A method and system for optimizing dynamic performance of a brushless generator

Abstract

The application relates to the field of computer technology application, in particular to a brushless generator dynamic performance optimization method and system, the method constructs a dynamic model modeling, control strategy optimization, hardware characteristic modeling, mechanical and heat dissipation parameter construction, simulation test and iteration correction whole-process closed-loop system, and the collaborative optimization of multi-domain parameters is realized. The application can improve the dynamic response speed and control precision of the brushless generator, and can enhance the system stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer technology application, and particularly relates to a brushless generator dynamic performance optimization method and system. BACKGROUND

[0002] Brushless motors have been widely used in many fields such as industrial automation, robots, unmanned aerial vehicles, medical devices and the like due to their high efficiency, high power density, long service life and other advantages. With the continuous development of these application fields, higher and higher requirements are put forward for the dynamic performance of brushless motors, such as faster response speed, wider speed regulation range, stronger anti-interference ability and more stable dynamic running state. However, the dynamic performance of existing brushless motors still has many deficiencies in actual application. In terms of response speed, the tracking of the motor to the instruction under the traditional control strategy is obviously lagging, which is difficult to meet the demand of fast dynamic response; in terms of speed regulation range, the high speed area is easily limited by factors such as voltage saturation, and the speed regulation range is limited; in terms of anti-interference ability, the dynamic stability of the motor is poor in the face of load mutation, parameter time-varying and the like, and problems such as overshoot and oscillation are prone to occur. In addition, the existing optimization methods often focus on the improvement of a single link, lack of systematic and collaborative optimization, resulting in unsatisfactory optimization effect. Therefore, a comprehensive and systematic brushless motor dynamic performance optimization method is needed to solve the above problems in the prior art. SUMMARY

[0003] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0004] According to the first aspect of the present application, a brushless generator dynamic performance optimization method is provided, which comprises the following steps:

[0005] S100, a digital modeling is performed on a dynamic model of a brushless generator to obtain a digital model of the brushless generator, and key parameters of the digital model are obtained as initial key parameters; the digital model comprises an electromagnetic model, a mechanical model and a driving system model.

[0006] S200, based on the digital model and the initial key parameters, optimization information for optimizing the control strategy of the brushless generator is generated as initial optimization information, and the optimization information comprises optimization information for optimizing the basic control framework, the control algorithm, the field weakening control and the starting and braking strategy.

[0007] S300, based on the initial optimization information, a digital modeling is performed on the hardware characteristic parameters of the brushless generator to obtain the modeling hardware characteristic parameters as the optimized hardware characteristic parameters.

[0008] S400, based on the optimized hardware characteristic parameters, constructing digital parameters of mechanical structure and heat dissipation performance of the brushless generator, as the optimized mechanical parameters and the optimized heat dissipation parameters respectively.

[0009] S500, constructing a simulation model based on the current reference information, and performing dynamic performance testing through the simulation model; the initial value of the current reference information is the digital model, the initial key parameters, the initial optimization information, the optimized hardware characteristic parameters, the optimized mechanical parameters and the optimized heat dissipation parameters.

[0010] S600, calculating the deviation between the simulation test result and the target performance test result, if the deviation does not meet the preset condition, modifying the current key parameters and the optimization information to update the current reference information, performing S500, if the deviation meets the preset condition, taking the current reference information as the target reference information.

[0011] According to the second aspect of the present application, a brushless generator dynamic performance optimization system is provided, the system comprising:

[0012] A model construction module is configured to digitally model a dynamic model of the brushless generator to obtain a digital model of the brushless generator, and to obtain key parameters of the digital model as initial key parameters; the digital model comprises an electromagnetic model, a mechanical model and a driving system model.

[0013] A first optimization module is configured to generate optimization information for optimizing a control strategy of the brushless generator based on the digital model and the initial key parameters, as initial optimization information, the optimization information comprising optimization information for optimizing a basic control framework, a control algorithm, a field weakening control and a starting and braking strategy.

[0014] A second optimization module is configured to digitally model hardware characteristic parameters of the brushless generator based on the initial optimization information to obtain modeled hardware characteristic parameters as optimized hardware characteristic parameters.

[0015] A third optimization module is configured to construct digital parameters of mechanical structure and heat dissipation performance of the brushless generator based on the optimized hardware characteristic parameters, as the optimized mechanical parameters and the optimized heat dissipation parameters respectively.

[0016] A simulation test module is configured to construct a simulation model based on the current reference information, and to perform dynamic performance testing through the simulation model; the initial value of the current reference information is the digital model, the initial key parameters, the initial optimization information, the optimized hardware characteristic parameters, the optimized mechanical parameters and the optimized heat dissipation parameters.

[0017] The judging module is used for calculating deviation between the simulation test result and the target performance test result, modifying the current key parameter and the optimization information to update the current reference information if the deviation does not satisfy the preset condition, and taking the current reference information as the target reference information if the deviation satisfies the preset condition.

[0018] The present application has at least the following beneficial effects:

[0019] The brushless generator dynamic performance optimization method provided by the embodiment of the present application realizes the collaborative optimization of multi-domain parameters through the whole-process closed-loop system of dynamic model modeling, control strategy optimization, hardware characteristic modeling, mechanical and heat dissipation parameter construction, simulation test and iterative correction.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0022] Figure 1 The flowchart of the brushless generator dynamic performance optimization method provided by the embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0025] It is to be understood that some of the example embodiments are described in terms of a process or method depicted as a flowchart. Although a flowchart can describe operations as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations can be re-arranged. A process can be terminated when its operations are completed, but can also have additional steps not included in a figure. A process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0026] The embodiment of the application provides a brushless generator dynamic performance optimization method, as shown in the figure, the method comprises the following steps: Figure 1 As shown in the figure, the method comprises the following steps:

[0027] S100, a digital modeling is performed on a dynamic model of a brushless generator, a digital model of the brushless generator is obtained, and key parameters of the digital model are acquired as initial key parameters; the digital model comprises an electromagnetic model, a mechanical model and a driving system model.

[0028] S200, based on the digital model and the initial key parameters, optimization information for optimizing a control strategy of the brushless generator is generated as initial optimization information, and the optimization information comprises optimization information for optimizing a basic control framework, a control algorithm, field weakening control and starting and braking strategies.

[0029] S300, based on the initial optimization information, a hardware characteristic parameter of the brushless generator is digitally modeled, and the modeled hardware characteristic parameter is obtained as an optimized hardware characteristic parameter.

[0030] S400, based on the optimized hardware characteristic parameter, a digital parameter of a mechanical structure and heat dissipation performance of the brushless generator is constructed as an optimized mechanical parameter and an optimized heat dissipation parameter respectively.

[0031] S500, a simulation model is constructed based on current reference information, and dynamic performance testing is performed through the simulation model; initial values of the current reference information are the digital model, the initial key parameters, the initial optimization information, the optimized hardware characteristic parameter, the optimized mechanical parameter and the optimized heat dissipation parameter.

[0032] S600, calculate the deviation between the simulation test result and the target performance test result, if the deviation does not satisfy the preset condition, correct the current key parameter and the optimization information to update the current reference information, execute S500, if the deviation satisfies the preset condition, take the current reference information as the target reference information. The initial values of the current key parameter and the optimization information are the initial key parameter and the initial optimization information respectively.

[0033] The brushless generator dynamic performance optimization method provided by the embodiment of the application realizes the collaborative optimization of multi-domain parameters through a full-process closed-loop system of dynamic model modeling, control strategy optimization, hardware characteristic modeling, mechanical and heat dissipation parameter construction, simulation test and iterative correction, can improve the dynamic response speed, control accuracy and system stability of the brushless generator. In addition, through the adaptation and iterative correction mechanism of hardware and control strategy, reliable operation under the conditions of temperature change and load fluctuation is ensured, the research and development cycle is greatly shortened, and the engineering landing efficiency is improved.

[0034] Further, in the embodiment of the application, the electromagnetic model is obtained by the following steps:

[0035] S110, obtaining voltage information and current information of the brushless generator in a three-phase stationary coordinate system as first voltage information and first current information.

[0036] In the embodiment of the application, the voltage and current of the brushless generator in the three-phase stationary coordinate system can be collected by a voltage sensor and a current sensor, and quantified as 16-bit digital signals to obtain the first voltage information and the first current information.

[0037] S111, substituting the first voltage information and the first current information into a Clark transformation matrix respectively to convert the voltage information and the current information in the two-phase stationary coordinate system into second voltage information and second current information.

[0038] Those skilled in the art should understand that the Clark transformation matrix can belong to the prior art category. For example, the second voltage information satisfies the following conditions: wherein u α and u β are voltages in the two-phase stationary coordinate system, and u a , u b and u c are voltages in the three-phase stationary coordinate system.

[0039] S112, obtaining a rotor position angle of the brushless generator and substituting it into a Park transformation matrix to convert the second voltage information and the second current information into voltage information and current information in a two-phase rotating coordinate system as third voltage information and third current information.

[0040] In this embodiment of the invention, the rotor position angle can be obtained by a rotor position sensor.

[0041] Those skilled in the art should understand that the Park transformation matrix falls within the scope of existing technology. For example, the third voltage information satisfies the following condition: Where θ is the rotor position angle, u d Let u be the direct-axis voltage in a two-phase rotating coordinate system. q It is the quadrature-axis voltage in a two-phase rotating coordinate system.

[0042] S113, Based on the third voltage information and the third current information, the electromagnetic model is constructed.

[0043] In this embodiment of the invention, the electromagnetic model includes a voltage equation, a flux linkage equation, and an electromagnetic torque equation. The key parameters of the voltage equation include stator resistance, direct-axis inductance and quadrature-axis inductance in a two-phase rotating coordinate system, electric angular velocity, and permanent magnet flux linkage. The key parameters of the flux linkage equation include direct-axis inductance and quadrature-axis inductance in a two-phase rotating coordinate system and permanent magnet flux linkage. The key parameters of the electromagnetic torque equation include the number of pole pairs, direct-axis flux linkage and quadrature-axis flux linkage in a two-phase rotating coordinate system, and direct-axis current and quadrature-axis current in a two-phase rotating coordinate system.

[0044] The voltage equation satisfies the following condition: .

[0045] Among them, i d Let i be the direct-axis current in a two-phase rotating coordinate system. q Let di be the quadrature-axis current in a two-phase rotating coordinate system. d / d t For i d The time derivative, di q / d t For i q The derivative of R with respect to time s L is the stator resistance of the brushless generator. d L is the direct-axis inductance in a two-phase rotating coordinate system. q Let w be the quadrature-axis inductance in a two-phase rotating coordinate system, and C be the electric angular velocity. f It is a permanent magnet flux linkage.

[0046] The flux linkage equation is: Among them, C d For the direct-axis flux linkage in a two-phase rotating coordinate system, C q It is the cross-axis magnetic flux in a two-phase rotating coordinate system.

[0047] The electromagnetic torque equation is: T e = (3 / 2) × n p ×(C d ×iq -C q ×i d ), where n p It is an extreme logarithm.

[0048] In this embodiment of the invention, the stator resistance can be measured using the DC volt-ampere method. Specifically, with the motor stationary, a DC current (e.g., 0.2 times the rated current) is applied to the stator winding, and the voltage across the winding is measured. The stator resistance is then calculated using Ohm's law. Simultaneously, the winding temperature is collected using a temperature sensor, and a temperature characteristic model R of the stator resistance is established. s =R s0 (1+α△T), where α is the temperature coefficient and △T is the temperature change, to adapt to the resistance changes under different operating conditions.

[0049] The direct-axis and quadrature-axis inductances in a two-phase rotating coordinate system can be obtained using the AC injection method. Specifically, in the two-phase rotating dq coordinate system, a small-signal AC current of a specific frequency (e.g., 1kHz) is injected into the direct axis (d-axis), while keeping the quadrature-axis (q-axis) current at zero. The inductance L is calculated by measuring the phase difference between the direct-axis voltage and current. d Similarly, inject AC current into the q-axis while keeping the d-axis current at 0, and calculate L. q For motors exhibiting magnetic saturation characteristics, repeated measurements at different current amplitudes are required to establish L. d =f(i d L q =f(i q The saturation curve model of f(). f() represents the preset function expression.

[0050] Electrical angular velocity can be measured by position sensors (such as encoders or Hall sensors) to measure the mechanical speed n of the motor rotor (unit: r / min). Then, the electrical angular velocity is calculated based on the relationship between electrical angular velocity and mechanical speed. The data is collected and updated in real time to reflect the dynamic changes in speed.

[0051] The initial value of the permanent magnet flux linkage can be obtained from the permanent magnet datasheet; or it can be measured by the back electromotive force method: when the motor is rotating under no-load, measure the effective value of the back electromotive force E0 of the stator winding, according to E0= (2 1 / 2 / 2)×n p ×C f ×w m Reverse C f Simultaneously, by combining temperature sensor data, C is established. f A temperature-adjusted model, such as C for every 10°C increase in temperature. f The decrease is approximately 2%. The number of pole pairs can be directly obtained from the motor design parameters or nameplate information; it is an inherent structural parameter of the motor, such as a 4-pole motor, where the number of pole pairs equals 2. The direct-axis flux linkage and quadrature-axis flux linkage in a two-phase rotating coordinate system can be derived based on the flux linkage equation.

[0052] Further, in the embodiments of the present application, the mechanical model satisfies the following condition: (T e -T L ) = J x dw m / dt + B x w m ; wherein T e is an electromagnetic torque, T L is a load torque, J is a moment of inertia, B is a friction coefficient, w m is a mechanical angular velocity, and dw m / dt represents a derivative of w m with respect to time.

[0053] Key parameters of the mechanical model can include the moment of inertia and the friction coefficient. The moment of inertia can be obtained by using the no-load deceleration method, specifically including: (1) running the motor in a no-load state to a stable speed w0, then cutting off the power supply, and recording the mechanical angular velocity w m (t) at different times during the process of decelerating from w0 to 0; (2) according to the mechanical model equation (T L = 0, T e = 0) when the load is zero, -B x w m = J x dw m / dt is derived, the influence of the friction coefficient is ignored (friction loss is small during the no-load deceleration stage), the slope dw m / dt of the deceleration curve is fitted, and is calculated, wherein T dec is the equivalent braking torque during deceleration, and Δw m is the change in speed; (3) for the load scenario, the load moment of inertia J load needs to be superimposed, the total moment of inertia J = J motor + J load , and J motor is the motor body moment of inertia.

[0054] In the embodiments of the present application, the friction coefficient can be obtained by using the no-load loss method. Specifically, at different no-load speeds w m , the motor input power P in and the electromagnetic power P e are measured, the friction loss P f = P in - P e ; according to the relationship between the friction loss and the speed P f = B x (w m ) 2 , B is obtained by fitting multiple sets of w m and P f data; for nonlinear friction characteristics, a model B = B0 + k b x wm where B0 is the static friction coefficient, k b is the dynamic friction coefficient, which is fitted by the loss data of low speed and high speed sections.

[0055] Further, in the embodiment of the present application, the drive system model comprises an inverter switch characteristic model and a power device dynamic response model; wherein the key parameters of the inverter switch characteristic model comprise a switching delay time, a rise time, a fall time, a DC bus voltage, an output current and a switching frequency, and the key parameters of the power device dynamic response model comprise an input capacitance and an output capacitance.

[0056] In the embodiment of the present application, the switching delay time, the rise time and the fall time can be obtained by double pulse testing, specifically: a specific voltage is applied across the power device (IGBT / MOSFET), the switching action is triggered, the waveforms of the gate voltage and the drain-source current / voltage are measured by using a high-speed oscilloscope (bandwidth ≥ 100 MHz), and the turn-on delay, the turn-off delay, the rise time of the current / voltage from 10% to 90%, and the fall time of the current / voltage from 90% to 10% are extracted. The turn-on delay is the time from the gate voltage reaching the threshold value to the current starting to rise, and the turn-off delay is the time from the gate voltage dropping to the threshold value to the current starting to fall. The DC bus voltage is collected in real time by a DC voltage sensor (accuracy ≥ 0.5% FS) on the DC side voltage of the inverter, the sampling frequency matches the switching frequency (such as 20 kHz), and the steady-state value and dynamic fluctuation data are obtained. The output current is measured by a Hall current sensor (bandwidth ≥ 10 kHz) on the three-phase current output by the inverter, converted into two-phase static coordinate system current by Clark transformation, and the effective value is extracted as the output current parameter for switching loss calculation. The switching frequency is set according to the inverter control strategy, and is verified by measuring the switching period of the power device, such as 50 μs for a 20 kHz switching frequency, which can be obtained by measuring the period of the gate drive signal by the oscilloscope. The input capacitance and the output capacitance can be obtained from the power device data manual, such as 1500 pF for the input capacitance and 300 pF for the output capacitance of IGBT (Insulated Gate Bipolar Transistor), or measured by an impedance analyzer under specific test conditions (such as 200 V drain-source voltage and 1 kHz frequency) to establish a capacitance-voltage correlation model (the output capacitance decreases with the increase of the drain-source voltage).

[0057] Further, in S200, the optimization of the basic control framework includes optimization of vector control and direct torque control; the optimization of the control algorithm includes optimization of model predictive control, sliding mode control and adaptive control; the optimization of the field weakening control includes optimization of voltage closed-loop monitoring and dynamic adjustment strategy of negative current injection in the two-phase rotating coordinate system; the optimization of the starting and braking strategy includes optimization of three-stage starting and energy feedback braking.

[0058] Further, the optimization of the vector control includes:

[0059] (1) Implementing Clark / Park transformation through a hardware acceleration module, shortening the transformation delay from P1 clock cycles to P2 clock cycles.

[0060] In the embodiment of the present application, P1=8 and P2=2.

[0061] (2) Using a fixed-point algorithm to make the calculation error of the Clark / Park transformation less than a set error threshold.

[0062] Specifically, the fixed-point algorithm includes: quantizing the three-phase current / voltage sampling values into 16-bit integers, and replacing floating-point operations with table lookup and linear interpolation to calculate sine / cosine values. A set of sine (sin) and cosine (cos) values calculated at fixed angle intervals (e.g. 0.001°) are stored in memory in advance to form a "angle-trigonometric function value" correspondence table. For example, the sin and cos values corresponding to 0°, 0.001°, 0.002°, …, 360° are stored, with each angle interval being only 0.001°, ensuring that the accuracy of the data in the table is high enough. When the sin value / cos value of a certain angle needs to be calculated, it is not necessary to calculate it in real time through a floating-point formula (such as Taylor series), but it can be directly looked up from the table, which can greatly reduce the amount of calculation.

[0063] In the embodiment of the present application, the set error threshold is 0.1%.

[0064] (3) Optimizing dynamic response through double-loop PI parameter tuning.

[0065] Specifically, the double-loop PI parameter tuning includes: applying a 50% rated current step command to the current loop, tuning to have no overshoot and a steady-state error less than 1% and a bandwidth of up to 2kHz; applying a 20% rated speed step command to the speed loop, tuning to have an overshoot less than 10% and a response lag less than 50ms, and a bandwidth of up to 250Hz.

[0066] Further, the optimization of the direct torque control includes:

[0067] The torque hysteresis bandwidth and the flux hysteresis bandwidth are dynamically adjusted, and the torque / flux change rate table corresponding to the pre-stored q basic voltage vectors is combined with parallel comparison logic to replace serial judgment, so that the vector selection is delayed to P3 clock cycles.

[0068] The torque hysteresis bandwidth and the flux hysteresis bandwidth are dynamically adjusted, and the torque / flux change rate table corresponding to the pre-stored q basic voltage vectors is combined with parallel comparison logic to replace serial judgment, so that the vector selection is delayed to P3 clock cycles.

[0069] In the embodiment of the application, q=8 and P3=1.

[0070] Further, the optimization of the model predictive control includes: designing a cost function including torque tracking, current constraint, and voltage stability, and performing rolling optimization every first set time, for example, 50, to select the optimal voltage vector.

[0071] The cost function can be: CS=λ T ×|Tm e -T e (k+1)|+λ i ×i m d (k+1) 2 +i m q (k+1) 2 +λ u ×|u dc -u ref |, wherein CS is the cost function of the model predictive control, used to evaluate the pros and cons of the candidate voltage vector, and the smaller the value is, the better the vector is. λ T is a weight coefficient of the torque tracking term, used to adjust the priority of the electromagnetic torque tracking accuracy. Tm e is an electromagnetic torque instruction value, T e (k+1)is a predicted electromagnetic torque value at k+1 time. i m d (k+1)is a predicted direct-axis current instruction value at k+1 time, i m q (k+1)is a predicted quadrature-axis current instruction value at k+1 time. λ i is a weight coefficient of the current constraint term, used to limit the current amplitude to avoid power device overcurrent. λ u is a weight coefficient of the voltage stability term, used to constrain the voltage fluctuation to ensure the stability of the DC bus. u dc is a measured value of the DC bus voltage. u ref is a reference value of the DC bus voltage.

[0072] The first preset time is 50 μs, that is, 1 PWM period. The rolling optimization includes: predicting future 2 period states based on the current current and speed, and traversing 8 voltage vectors to select the vector that minimizes J.

[0073] In the embodiment of the application, the optimization of the sliding mode control includes: designing a speed sliding mode surface and a current sliding mode surface; adopting an exponential reaching law, and suppressing chattering through a low-pass filter, and specifically, the chattering can be suppressed through a low-pass filter with a cutoff frequency of 4 kHz.

[0074] In the embodiment of the application, the optimization of the adaptive control includes: identifying the stator resistance and the moment of inertia in real time by using a recursive least square method; and dynamically adjusting PI parameters to adapt to parameter time variation.

[0075] The identification period can be 1 ms. The dynamic adjustment of the PI parameters can include: when the stator resistance increases by 10%, the current loop is increased by 8%; and when the moment of inertia increases by 15%, the speed loop is increased by 12%.

[0076] In the embodiment of the application, the optimization of the voltage closed-loop monitoring includes: calculating the voltage amplitude in real time; comparing the calculated voltage amplitude with a voltage threshold value, and triggering or exiting the field weakening control based on the comparison result.

[0077] If the voltage amplitude is greater than or equal to the voltage threshold value, the field weakening control is triggered, and when the voltage amplitude is less than or equal to 0.8 times the voltage threshold value, the field weakening control is exited.

[0078] In the embodiment of the application, the optimization of the negative current dynamic adjustment strategy of the direct-axis injection in the two-phase rotating coordinate system includes: setting an initial instruction, for example, setting it to -0.1×i N , i N is the rated current; adjusting the negative current every second set time, for example, 100 μs, according to the voltage deviation, increasing the absolute value of the negative current if the voltage deviation is greater than 5 V, and reducing the absolute value of the negative current if the voltage deviation is less than -5 V; and limiting the negative current in a set current range to avoid demagnetization of the magnetic steel, and the set current range can be [-0.3×i N , 0].

[0079] Further, the optimization of the three-stage starting includes: pre-positioning, open-loop acceleration and closed-loop switching; wherein, the pre-positioning includes inputting a preset current value of direct current to the A phase and the B phase, and positioning the rotor to 0° electrical angle for a preset time to ensure that the positioning error is less than a preset positioning error threshold; the open-loop acceleration includes: using voltage frequency ratio control to linearly increase from a first preset rotating speed to a second preset rotating speed at a set rotating speed change rate, and correcting the voltage output every third set time to avoid step loss; the closed-loop switching includes: when the rotating speed reaches the second preset rotating speed and is stable for a fourth set time, switching to vector control, freezing the current loop integral term and smoothing the transition instruction to ensure that the impact current is less than a set impact current threshold.

[0080] wherein, the preset current value is 0.2×i N The preset time is 200 ms. The preset positioning error threshold is 2°. The first preset rotating speed is 50 rpm, the set rotating speed change rate is 500 rpm / s, and the second preset rotating speed is 300 rpm. The third set time is 5 ms. The set impact current threshold is 1.2×i N .

[0081] Further, the optimization of the energy feedback braking includes: storing energy by a capacitor with a capacity greater than a set capacitor threshold; rectifying alternating current into direct current by IGBT during braking, and charging through a DC / DC converter, wherein the charging voltage is less than or equal to 1.1 times the direct current bus voltage; calculating the required braking torque according to the target braking time, and adjusting the cross-axis alternating current instruction in the two-phase rotating coordinate system to ensure that the rotating speed decreases at a preset acceleration.

[0082] In the embodiment of the application, the set capacitor threshold is 100 F. The preset acceleration is 5000 rpm / s.

[0083] Further, in the embodiment of the application, the hardware characteristic parameters include power device hardware characteristic parameters, motor body hardware characteristic parameters, sensor and drive circuit hardware characteristic parameters, and heat dissipation system hardware characteristic parameters; wherein, the power device hardware characteristic parameters include switching rise time, fall time, on-resistance, input capacitance, output capacitance, reverse recovery time and withstand voltage parameters; the motor body hardware characteristic parameters include stator resistance, direct-axis inductance and cross-axis inductance in the two-phase rotating coordinate system, permanent magnet flux linkage, rotational inertia and friction coefficient; the sensor and drive circuit hardware characteristic parameters include linear error, bandwidth and zero drift of the current sensor, resolution and delay time of the position sensor, gate drive resistance and direct current bus capacitance; the heat dissipation system hardware characteristic parameters include junction-to-case thermal resistance of the power device, case-to-environment thermal resistance, thermal resistance from the motor stator winding to the case, and heat dissipation structure parameters.

[0084] In the embodiments of the present application, the optimized hardware characteristic parameters can be obtained through experimental measurement, simulation calibration and adaptability correction of the control strategy.

[0085] Further, in the embodiments of the present application, for the power devices such as IGBT / MOSFET in the inverter, based on the switching frequency, current limit and switching loss model in the initial optimization information, the following modeling is performed:

[0086] Key parameter acquisition and modeling:

[0087] Obtain initial parameters from the power device data manual: switching rise time, fall time, on-resistance, input capacitance, output capacitance, reverse recovery time;

[0088] Measure the actual rise time and actual fall time under different voltages and currents through double pulse experiments, establish a fitting model between the rise time and the voltage and current, and establish a fitting model between the fall time and the voltage and current;

[0089] Combine the switching frequency optimization range (20-30 kHz) in the initial optimization to correct the parasitic capacitance model: when the switching frequency is increased, adjust the influence weight of the output capacitance on the switching loss through the frequency characteristic curve of the output capacitance.

[0090] Optimization correction:

[0091] Based on the voltage constraint term of the MPC cost function in the initial optimization, limit the voltage withstanding parameter model of the power device, so that 1.2 times of the voltage is less than or equal to the maximum voltage, and ensure the threshold requirement of the hardware adaptive voltage closed-loop control.

[0092] Further, for the stator, rotor and mechanical structure of the brushless generator, the initial optimization information of the electromagnetic model and the mechanical model can be combined to model the electromagnetic parameters and the mechanical parameters, wherein:

[0093] The electromagnetic parameter modeling includes:

[0094] Stator resistance R s : Measure R s at different temperatures by direct current volt-ampere method, establish a temperature characteristic model of R s =R s0 (1+α△T), and dynamically correct it combined with the resistance identification result of the adaptive control in the initial optimization;

[0095] Dq-axis inductance L d / L q : Obtain the inductance saturation curve under different currents by finite element simulation, and correct i d range (-0.3i N to 0) of the field weakening control in the initial optimization.d L d model;

[0096] Permanent magnet flux linkage C f : Based on the demagnetization curve of the permanent magnet (data manual), the correlation model between the permanent magnet flux linkage and the temperature and the direct-axis current is established, and the negative i d range in the initial optimization is ensured C f is not less than 80% of the rated value (to avoid demagnetization).

[0097] Mechanical parameter modeling includes:

[0098] Moment of inertia: The moment of inertia under different loads is measured through the no-load deceleration experiment, and the correlation model between the moment of inertia and the load torque is established to adapt the acceleration limit of the start-up acceleration strategy in the initial optimization;

[0099] Friction coefficient: The friction loss under different rotational speeds is measured, and a nonlinear model between the friction coefficient and the mechanical angular velocity is established to correct the mechanical motion equation to match the torque calculation of the braking strategy in the initial optimization.

[0100] Further, for the current, position sensor and drive circuit, the sensor parameters and drive circuit parameters are modeled in combination with the feedback requirements of the control strategy. Among them:

[0101] Sensor parameter modeling includes:

[0102] Current sensor: Measure its linear error (±0.5%FS), bandwidth (≥10kHz), and establish a current error model i meas =i true (1+δ)+ε, combined with the steady-state error requirement (<1%) of the current loop PI parameter setting in the initial optimization, set the error compensation coefficient, wherein i meas is the current measurement value, i true is the true value of the current, δ is the proportional error, and ε is the zero drift;

[0103] Position sensor (such as encoder): Get resolution (such as 16384 lines) and delay time, and establish a rotor position angle error model θ meas =θ true +θ error (t), combined with the delay correction of Clark / Park transformation in the initial optimization information, compensate for the position feedback lag. θ meas is the rotor position angle measurement value, θ true is the true value of the rotor position angle, and θ error (t) is the rotor position angle error at time t.

[0104] Drive circuit parameter modeling includes:

[0105] gate drive resistance R g : measure the switching time under different R g , establish model t r =f(R g , U gs ) and t f =f(R g , U gs ), combined with the switching frequency (20-30 kHz) in the initial optimization, select the optimal R g to balance the switching loss and electromagnetic interference; where t r is the rise time, t f is the fall time, U gs is the gate-source voltage of the power device such as IGBT or MOSFET.

[0106] DC bus capacitor C dc : based on the ripple voltage requirement (△U dc ≤5%), establish the model C dc =f(Io, f s ), adapt the requirement of capacitor energy storage (100F) in the initial optimization of energy feedback braking. Io is the output current, f s is the switching frequency.

[0107] For the heat dissipation structure of power devices and motors, combined with the loss calculation in the initial optimization information, modeling:

[0108] Power device thermal model: establish the junction temperature model T j =T a +P toatl ×R th（j-a） , where T j is the junction temperature of the power device (such as IGBT, MOSFET), which is the temperature of the semiconductor chip inside the device, and is the core indicator of measuring device thermal stress, which needs to be controlled within the rated junction temperature to avoid damage. T a is the ambient temperature, P toatl is the total loss, P toatl =P sw +P cond , P sw is the switching loss, which comes from the switching loss model in the initial optimization, P cond is the conduction loss, R th（j-a） is the total thermal resistance, which reflects the resistance of heat transfer from the chip (junction) to the external environment, with the unit of ℃ / W, its value needs to be corrected through heat dissipation simulation to match the actual heat dissipation conditions, through R th（j-a） to ensure T j ≤125℃.

[0109] Motor heat dissipation model: establish the stator winding temperature Twinding = T a + (i d 2 + i q 2 ) x R s x R th（w-a） The model, combined with the current limit in the initial optimization, ensures that the winding temperature does not exceed 155℃ (insulation level). Wherein, T winding is the stator winding temperature, and R th（w-a） is the thermal resistance of the stator winding to the environment.

[0110] In the embodiment of the application, through hardware-in-the-loop (HIL) simulation and experimental test, the hardware characteristic parameters of the digital modeling are substituted into the control strategy model, including: comparing the switching loss, torque response, speed fluctuation and other indicators of simulation and experiment, if the error is > 5%, iteratively correct the parameter model (such as adjusting the fitting coefficient of t r , t f or L d ); and based on the dynamic performance indicators (such as current loop bandwidth 2kHz, speed overshoot <10%) in the initial optimization, the adaptability of the hardware characteristic parameter model is verified, and finally the optimized hardware characteristic parameters meeting the control accuracy and reliability requirements are output.

[0111] Further, the mechanical parameters include rotor structure parameters, stator structure parameters, bearing and transmission structure parameters and load adaptation parameters, and the heat dissipation parameters include motor body heat dissipation parameters, power device heat dissipation parameters and overall heat dissipation path parameters.

[0112] Further, S400 can specifically include:

[0113] S410, based on the moment of inertia J, friction coefficient B and mechanical motion characteristics in the optimized hardware characteristic parameters, refining the digital parameter model of the mechanical structure.

[0114] Wherein, the rotor structure parameters can be obtained by the following steps: establishing a digital model of the rotor diameter D r , length L r , permanent magnet thickness h m , and through J= (1 / 2) x m r x D r 2 correlate the moment of inertia to ensure that the load correlation model of J in the optimized hardware characteristic parameters matches; wherein, m r is the mass of the shaft.

[0115] The stator structure parameters can be obtained by the following steps: establishing a digital model of the stator core length, tooth width, and yoke height, combining the density and stacking factor of the core material (silicon steel sheet), and calculating the influence of the stator part on the overall moment of inertia (occupies ≤10%).

[0116] The bearing and transmission structure parameters can be obtained by the following steps:

[0117] A bearing friction coefficient model is constructed: based on the bearing type (such as deep groove ball bearing 6205), a digital relationship between the friction coefficient and the rotational speed and axial load is established, the friction loss at different rotational speeds is measured by experiment, B is modified, and the rotational speed nonlinear model of B in the optimization of hardware characteristic parameters is adapted; the correction formula is B=B0+k b n, n is the rotational speed.

[0118] Obtain the shaft stiffness parameters: establish the digital model of shaft diameter d s , span L s and torsional stiffness K t K t = (πd s 4 G) / 32L s , ensure that the torsional deformation of the shaft system under the rated torque is ≤0.1°, avoid affecting the rotor position accuracy, and G is the shear modulus.

[0119] The load adaptation parameters can be obtained by the following steps:

[0120] Obtain the shaft coupling moment of inertia: based on the shaft coupling type (such as elastic coupling), establish its moment of inertia model J c =f (d c , L c ), include the total moment of inertia J total =J motor +J c +J load , adapt the correlation model of J and load torque in the optimization of hardware characteristics; wherein, d c is the diameter of the shaft coupling, and L c is the length.

[0121] Obtain the mechanical gap parameters: quantify the digital model of the stator and rotor air gap g (g=0.3~0.5mm), through the constraint of air gap unevenness △g / g≤5%, avoid L d / L q fluctuation caused by mechanical deviation, and correlate with the inductance saturation curve model in the optimization of hardware characteristic parameters.

[0122] S420, based on the thermal resistance, heat dissipation loss and temperature characteristics in the optimization of hardware characteristic parameters, a digital parameter model of heat dissipation performance is constructed.

[0123] The heat dissipation parameters of the motor body are obtained through the following steps:

[0124] Obtain stator winding heat dissipation parameters: Establish the cross-sectional area A of the winding conductor. w Thermal conductivity λ of the insulation layer ins The digital model, through R th，winding =L s / (A w ×λ ins Calculate the thermal resistance R from the stator winding to the core. th，winding The stator winding temperature model in the correlation optimization hardware characteristics.

[0125] Obtaining the rotor heat dissipation coefficient: based on the thermal conductivity λ of the permanent magnet. m Establish the thermal resistance R from the rotor to the housing. th，rotor =g / (A g ×λ m ), g is the air gap, A g (for air gap heat dissipation area), ensuring that the rotor temperature is less than or equal to 120℃ to avoid demagnetization of permanent magnets.

[0126] The heat dissipation parameters of power devices are obtained through the following steps:

[0127] Obtain heatsink structure parameters: Construct heatsink area A fin Thickness t fin Spacing s fin The digital model, through R th，fin =1 / h×A fin Junction temperature T of associated power devices j To adapt and optimize the junction temperature model in the hardware characteristic parameters, where R th，fin denoted as , where is the thermal resistance of the heat sink, and h is the convective heat dissipation coefficient.

[0128] Obtain cooling fan parameters: Establish fan speed n f With air volume Q f Digital Relationship Q f =k f ×n f via Q f ≥1.2Q req Constraints are imposed to ensure that power device losses are effectively dissipated, and this is correlated with the switching loss model in the optimized hardware. Where k f Q is the air volume coefficient. req The airflow required for heat dissipation.

[0129] The overall heat dissipation path parameters can be obtained through the following steps:

[0130] Obtain chassis heat dissipation parameters: Establish chassis thickness tc and material thermal conductivity λ cdigital model of the computer case, computer case thermal resistance, ensuring that the computer case surface temperature is less than or equal to 60℃ (ambient temperature T a =25℃);

[0131] Obtain thermal coupling parameters: build a thermal resistance network model R th，Total =R th，j-case +R th，casing +R th，ambient of the power device-case-environment, through which the contribution of each heat dissipation link to the total temperature difference is quantified. Among them, R th，j-case is the thermal resistance of the device to the case (taken from the power device data manual in the optimized hardware characteristics). R th，casing is the thermal resistance of the case to the heat dissipation structure (such as the heat sink), reflecting the resistance of heat transfer from the device shell to the external heat dissipation structure, which is related to the case material, contact area and heat conducting medium (such as heat conducting silicone grease). R th，ambient is the thermal resistance of the heat dissipation structure to the environment, which refers to the resistance of heat transfer from the heat dissipation structure (such as the heat sink) to the surrounding environment, which is related to the convective heat transfer coefficient, radiation characteristics of the heat dissipation structure and environmental temperature, etc.

[0132] Further, in S500, MATLAB / Simulink and AnsysMaxwell are used as the core simulation platform to integrate electromagnetic, mechanical, drive and heat dissipation sub-models to realize multi-physical field coupling simulation, which specifically includes:

[0133] S510, integrate electromagnetic sub-model, mechanical sub-model, drive system sub-model and heat dissipation sub-model.

[0134] Among them, the electromagnetic sub-model is obtained by embedding the voltage equation and torque equation based on the dq coordinate system, and the parameters are the optimized stator resistance, direct-axis inductance, cross-axis inductance and temperature characteristic model. The mechanical sub-model is constructed based on the motion equation, integrating the rotational inertia J (including the load correlation model), friction coefficient B (including the rotational speed nonlinear model) and shaft torsional stiffness parameters in the optimized mechanical parameters. The drive system sub-model includes the switching characteristics of the inverter and the dynamic response of the power device, and the switching characteristics include the switching loss model based on P sw =(1 / 2)×u dc I o ×f s (t r +t f ). The dynamic response is the equivalent circuit model of IGBT / MOSFET, and the parameters are the optimized rise time, fall time, input capacitance and output capacitance. The heat dissipation sub-model is a thermal resistance network of the attack power device-motor body-environment, which inputs the loss parameters and outputs the temperature of each node, and the parameters are R th（j-c） , R th（w-a） and the heat dissipation structure parameters in the optimized heat dissipation parameters.

[0135] Wherein, the sub-model parameter linkage is realized through the data interface, for example: the current output of the electromagnetic sub-model is taken as the input of the switching loss of the driving system sub-model; the rotating speed output of the mechanical sub-model is taken as the back electromotive force input of the electromagnetic sub-model; the temperature output of the heat dissipation sub-model is taken as the feedback correction of the stator resistance of the electromagnetic sub-model.

[0136] S520, based on the constructed simulation model, a multi-dimensional test scheme is designed to verify the effectiveness of the optimization strategy.

[0137] The specific test items and methods are as follows:

[0138] 1. Start-up performance test

[0139] Test method: input three-stage start-up instruction (pre-positioning→open-loop acceleration→closed-loop switching) in the simulation model, and monitor the current, rotating speed and positioning error curves during the start-up process;

[0140] Evaluation index: start-up impact current≤1.2i N (the same as the optimization target), positioning error≤2°, and time from start-up to stable operation≤500ms;

[0141] Simulation verification: compare the start-up curves under different moments of inertia J to verify the start-up acceleration and J adaptability, for example, when J increases, the open-loop acceleration automatically decreases to 300rpm / s.

[0142] 2. Speed regulation performance test

[0143] Test method: set the rotating speed command to step from 500rpm to 1500rpm (covering the base speed range), and then step to 2400rpm (field weakening range), and record the rotating speed response, current waveform and voltage amplitude;

[0144] Evaluation index: rotating speed overshoot≤10%, response lag≤50ms (matching the speed loop optimization target), and rotating speed fluctuation in the field weakening stage≤2%;

[0145] Simulation verification: through the linkage of the electromagnetic sub-model and the field weakening control sub-model, verify the dynamic adjustment of the rotating speed from 1500rpm to 2200rpm when i d from -0.1i d to -0.2i N . N .

[0146] 3. Load disturbance response test

[0147] Test method: at the rated rotating speed (1500rpm), suddenly apply 50% rated load, and monitor the rotating speed recovery time, current fluctuation and torque response;

[0148] Evaluation index: speed drop ≤5%, recovery time ≤100ms, current fluctuation ≤1.2i N ;

[0149] Simulation verification: through the J and B parameters of the mechanical sub-model, verify the effect of adaptive adjustment of the speed loop PI parameters, such as when J increases by 15%, the speed loop increases by 12%, and the recovery time is shortened to 80ms.

[0150] 4. Weak magnetic speed expansion performance test

[0151] Test method: set the speed to gradually increase from the base speed (1500rpm) to 1.6 times the base speed (2400rpm), record the back EMF, i d Adjustment curve and DC bus voltage;

[0152] Evaluation index: back EMF less than or equal to 0.9u dc / (3) 1 / 3 , i d In the range of -0.3i N to 0, the speed stable fluctuation is less than or equal to 2%;

[0153] Simulation verification: through the u dc parameters of the drive system sub-model and the back EMF calculation of the electromagnetic sub-model, verify the effectiveness of the negative i d adjustment strategy (such as u dc =310V), the back EMF is stable within 250V).

[0154] 5. Braking performance test

[0155] Test method: trigger the energy feedback braking instruction from the rated speed (1500rpm), monitor the braking time, current peak, bus voltage and capacitor energy storage;

[0156] Evaluation index: braking time ≤50ms, impact current ≤2i N , bus voltage ≤1.1Udc, speed drop acceleration ≤5000rpm / s;

[0157] Simulation verification: through the J and braking torque formula of the mechanical sub-model, verify the control effect of i q instruction adjustment on the braking process.

[0158] 6. Heat dissipation stability test

[0159] Test method: continuously run for 30 minutes under rated load, monitor the power device junction temperature T j , winding temperature T w and heat sink temperature;

[0160] Evaluation index: Tj ≤125℃, T w ≤155℃, temperature fluctuation ≤5℃;

[0161] Simulation verification: through the thermal resistance network of the heat dissipation sub-model, verify the effect of heat dissipation parameters (such as 100F super capacitor, heat dissipation fin area) on temperature control (such as P sw =50W, T j Stable at 110℃).

[0162] S530, output of simulation results and iteration.

[0163] After the test is completed, the simulation curves of each performance index (such as speed-time curve, current-torque curve, temperature-time curve) are output, and compared with the optimization target: if a certain index does not meet the standard (such as starting impact current =1.3i N ), back to S200 (control strategy optimization) or S400 (parameter modeling) for correction until the simulation results meet all dynamic performance requirements, forming a closed-loop optimization of “modeling-test-iteration”.

[0164] Further, in the embodiment of the application, the deviation between the simulation test result and the target performance test result includes: starting performance deviation, speed regulation performance deviation, load disturbance response deviation, and braking and heat dissipation performance deviation, wherein the starting performance deviation includes starting impact current deviation and positioning error deviation, the speed regulation performance deviation includes speed overshoot deviation and field weakening speed rotation deviation, the load disturbance response deviation includes speed recovery time deviation and current fluctuation deviation, and the braking and heat dissipation performance deviation includes braking time deviation and junction temperature deviation; wherein the starting impact current deviation, the speed overshoot deviation, the field weakening speed rotation deviation and the braking time deviation belong to the first type of deviation, and the positioning error deviation, the speed recovery time deviation, the current fluctuation deviation and the junction temperature deviation belong to the second type of deviation.

[0165] Wherein the starting performance deviation, the speed overshoot deviation, the field weakening speed rotation deviation, the current fluctuation deviation and the braking time deviation are equal to the ratio of the absolute value of the difference between the corresponding simulation test value and the target value and the target value, for example, the starting performance deviation is equal to the ratio of the absolute value of the difference between the simulation impact current peak value and the target impact current peak value and the target impact current peak value. The positioning error deviation, the speed recovery time deviation, the braking time deviation and the junction temperature deviation are equal to the absolute value of the difference between the corresponding simulation test value and the target value, for example, the positioning error deviation is equal to the absolute value of the difference between the simulation position error and the target position error.

[0166] In the embodiment of the present application, the target performance test result is a benchmark index for measuring whether the performance of a system (such as a motor drive system) in key links such as starting, speed regulation, load disturbance response, braking and heat dissipation meets the standard, including specific indexes such as starting performance (starting impact current peak value, positioning error), speed regulation performance (speed overshoot, field weakening speed range), load disturbance response (speed recovery time, current fluctuation amplitude), braking and heat dissipation performance (braking time, junction temperature steady-state value), and comprehensively reflects the dynamic and static characteristics of the system. The target performance test result can be set based on experience value.

[0167] Covering multi-dimensional performance parameters: including specific indexes such as starting performance (starting impact current peak value, positioning error), speed regulation performance (speed overshoot, field weakening speed range), load disturbance response (speed recovery time, current fluctuation amplitude), braking and heat dissipation performance (braking time, junction temperature steady-state value), and comprehensively reflecting the dynamic and static characteristics of the system.

[0168] Further, in S600, if any deviation in the first type of deviation is greater than the first deviation threshold, or any deviation in the second type of deviation is greater than the second deviation threshold, it is determined that the deviation does not meet the preset condition, and if all deviations in the first type of deviation are less than or equal to the first deviation threshold, and all deviations in the second type of deviation are less than or equal to the second deviation threshold, it is determined that the deviation meets the preset condition, and the first deviation threshold is less than the second deviation threshold.

[0169] In the embodiment of the present application, the first deviation threshold can be 5%, and the second deviation threshold can be 10%.

[0170] In the embodiment of the present application, if the deviation does not meet the preset condition, the key parameters and optimization information are corrected based on the deviation direction and amplitude, as follows:

[0171] 1. Key parameter correction

[0172] Electromagnetic parameter correction:

[0173] If the speed overshoot deviation is too large (such as simulation overshoot 12%, target 10%), the L d / L q saturation curve is corrected in combination with the deviation: if the overshoot is caused by too low inductance value leading to too fast response, the fitting coefficient of L d / L q is increased (such as increased by 5%), and the flux linkage stability is enhanced;

[0174] If the current loop steady-state error deviation is out of range (such as simulation 2%, target 1%), the temperature model of the stator resistance is corrected: if the deviation is caused by underestimation of the stator resistance at low temperature, the temperature coefficient is adjusted (such as from 0.0039 / ℃ to 0.0041 / ℃).

[0175] Mechanical parameter correction: If the deviation of the recovery time of the speed after load disturbance exceeds the range (e.g., simulation 120 ms, target 100 ms), the load-related model of the moment of inertia J is corrected: if the recovery is too slow due to overestimation of J, the load-related coefficient is reduced (e.g., from 1.2 to 1.1) to match the actual inertia characteristics; if the friction coefficient causes the low-speed fluctuation deviation to exceed the range, the dynamic friction coefficient is adjusted (e.g., from 0.002 to 0.0018) to weaken the sensitivity of the speed to friction.

[0176] Hardware characteristic parameter correction: If the deviation of the switching loss simulation value and the target exceeds 10%, the t r off of the power device is corrected: if the loss is too high due to the overestimation of t f off, the t r off is corrected from 120 ns to 105 ns based on the deviation; r

[0177] If the deviation of the DC bus voltage fluctuation exceeds the range, the capacity model of the bus capacitor is adjusted: if the fluctuation is due to the underestimation of the bus capacitor, the set value is increased (e.g., from 1000 μF to 1200 μF).

[0178] 2. Optimization information correction

[0179] If the speed loop response lag deviation exceeds the range (e.g., simulation 60 ms, target 50 ms), the PI parameters are adjusted: based on the deviation of 10 ms, the speed loop proportional coefficient is increased (e.g., from 5.0 to 5.5) to speed up the response;

[0180] If the flux-weakening speed fluctuation deviation exceeds the range (e.g., simulation 3%, target 2%), the adjustment step of the negative i d is reduced from 0.02i N to 0.015i N to reduce the adjustment amplitude to smooth the speed.

[0181] Start / braking strategy correction

[0182] If the starting impact current deviation exceeds the range (e.g., simulation 1.3i N , target 1.2i N ), the closed-loop switching transition time is extended (e.g., from 5 ms to 7 ms) to reduce the current mutation rate;

[0183] If the braking acceleration deviation exceeds the range (e.g., simulation 6000 rpm / s, target 5000 rpm / s), the adjustment amplitude of the quadrature axis current is reduced (e.g., from 0.05i N to 0.03i N ).

[0184] ​3. After updating the reference information and iteration correction, the adjusted key parameters (electromagnetic, mechanical, hardware) and optimization information (control strategy, start / brake parameters) are integrated into the "updated reference information", and returned to S500 to rebuild the simulation model and perform dynamic performance test, repeat the deviation calculation and judgment of S600, until the deviation falls within the preset range.

[0185] In the embodiment of the application, the target reference information can include the optimized control strategy, the calibrated hardware characteristic parameter, the matched mechanical parameter and the verified heat dissipation parameter. The target reference information serves as a benchmark for the design, production and debugging of the brushless generator, ensuring that the actual running performance is consistent with the simulation target.

[0186] Based on the same inventive concept, the embodiment of the application provides a brushless generator dynamic performance optimization system, which comprises:

[0187] A model construction module is configured to digitally model a dynamic model of the brushless generator to obtain a digital model of the brushless generator, and obtain key parameters of the digital model as initial key parameters; the digital model comprises an electromagnetic model, a mechanical model and a driving system model.

[0188] A first optimization module is configured to generate optimization information for optimizing a control strategy of the brushless generator based on the digital model and the initial key parameters, as initial optimization information; the optimization information comprises optimization information for optimizing a basic control framework, a control algorithm, a field weakening control and a start and brake strategy.

[0189] A second optimization module is configured to digitally model hardware characteristic parameters of the brushless generator based on the initial optimization information to obtain modeled hardware characteristic parameters as optimized hardware characteristic parameters.

[0190] A third optimization module is configured to construct digital parameters of mechanical structures and heat dissipation performance of the brushless generator based on the optimized hardware characteristic parameters, as optimized mechanical parameters and optimized heat dissipation parameters respectively.

[0191] A simulation test module is configured to construct a simulation model based on current reference information, and perform dynamic performance test through the simulation model; the initial value of the current reference information is the digital model, the initial key parameters, the initial optimization information, the optimized hardware characteristic parameters, the optimized mechanical parameters and the optimized heat dissipation parameters.

[0192] A judgment module is configured to calculate the deviation between the simulation test result and the target performance test result, if the deviation does not meet the preset condition, correct the current key parameters and optimization information to update the current reference information, if the deviation meets the preset condition, take the current reference information as the target reference information.

[0193] The system can be used to perform Figure 1 The method shown in the embodiment shown, thus, for the function modules of the system can be achieved by the function, etc. can refer to Figure 1 The description of the embodiment shown in the embodiment, not much superfluous.

[0194] The embodiment of the application also provides an electronic device, comprising: at least one processor; and, memory connected with the at least one processor in communication; wherein, the memory stores instructions executable by the at least one processor, the instructions are set to execute the method described in the embodiment of the application.

[0195] The embodiment of the application also provides a computer readable storage medium, stores computer executable instructions, the computer instructions are used to execute the method described in the embodiment of the application.

[0196] It should be understood that the various forms of flow shown above can be reordered, added or deleted steps. For example, each step described in the application can be executed in parallel, can be executed sequentially, can be executed in different order, as long as the desired results of the technical solution disclosed in the application can be achieved, this article does not limit.

[0197] The above specific embodiments, do not constitute a limitation on the scope of protection of the application. Those skilled in the art should understand that, according to the design requirements and other factors, various modifications, combinations, sub combinations and alternatives can be made. Any modification, equivalent replacement and improvement within the spirit and principles of the application shall be included in the scope of protection of the application.

Claims

1. A method for optimizing the dynamic performance of a brushless generator, characterized in that, The method includes the following steps: S100, digital modeling is performed on the dynamic model of the brushless generator to obtain a digital model of the brushless generator, and the key parameters of the digital model are obtained as initial key parameters; the digital model includes an electromagnetic model, a mechanical model, and a drive system model; the mechanical model satisfies the following condition: (T e -T L =J×dw m / dt+B×w m Among them, T e For electromagnetic torque, T L Where J is the load torque, B is the moment of inertia, and W is the coefficient of friction. m For mechanical angular velocity, dw m / dt means w m The derivative with respect to time; the drive system model includes an inverter switching characteristic model and a power device dynamic response model; wherein, the key parameters of the inverter switching characteristic model include switching delay time, rise time, fall time, DC bus voltage, output current and switching frequency, and the key parameters of the power device dynamic response model include input capacitance and output capacitance; S200, based on the digital model and initial key parameters, generates optimization information to optimize the control strategy of the brushless generator. As initial optimization information, the optimization information includes optimization information for the basic control framework, control algorithm, field weakening control, and starting and braking strategies. Among them, the optimization of the control algorithm includes the optimization of adaptive control, which uses the recursive least squares method to identify the stator resistance and moment of inertia in real time. S300, based on the initial optimization information, digitally model the hardware characteristic parameters of the brushless generator to obtain the modeled hardware characteristic parameters, which are used as optimized hardware characteristic parameters; the hardware characteristic parameters include power device hardware characteristic parameters, motor body hardware characteristic parameters, sensor and drive circuit hardware characteristic parameters, and heat dissipation system hardware characteristic parameters. S400, based on optimized hardware characteristic parameters, constructs digital parameters of the mechanical structure and heat dissipation performance of the brushless generator, which are used as optimized mechanical parameters and optimized heat dissipation parameters, respectively. S500 constructs a simulation model based on the current reference information and performs dynamic performance tests through the simulation model; the initial values ​​of the current reference information are the digital model, initial key parameters, initial optimization information, optimized hardware characteristic parameters, optimized mechanical parameters, and optimized heat dissipation parameters; S600: Calculate the deviation between the simulation test results and the target performance test results. If the deviation does not meet the preset conditions, correct the current key parameters and optimization information to update the current reference information. Execute S500. If the deviation meets the preset conditions, use the current reference information as the target reference information. The deviations include starting performance deviation, speed regulation performance deviation, load disturbance response deviation, and braking and heat dissipation performance deviation. Starting inrush current deviation, speed overshoot deviation, field weakening speed rotation deviation, and braking time deviation belong to the first type of deviation. Positioning error deviation, speed recovery time deviation, current fluctuation deviation, and junction temperature deviation belong to the second type of deviation. The threshold for the first type of deviation is less than the threshold for the second type of deviation.

2. The method for optimizing the dynamic performance of a brushless generator according to claim 1, characterized in that, in, The electromagnetic model was obtained through the following steps: S110, acquire the voltage and current information of the brushless generator in the three-phase stationary coordinate system, and use them as the first voltage information and the first current information; S111, Substitute the first voltage information and the first current information into the Clark transformation matrix respectively to convert them into voltage information and current information in the two-phase stationary coordinate system, which are used as the second voltage information and the second current information. S112, obtain the rotor position angle of the brushless generator and substitute it into the Park transformation matrix to convert the second voltage information and the second current information into voltage information and current information in a two-phase rotating coordinate system, which are used as the third voltage information and the third current information. S113, Based on the third voltage information and the third current information, the electromagnetic model is constructed. The electromagnetic model includes voltage equation, flux linkage equation and electromagnetic torque equation. The key parameters of the voltage equation include stator resistance, direct-axis inductance and quadrature-axis inductance in the two-phase rotating coordinate system, electric angular velocity and permanent magnet flux linkage. The key parameters of the flux linkage equation include direct-axis inductance and quadrature-axis inductance in the two-phase rotating coordinate system and permanent magnet flux linkage. The key parameters of the electromagnetic torque equation include pole pair number, direct-axis flux linkage and quadrature-axis flux linkage in the two-phase rotating coordinate system, direct-axis current and quadrature-axis current in the two-phase rotating coordinate system.

3. The method for optimizing the dynamic performance of a brushless generator according to claim 2, characterized in that, In S200, optimization of the basic control framework includes optimization of vector control and direct torque control; optimization of control algorithms includes optimization of model predictive control, sliding mode control and adaptive control; optimization of field weakening control includes optimization of voltage closed-loop monitoring and dynamic adjustment strategy of negative current injected into the direct axis in a two-phase rotating coordinate system; and optimization of starting and braking strategies includes optimization of three-stage starting and energy feedback braking.

4. The method for optimizing the dynamic performance of a brushless generator according to claim 1, characterized in that, in, The hardware characteristics of power devices include switching rise time, fall time, on-resistance, input capacitance, output capacitance, reverse recovery time, and withstand voltage parameters; the hardware characteristics of the motor body include stator resistance, direct-axis and quadrature-axis inductance in a two-phase rotating coordinate system, permanent magnet flux linkage, moment of inertia, and coefficient of friction; the hardware characteristics of sensors and drive circuits include linearity error, bandwidth, and zero drift of current sensors, resolution and delay time of position sensors, gate drive resistance, and DC bus capacitance; the hardware characteristics of the heat dissipation system include junction-to-case thermal resistance of power devices, case-to-ambient thermal resistance, thermal resistance of motor stator windings to the housing, and heat dissipation structure parameters.

5. The method for optimizing the dynamic performance of a brushless generator according to claim 1, characterized in that, The mechanical parameters include rotor structure parameters, stator structure parameters, bearing and transmission structure parameters, and load adaptation parameters. The heat dissipation parameters include motor body heat dissipation parameters, power device heat dissipation parameters, and overall heat dissipation path parameters.

6. The method for optimizing the dynamic performance of a brushless generator according to claim 1, characterized in that, in, The starting performance deviation includes starting inrush current deviation and positioning error deviation; the speed regulation performance deviation includes speed overshoot deviation and field weakening speed rotation deviation; the load disturbance response deviation includes speed recovery time deviation and current fluctuation deviation; and the braking and heat dissipation performance deviation includes braking time deviation and junction temperature deviation. In S600, if any deviation in the first type of deviation is greater than the first deviation threshold, or any deviation in the second type of deviation is greater than the second deviation threshold, the deviation is determined to not meet the preset condition. If all deviations in the first type of deviation are less than or equal to the first deviation threshold, and all deviations in the second type of deviation are less than or equal to the second deviation threshold, the deviation is determined to meet the preset condition, and the first deviation threshold is less than the second deviation threshold.

7. A brushless generator dynamic performance optimization system, characterized in that, The system includes: The model building module is used to digitally model the dynamic model of the brushless generator, obtain the digital model of the brushless generator, and acquire the key parameters of the digital model as initial key parameters; the digital model includes an electromagnetic model, a mechanical model, and a drive system model; the mechanical model satisfies the following condition: (T e -T L =J×dw m / dt+B×w m Among them, T e For electromagnetic torque, T L Where J is the load torque, B is the moment of inertia, and W is the coefficient of friction. m For mechanical angular velocity, dw m / dt means w m The derivative with respect to time; the drive system model includes an inverter switching characteristic model and a power device dynamic response model; wherein, the key parameters of the inverter switching characteristic model include switching delay time, rise time, fall time, DC bus voltage, output current and switching frequency, and the key parameters of the power device dynamic response model include input capacitance and output capacitance; The first optimization module is used to generate optimization information for optimizing the control strategy of the brushless generator based on the digital model and initial key parameters. As the initial optimization information, the optimization information includes optimization information for optimizing the basic control framework, control algorithm, field weakening control, and starting and braking strategies. Among them, the optimization of the control algorithm includes the optimization of adaptive control, which uses the recursive least squares method to identify the stator resistance and moment of inertia in real time. The second optimization module is used to digitally model the hardware characteristic parameters of the brushless generator based on the initial optimization information, and obtain the modeled hardware characteristic parameters as optimized hardware characteristic parameters; the hardware characteristic parameters include power device hardware characteristic parameters, motor body hardware characteristic parameters, sensor and drive circuit hardware characteristic parameters, and heat dissipation system hardware characteristic parameters. The third optimization module is used to construct digital parameters of the mechanical structure and heat dissipation performance of the brushless generator based on the optimized hardware characteristic parameters, which are used as optimized mechanical parameters and optimized heat dissipation parameters, respectively. The simulation testing module is used to build a simulation model based on the current reference information and perform dynamic performance tests through the simulation model; the initial values ​​of the current reference information are the digital model, initial key parameters, initial optimization information, optimized hardware characteristic parameters, optimized mechanical parameters, and optimized heat dissipation parameters; The judgment module is used to calculate the deviation between the simulation test results and the target performance test results. If the deviation does not meet the preset conditions, the current key parameters and optimization information are corrected to update the current reference information. If the deviation meets the preset conditions, the current reference information is used as the target reference information. The deviations include starting performance deviation, speed regulation performance deviation, load disturbance response deviation, and braking and heat dissipation performance deviation. Starting inrush current deviation, speed overshoot deviation, field weakening speed rotation deviation, and braking time deviation belong to the first type of deviation. Positioning error deviation, speed recovery time deviation, current fluctuation deviation, and junction temperature deviation belong to the second type of deviation. The threshold for the first type of deviation is less than the threshold for the second type of deviation.

Images