitoring and rapid optimization of motor performance.

[0172] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A model optimization method based on dynamic parameter identification of brushless DC motor products, characterized in that: It includes the following steps: S1. Collect basic information of brushless DC motor: The basic information of brushless DC motor includes CAD model of the motor, structural parameters of the motor, key performance parameters, power and control parameters of the motor and typical working environment; S2. Build simulation model: Based on the CAD model of the brushless DC motor, combined with the motor structural characteristics and electromagnetic dynamics characteristics, build a preliminary simulation model of the motor; S3. Constructing a proxy model: Taking the response data of the initial simulation model as the basis, a proxy model is constructed through data fitting technology. The input of the proxy model is a dynamic parameter combination, and the output is a key performance indicator; S4. Obtain reliability enhancement test data: In a laboratory environment, the actual response data of the motor under different working conditions is tested through reliability enhancement tests. The actual response data includes performance response and motor health status data; S5. Use the reverse optimization algorithm to identify dynamic parameters: Based on the proxy model, the reverse optimization algorithm is applied to find the dynamic parameter combination that makes the simulation model response most consistent with the actual data using the experimental data under different working conditions. This step specifically includes the following sub-steps: S51, determine the optimization variables and optimization targets of the reverse optimization algorithm, the optimization variables are the resistance value, the motor constant and the sensitivity coefficient K of the inductance value R , K Ce , K L ,The optimization goal is to minimize the error between the motor response output by the proxy model and the measured data under all temperature conditions; S52, import the maximum torque and no-load speed data measured under different temperature conditions, construct the objective function, use the particle swarm algorithm to collaboratively search through multiple particles in the solution space, continuously iterate and update, and finally converge to the global optimal dynamic parameter combination; the dynamic parameter combination includes the resistance value R, the motor constant Ce and the inductance value L of the brushless DC motor; the objective function is specifically: The objective function is as follows: Where x = [K R ,K Ce ,K L ] is the sensitivity coefficient to be optimized, T model,j and n model,j are the maximum torque and no-load speed calculated by the proxy model, T meas,j and n meas,j is the measured data of maximum torque and no-load speed, M is the number of temperature conditions; S6. Use dynamic parameter combination to optimize and verify the simulation model.

2. The model optimization method based on dynamic parameter identification of brushless DC motor products according to claim 1 is characterized in that: In step S1, the structural parameters of the motor include winding structure, permanent magnet material, stator and rotor structure dimensions and air gap length; the key performance parameters of the motor include torque output, speed, efficiency and temperature change; the power and control parameters of the motor include voltage, current, controller setting value and excitation mode; typical working environment conditions include temperature, humidity, vibration and load conditions.

3. The model optimization method based on dynamic parameter identification of brushless DC motor products according to claim 1 is characterized in that: In step S4, the performance responses are temperature, no-load speed and maximum torque.

4. The model optimization method based on dynamic parameter identification of brushless DC motor products according to claim 1, characterized in that: The relationship between the resistance value R, the motor constant Ce and the inductance value L of the brushless DC motor and the temperature in step S5 is as follows: R=R0+K R (T-T0) Ce=Ce0+K Ce (T-T0) L=L0+k L (T-T0) Among them, R0, Ce0, L0 are dynamic parameter values ​​under T0 condition, and T and T0 are time.

5. The model optimization method based on dynamic parameter identification of brushless DC motor products according to claim 4 is characterized in that: In step S52, each particle represents a possible sensitivity coefficient combination, and the position and speed of the particle are dynamically adjusted according to the individual optimal solution and the global optimal solution. The speed update formula is: v i,d (t+1)=ωv i,d (t)+c1r1(p i,d -x i,d (t))+c2r2(g d -x i,d (t)); The position update formula is: x i,d (t+1)=x i,d (t)+v i,d (t+1) Among them, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p i is the individual optimal position of the particle, g is the global optimal position, and the algorithm gradually converges through multiple iterations and finally finds the optimal sensitivity coefficient combination at the global optimal point.

6. The model optimization method based on dynamic parameter identification of brushless DC motor products according to claim 5 is characterized in that: Step S6 specifically includes, after determining the dynamic parameter combination of the simulation model, embedding the relationship between the dynamic parameter combination and temperature into the simulation model, and applying it to the resistance value R, motor constant Ce and inductance value L of the motor, so that it can be adaptively adjusted with the change of temperature L, so as to simulate the motor response under different temperature conditions.

7. The model optimization method based on dynamic parameter identification of brushless DC motor products according to claim 1, characterized in that: In step S2, the simulation model is constructed using MATLAB Simulink simulation software, and the simulation model includes input signals, load signals, feedback control, motor principle model and reduction device.

8. The model optimization method based on dynamic parameter identification of brushless DC motor products according to claim 1, characterized in that: The proxy model in step S3 is a neural network framework model.

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