Refrigeration compressor optimization method and system based on modular disc type motor

Abstract

The invention discloses a refrigeration compressor optimization method and system based on a modular disc type motor, and relates to the technical field of refrigeration compressor optimization based on the modular disc type motor, and the method comprises the steps: completing the compression process of gas based on the reciprocating motion of a piston in a cylinder, and building a refrigeration compressor mathematical model; a sensorless control technology is adopted, and full-speed-domain accurate control is realized through low-speed high-frequency signal injection and a medium-high speed sliding mode observer method; and based on a torque collaborative optimization control strategy of an MOPSO algorithm, performing torque optimal distribution under different rotating speeds and torques. According to the method, through a torque collaborative optimization control strategy based on a multi-target particle swarm algorithm, torque optimal distribution can be carried out on two groups of motor modules in real time under different rotating speeds and torques of motors.

Description

Technical Field

[0001] The present invention relates to the technical field of modular disc motors, and in particular to a refrigeration compressor optimization method and system based on modular disc motors. Background Art

[0002] As the global demand for energy efficiency and environmental protection continues to increase, refrigeration, nitrogen and hydrogen production systems are increasingly used in various industries. These systems play a vital role in the food cold chain, medical refrigeration, chemical production and energy storage. However, although traditional compressor systems have been widely used in industrial, commercial and household fields, they still face problems such as low energy efficiency, high noise and complex maintenance. The mechanical structure of traditional compressors generates friction and heat loss during operation, resulting in low energy efficiency, especially under long-term operation and high-load conditions.

[0003] With the continuous improvement of high efficiency and environmental protection requirements, the traditional single motor system can no longer meet the high energy efficiency and high power requirements of modern refrigeration, nitrogen production and hydrogen production compressors. Therefore, the focus of research has gradually shifted to modular motor systems. Each sub-motor of the modular motor can be controlled according to the control method of the traditional three-phase motor, and the control method is simple. At the same power level, the current passing through each phase winding of each module unit of the modular motor is smaller, so power devices with smaller power levels but better performance can be applied to high-power equipment. In addition, the modular motor has a redundant module design. When a sub-module fails, the faulty sub-module can be cut off and the remaining normal sub-modules can be controlled to operate at a reduced rating. Combined with these advantages, modular motors will surely become a popular choice for high-power refrigeration, nitrogen production and hydrogen production compressors.

[0004] Disc motors have many significant advantages, including high torque density and flexible space design. These advantages are derived from the characteristics of permanent magnet synchronous motors. At the same time, their rotors and stators have the unique flat structure of axial motors, making them easy to modularize. By superimposing multiple modules, greater power can be obtained and the safety performance of the system can be improved. In addition, the control method of disc motors is basically the same as that of traditional columnar permanent magnet synchronous motors, which makes them significantly superior in application. Therefore, disc motors also have broad application prospects in high-power refrigeration, nitrogen production and hydrogen production compressor systems. . Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above problems or problems existing in the prior art, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is that with the continuous improvement of requirements for high efficiency and environmental protection, the traditional single-motor system can no longer meet the requirements of modern refrigeration, nitrogen production and hydrogen production compressors for high energy efficiency and high power.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing a refrigeration compressor based on a modular disc motor, which includes completing a gas compression process based on the reciprocating motion of a piston in a cylinder and establishing a mathematical model of the refrigeration compressor;

[0009] Adopt sensorless control technology to achieve precise control in the full speed range through low-speed and high-frequency signal injection and medium- and high-speed sliding mode observer method;

[0010] The torque collaborative optimization control strategy based on the MOPSO algorithm performs optimal torque distribution under different speeds and torques.

[0011] As a preferred solution of the refrigeration compressor optimization method based on modular disc motor of the present invention, wherein: the reciprocating motion includes three basic processes including suction, compression and exhaust in each stage, and the processes are combined together to form a complete cycle;

[0012] The mathematical model of the refrigeration compressor is established as follows:

[0013]

[0014] λ=λ V λ p λ T λ t

[0015] Where, λ is the gas transmission coefficient; Q is the refrigerant flow rate; λ is the compressor capacity; v i is the inlet specific volume of the refrigeration compressor; V is the volumetric air delivery of the compressor;

[0016]

[0017] Where D is the cylinder diameter; S is the piston path; n is the speed; Z is the number of compressor cylinders;

[0018]

[0019] Where, c is the relative clearance volume; m is the polynomial index; ε is the compression ratio;

[0020]

[0021] Among them, λ pis the pressure coefficient; T is the temperature coefficient;

[0022]

[0023] Among them, λ t Leakage coefficient; T2 compressor exhaust temperature;

[0024]

[0025] Where, T1 is the suction temperature; p d Exhaust pressure; p s Inspiratory pressure; P i Indicated power;

[0026]

[0027] Among them, P m is the input power;

[0028]

[0029] Among them, η m is the mechanical efficiency; η i is electrical efficiency;

[0030] Q=q m (h e -h V )

[0031] Among them, q m Mass flow rate; h e Evaporator outlet enthalpy; h v Evaporator inlet enthalpy;

[0032]

[0033] Among them, COP is the coefficient of performance; Q is the cooling capacity.

[0034] As a preferred solution of the refrigeration compressor optimization method based on modular disc motor of the present invention, wherein: the suction stage includes the piston moving backward, the pressure in the cylinder is reduced, and the gas entering the cylinder enters through the suction valve;

[0035] The compression stage includes the piston moving forward, the gas in the cylinder is compressed, and the intake valve is closed and the exhaust valve is opened, so that the gas is compressed to a high pressure state;

[0036] The exhaust stage includes that after compression is completed, the gas is discharged from the cylinder through the exhaust valve and transported to the condenser, thereby completing a cycle.

[0037] As a preferred solution of the refrigeration compressor optimization method based on modular disc motor of the present invention, wherein: the low-speed high-frequency signal injection includes:

[0038] Injected rotating high frequency signal frequency ω h Ratio to power supply fundamental angular frequency ω e At the same time, a symmetrical three-phase high-frequency rotating voltage is injected into the motor stator winding, which is transformed into a sinusoidal waveform with a phase difference of 90° in a two-phase stationary coordinate system and the high-frequency voltage amplitude is set to V inj , expressed as:

[0039]

[0040] Due to the cross-coupling term of the motor in the low-speed domain, the back EMF has little effect, and the equivalent model is:

[0041]

[0042] Written in vector form:

[0043]

[0044] Define the positive phase sequence amplitude I cp and the negative phase sequence amplitude I cn ; The modulation of high-frequency rotating current is affected by the motor salient pole position information, and the negative phase sequence component contains the rotor angle position information; therefore, after filtering, the component is modulated using the heterodyne method, which is expressed as:

[0045]

[0046] The Luenberger observer is used to estimate the rotor position.

[0047] As a preferred solution of the refrigeration compressor optimization method based on modular disc motor of the present invention, wherein: the medium-high speed sliding mode observer method includes:

[0048] The voltage equation is converted into the current state equation, expressed as:

[0049]

[0050] Construct an improved integral sliding surface, expressed as:

[0051]

[0052] According to the equivalent control principle:

[0053]

[0054] The design improves the sliding mode approach rate and adjusts the weights of k1 and k2, which can be expressed as:

[0055]

[0056] Differentiate the sliding surface and substitute into Z eq The improved control rate is expressed as:

[0057]

[0058] By applying the phase-locked loop to the observation of the motor position angle and speed and utilizing its closed-loop control characteristics, the position angle estimation value can quickly converge to the actual value.

[0059] The motor angle related quantity ε is obtained and the rotor position is estimated using the Luenberger observer.

[0060] As a preferred solution of the refrigeration compressor optimization method based on modular disc motor of the present invention, the torque collaborative optimization control strategy based on MOPSO algorithm performs optimal torque distribution under different speeds and torques, including:

[0061] Based on the double-stator single-rotor disc motor, the total output torque is T, where the output torques of the two motor modules are T1 and T2 respectively; there is a certain relationship between the refrigerant flow Q and the compression ratio r and the motor torque T, and a relationship model between the intake pressure, exhaust pressure and motor torque is established:

[0062] p d = k·T·p s

[0063] Where k is a constant, representing the proportionality coefficient between torque and compression ratio;

[0064] The refrigeration compressor system is optimized based on the particle swarm optimization algorithm, which includes:

[0065] Set algorithm parameters: set learning factors c1, c2 and weight coefficient w parameters; initialize the population to ensure that the number of particles, position X and velocity V are within the limit; set the Archive set to ensure that better and better non-inferior solutions are continuously added during the iteration process, and set the maximum archive size; set the number of iterations k = 1;

[0066] Calculate the fitness value of each particle;

[0067] Update the individual optimal position Pbest of each particle;

[0068] According to the particle dominance relationship, the non-inferior solution set is selected and stored in the Archive set, and the density information of the particles is calculated at the same time;

[0069] In the Archive set, the global optimal particle Gbest is selected according to the particle density information provided by the adaptive grid method;

[0070] Update the position and velocity of the particle according to the following formula, so that the particle changes its position in the search space by following Pbest and Gbest, and continuously approaches the global optimal solution:

[0071] v i (t+1)=w·v i (t)+c1·r1·(PBest i -x i (t))+c2·r2·(GBest-x i (t))

[0072] x i (t+1)=x i (t)+v i (t+1)

[0073] Update the Archive set and release the new generation of excellent and non-inferior games into the archive;

[0074] If the termination condition is met, the loop will be jumped out and the next step will be entered; if not, the next loop will be entered to continue the optimization;

[0075] Output a set of Pareto optimal solutions in the Archive set:

[0076]

[0077] The optimization objective function of the refrigeration compressor system is expressed as:

[0078]

[0079] Among them, the optimization objective function F(x) comprehensively considers the refrigeration performance of the refrigeration compressor and the efficiency of the disc motor. The weight coefficients ω1 and ω2 are the weight coefficients for adjusting the compressor power and the motor power respectively; t1 and t2 are the ratios of the single disc torque to the total torque; the optimization process satisfies a series of constraints g j (x), including but not limited to inlet temperature, exhaust temperature, refrigerant flow, compressor volumetric air delivery and torque limit, and ensuring that the variable inlet and exhaust pressures are between their upper and lower limits.

[0080] Finally, the number of particle swarms was set to 10, the maximum number of iterations was set to 50, and the optimal value was found after 500 iterations.

[0081] Another object of the present invention is to provide a system for optimizing refrigeration compressors based on modular disc motors, which can complete the gas compression process based on the reciprocating motion of the piston in the cylinder and establish a mathematical model of the refrigeration compressor; adopt sensorless control technology to achieve precise control over the entire speed range through low-speed and high-frequency signal injection and medium- and high-speed sliding mode observer methods; and based on the torque collaborative optimization control strategy of the MOPSO algorithm, perform optimal torque distribution at different speeds and torques, thereby solving the current problem of poor optimization of refrigeration compressors based on modular disc motors.

[0082] As a preferred solution of the system for optimizing the refrigeration compressor based on modular disc motors of the present invention, it includes: a building module, a control module, and a distribution module;

[0083] The establishment module is used to complete the gas compression process based on the reciprocating motion of the piston in the cylinder and establish a mathematical model of the refrigeration compressor;

[0084] The control module is used to achieve full-speed precision control by adopting sensorless control technology through low-speed and high-frequency signal injection and medium- and high-speed sliding mode observer method;

[0085] The distribution module is used for the torque collaborative optimization control strategy based on the MOPSO algorithm to perform optimal torque distribution under different speeds and torques.

[0086] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a refrigeration compressor optimization method based on a modular disc motor.

[0087] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a refrigeration compressor optimization method based on a modular disc motor.

[0088] Beneficial effects of the present invention: The present invention can perform optimal torque distribution for two groups of motor modules in real time at different motor speeds and torques through a torque collaborative optimization control strategy based on a multi-objective particle swarm algorithm. Compared with the torque equalization control strategy, the collaborative optimization control strategy based on the particle swarm algorithm can distribute torque according to the optimal efficiency of the refrigeration compressor and the optimal efficiency of the disc motor. The torque distribution ratio is not fixed, which can effectively improve the system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0090] Figure 1 This is a schematic diagram of the working of the refrigeration unit.

[0091] Figure 2 This is a structural diagram of a modular disc motor.

[0092] Figure 3 Block diagram of a disc motor driven refrigeration compressor.

[0093] Figure 4 This is the topological structure diagram of the single drive motor system.

[0094] Figure 5 This is the topological structure diagram of the three-module dual-stator single-rotor disc motor drive system.

[0095] Figure 6 This is the internal structure diagram of the torque distributor.

[0096] Figure 7 Schematic diagram of the phase-locked loop structure. DETAILED DESCRIPTION

[0097] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0098] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0099] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0100] Example 1

[0101] Reference Figures 1 to 7 , as one embodiment of the present invention, provides a refrigeration compressor optimization method based on a modular disc motor, comprising:

[0102] S1: Based on the reciprocating motion of the piston in the cylinder to complete the gas compression process, a mathematical model of the refrigeration compressor is established.

[0103] Working principle of refrigeration compressor:

[0104] The piston compressor completes the gas compression process through the reciprocating motion of the piston in the cylinder. Each level (or stage) includes three basic processes: suction, compression and exhaust, which are combined to form a complete cycle.

[0105] Suction stage: The piston moves backward, and the pressure in the cylinder decreases, causing the gas entering the cylinder (such as refrigerant) to enter through the suction valve.

[0106] Compression stage: The piston moves forward, the gas in the cylinder is compressed, and at the same time the intake valve is closed and the exhaust valve is opened, compressing the gas to a high pressure state.

[0107] Exhaust stage: After compression is completed, the gas is discharged from the cylinder through the exhaust valve and transported to the condenser, thus completing a cycle.

[0108] Refrigeration compressor mathematical model:

[0109]

[0110] λ=λ V λ p λ T λ t (2)

[0111] Among them, λ is the gas transmission coefficient; Q is the refrigerant flow rate; λ is the compressor capacity; vi is the refrigeration compressor inlet specific volume value; V is the compressor volumetric gas transmission volume;

[0112]

[0113] Where D is the cylinder diameter; S is the piston path; n is the speed; Z is the number of compressor cylinders;

[0114]

[0115] Wherein, c is relative clearance volume; m is polynomial index; ε is compression ratio;

[0116]

[0117] Among them, λ p Pressure coefficient; λ TTemperature coefficient, the effect of the temperature increase during the suction process on the gas transfer coefficient is an important consideration. When the refrigerant enters the compressor, it exchanges heat with the hot compressor walls and the surrounding air. This causes the suction fluid to have a lower density when it enters the compressor because it is heated and therefore expanded. In contrast, the working fluid is more dense at the compressor inlet because it has not yet been affected by the heating.

[0118]

[0119] Among them, λ t Leakage coefficient; T2 compressor exhaust temperature:

[0120]

[0121] Where, T1 is the suction temperature; p d Exhaust pressure; p s Inspiratory pressure; P i Indicated power refers to the work done by the gas on the piston in the cylinder during the compression process of the compressor. This is a theoretical power value that reflects the conversion of internal energy when the gas state changes, without considering the actual mechanical loss.

[0122]

[0123] Among them, P m Input power; ηm mechanical efficiency; ηi electrical efficiency;

[0124] Q=q m (h e -h V ) (10)

[0125] Among them, q m Mass flow rate; h e Evaporator outlet enthalpy; h v Evaporator inlet enthalpy;

[0126]

[0127] Among them, COP is the coefficient of performance; Q is the cooling capacity.

[0128] Reference Figure 1 This is a schematic diagram of the working of the refrigeration unit.

[0129] Furthermore, the high-temperature and high-pressure refrigerant vapor discharged by the piston refrigeration compressor first enters the oil separator for oil and gas separation, then condenses into liquid through the air-cooled condenser and enters the liquid storage tank for storage. Next, the liquid refrigerant passes through the drying filter and the regenerator to remove impurities and moisture to improve the refrigeration efficiency, and then becomes a low-temperature and low-pressure liquid refrigerant after throttling and reducing the pressure through the thermal expansion valve. Subsequently, the refrigerant enters the air cooler in the cold storage, absorbs the heat in the storage and vaporizes into low-temperature and low-pressure refrigerant vapor, and finally returns to the compressor for compression to start the cycle again.

[0130] It should be noted that the modular disc motor structure:

[0131] The modular disc motor structure usually consists of an integer multiple of double-stator single-rotor structures. Figure 2 The schematic diagram of the modular motor with a dual-stator single-rotor structure is shown. Connecting multiple sets of axial sub-motors coaxially can obtain greater torque and power without increasing the difficulty of the mechanical structure, better meeting the needs of high-probability compressors.

[0132] Further, taking a single disc motor with double stators and single rotor as an example:

[0133] The magnetic flux equation is:

[0134]

[0135] Where L AA , L BB , L CC are the self-inductance of the three-phase windings ABC; M AB and M BA , M AC and M CA , M BC and M CB , are the mutual inductances between stator windings AB, AC and BC respectively; ψ r is the magnetic flux of the rotor permanent magnet; θ is the rotor position angle.

[0136] The voltage equation can be obtained from the flux equation as follows:

[0137]

[0138] Where u A 、u B and u C ,i A 、i B and i C are the stator voltage and stator current in the ABC coordinate system, ψ A , B and ψ C is the stator flux in the ABC coordinate system, R sis the stator winding resistance.

[0139] Electromagnetic torque equation:

[0140] T e =N p ψ s ×i s (14)

[0141] Where T e is the electromagnetic torque; N p is the number of pole pairs of the torque winding; ψ s is the stator flux vector; i s is the stator current vector.

[0142] Equations of motion:

[0143]

[0144] Where, J is the motor's moment of inertia; B is the viscosity coefficient; T L is the load torque; ω r is the rotor angular velocity.

[0145] d.q axis voltage equation:

[0146]

[0147] In the formula, u d ,i d are the components of the stator voltage and current vector on the d axis; u q ,i q are the components of the stator voltage and current vectors on the q axis.

[0148] d, q axis stator flux equation:

[0149]

[0150] Where ψf is the flux linkage of the permanent magnet fundamental excitation magnetic field coupled to the stator winding; ψd and ψq are the components of the air gap magnetic field on the d-axis and q-axis respectively; L d , L q is the self-inductance of the d and q axis coils.

[0151] Electromagnetic torque equation:

[0152]

[0153] Since the axial motor selected in this paper does not have salient pole effect and L d =L q , so the torque equation is

[0154]

[0155] When modeling and analyzing the dual-stator single-rotor disc motor system, since the two sets of stators drive one rotor disc together, the structure is equivalent to two motor shafts. Therefore, ωr=ωr1=ωr2. Since the reference coordinate system corresponding to the stator is consistent, the shaft-connected motor can be placed in the same d, q reference coordinate system for analysis. The total electromagnetic torque is:

[0156]

[0157] It should be noted that the modular disc motor direct drive system:

[0158] According to the requirements of the host computer, the inverter converts the input voltage into a pulse width modulated voltage with adjustable amplitude and frequency, thereby controlling the speed of the disc motor and driving the compressor to operate efficiently.

[0159] In order to build a refrigeration compressor system, this paper adopts a modular disc motor system with dual stators and single rotor. The motor adopts axial flux design, has high power density and high efficiency, and is suitable for compact and high-performance applications. Figure 3 The refrigeration / nitrogen / hydrogen production compressor drive system using a single drive motor is demonstrated. The PC host computer is connected to the motor controller via the CAN bus. The host computer sends torque setting values, forward and reverse commands to the motor controller, and the motor controller feeds back the motor speed, current, voltage, temperature and other data to the host computer. After the motor drives the compressor to operate, the temperature, pressure and other parameters are fed back to the host computer.

[0160] For multi-motor systems, a collaborative optimization controller needs to be introduced between the host computer and the motor controller. Figure 4 The topology of the disc motor drive system with a modular dual-stator single-rotor structure is shown. One motor controller is used to control the motor of a module unit, which is equivalent to controlling two motor modules. The host computer needs the collaborative optimization controller to receive the control signal and torque data sent by it, and then transmit it to the motor controller through an appropriate communication protocol (such as CAN communication) to independently control each motor module. The motor controller needs to return the current motor status information to the collaborative optimization controller, integrate the data and then send it to the PC host computer.

[0161] Reference Figure 5 The collaborative optimization controller can be further replaced by a torque distributor module to handle torque grading and distribute it to the two motors. This control method can effectively save the on-board space of electric vehicles. In addition, this method makes it easier for the system to be modularly superimposed, so that the sub-motor modules have no impact on each other.

[0162] Reference Figure 6For refrigeration compressors, a large power is required under high load working conditions, so modular disc motors are used to obtain a large power refrigeration effect. At the same time, the number of modules to be started can be flexibly determined according to changes in load and working conditions, or the number of modular motors can be stacked up. The modules run simultaneously to provide sufficient torque requirements; in the case of low load or partial load, only one module can be started to save energy.

[0163] It should be noted that, referring to Figures 3 to 6 The whole control idea is to make the modular disc motor design allow the number of running motors to be flexibly adjusted according to actual needs. When the refrigeration compressor is under high load, two motor modules can be started at the same time to meet the high power demand, thereby saving energy. At the same time, the torque distribution is adjusted. The traditional control method uses the torque to be equally divided into two motor modules to drive the refrigeration compressor to operate.

[0164] S2: Adopt sensorless control technology to achieve precise control in the full speed range through low-speed and high-frequency signal injection and medium- and high-speed sliding mode observer method.

[0165] Sensorless Control Algorithm:

[0166] The application of sensorless control technology in disc motor refrigeration compressor systems is of great significance, especially in refrigeration systems. Through sensorless control technology, the motor can be accurately controlled without using a physical position sensor, which has significant advantages in improving system reliability, reducing costs and simplifying structures.

[0167] The control system design of refrigeration compressor is suitable for long-term efficient and stable operation in the medium and high frequency bands, and has low performance requirements for the low frequency band. Its driving control strategy is to accelerate the compressor motor to the appropriate frequency range when starting, and then run stably in the medium and high frequency bands for a long time. Combined with sensorless control technology, this paper proposes the following full-speed operation control strategy: high-frequency signal injection method is used in the low speed section; sliding mode observer method is used in the medium and high speed section to achieve smooth operation.

[0168] (1) Using high-frequency voltage injection method in the low-speed domain:

[0169] Injected rotating high frequency signal frequency ω h Ratio to power supply fundamental angular frequency ω e At the same time, a symmetrical three-phase high-frequency rotating voltage is injected into the motor stator winding, which can be transformed into a sinusoidal waveform with a phase difference of 90° in a two-phase stationary coordinate system and the high-frequency voltage amplitude is set to V inj .

[0170]

[0171] Due to the cross-coupling term of the motor in the low-speed domain, the back electromotive force has little effect, which is equivalent to the model shown in the following figure:

[0172]

[0173] Written in vector form:

[0174]

[0175] Define the positive phase sequence amplitude I cp and the negative phase sequence amplitude I cn The modulation of the high-frequency rotating current is affected by the motor salient pole position information, and the negative phase sequence component contains the rotor angle position information. Therefore, after filtering, the component is modulated using the heterodyne method:

[0176]

[0177] Finally, the Luenberger observer is used to estimate the rotor position.

[0178] (2) A new type of sliding mode observer method is used in the medium and high speed domain:

[0179] Convert the voltage equation into a current state equation:

[0180]

[0181] Construct an improved integral sliding surface:

[0182]

[0183] It should be noted that the improved integral sliding surface can enhance the anti-interference ability of the system. By introducing the integral term of the current error, high-frequency noise and interference can be better filtered out, and the robustness of the system can be enhanced. The integral term helps to smooth the error response, avoid sudden changes in transient response, and improve the stability of the system. The jitter caused by high-frequency noise is effectively reduced, and the system is more stable. The noise sensitivity problem of traditional sliding mode control is solved.

[0184] According to the equivalent control principle:

[0185]

[0186] It should be noted that the equivalent control Zeq represents the equivalent dynamic behavior of the system near the sliding surface. s is the equivalent resistance of the system, CL is the equivalent inductance of the system, i sis the current error. Through equivalent control, the dynamic behavior of the system on the sliding surface can be described more accurately, providing the basis for the design of the control law. Through the calculation of equivalent control, the sliding mode controller can more accurately track and control the dynamic behavior of the system, improving the accuracy and effectiveness of the sliding mode controller design.

[0187] By improving the sliding mode approach rate and adjusting the weights of k1 and k2 appropriately, the high-frequency chattering problem caused by the traditional sliding mode can be effectively reduced:

[0188]

[0189] It should be noted that traditional sliding mode control will produce high-frequency jitter near the switching surface, affecting system stability. Traditional sliding mode control is not robust enough to parameter changes and external interference. By introducing the nonlinear term |s| α sat(s), which can effectively suppress high-frequency chattering.

[0190] Differentiating the sliding surface and substituting it into Zeq, we can get the improved control rate:

[0191]

[0192] It should be noted that the equation combines the equivalent control and the new sliding mode control rate to define the final control rate Z eq ; contains the dynamic change term of current i s And improved sliding mode control terms. Substituting the above formulas into the above formulas, we can get that the above three formulas are generally for a better control strategy, which improves the overall performance of the system and can maintain high performance and high stability when facing complex working conditions.

[0193] By applying a phase-locked loop (PLL) to the observation of the motor position angle and speed, and utilizing its closed-loop control characteristics, the position angle estimate can quickly converge to the actual value, significantly reducing the impact of high-frequency noise and improving the observation accuracy. Figure 7 shown.

[0194] The motor angle related quantity ε is finally obtained, and the rotor position is estimated using the Luenberger observer.

[0195] S3: Torque collaborative optimization control strategy based on MOPSO algorithm to achieve optimal torque distribution under different speeds and torques.

[0196] MOPSO torque allocation algorithm

[0197] Taking the double-stator single-rotor disc motor studied in this paper as an example, the total output torque is T, ranging from [0,500Nm], where the output torques of the two motor modules are T1 and T2, both ranging from [0,250Nm]. The motor torque directly affects the operation of the compressor, thereby affecting the flow and compression process of the refrigerant in the system, and further affecting the intake and exhaust pressures. There is a certain relationship between the refrigerant flow rate Q and the compression ratio r and the motor torque T, and a relationship model between the intake pressure, exhaust pressure and motor torque can be established:

[0198] p d = k·T·p s (30)

[0199] Wherein, k is a constant representing the proportionality coefficient between torque and compression ratio.

[0200] The MOPSO algorithm maintains the individual optimal solution (PBest) of each particle and a set of global optimal solutions (GBest), and uses archives to avoid losing the optimal solution of the particle. In the initial stage, the basic parameters are set and the particle swarm is initialized, the fitness is calculated and the results are saved to the archive. Subsequently, the speed and position of the particle are updated according to the current individual optimal solution and the global optimal solution, the fitness is calculated again and the individual optimal solution is updated. By updating the archive set, the global optimal solution is selected, and it is iterated repeatedly before the termination condition is met to find the optimal solution space exploration path.

[0201] The basic steps of MOPSO are as follows:

[0202] The optimization steps of the refrigeration compressor system based on the particle swarm optimization algorithm are as follows:

[0203] 1. Set algorithm parameters: set learning factors c1, c2 and weight coefficient w and other parameters; initialize the population to ensure that the number of particles, position X and velocity V are within the limit; set the Archive set to ensure that better and better non-inferior solutions are continuously added during the iteration process, and set the maximum archive size; set the number of iterations k = 1.

[0204] 2. Fitness calculation: Calculate the fitness value of each particle.

[0205] 3. Update the individual historical best particle Pbest: Update the individual best position Pbest of each particle.

[0206] 4. Filter the non-inferior solution set: Filter the non-inferior solution set according to the particle dominance relationship, store it in the Archive set, and calculate the density information of the particles.

[0207] 5. Select the global optimal particle Gbest: In the Archive set, select the global optimal particle Gbest according to the particle density information provided by the adaptive grid method.

[0208] 6. Update position X and velocity V: Update the position and velocity of the particle according to the following formula, so that the particle changes its position in the search space by following Pbest and Gbest, and continuously approaches the global optimal solution:

[0209] v i (t+1)=w·v i (t)+c1·r1·(PBest i -x i (t))+c2·r2·(GBest-x i (t)) (31)

[0210] x i (t+1)=x i (t)+v i (t+1) (32)

[0211] 7. Update Archive set: release the new generation of excellent and non-inferior games into the archive.

[0212] 8. Determine the termination condition: Determine whether the algorithm can terminate. If the termination condition is met, jump out of the loop and enter step 9; if not, enter the next loop to continue optimizing and return to step 2.

[0213] 9. Output the optimal solution: Output a set of Pareto optimal solutions in the Archive set.

[0214]

[0215] From formula (11), we can know that to find the maximum value of the performance coefficient is to find the minimum value of the input power.

[0216] To improve the system efficiency, we have to reduce the total input power. To maximize the motor efficiency η, we need to find the minimum value of the denominator of formula (23).

[0217] For the optimization objective function of the refrigeration compressor system, starting from improving the performance coefficient of the refrigeration compressor system and improving the efficiency of the disc motor system, the problem is transformed into a multi-objective optimization problem of how to minimize the Pm value when the cooling capacity of the refrigeration compressor is constant and how to distribute the torque ratio of the two groups of motor modules under a given torque so that the value of A is minimized.

[0218] The objective function can be obtained as follows:

[0219]

[0220] In the optimization of the disc electric drive system of the refrigeration compressor, the optimization objective function F(x) comprehensively considers the refrigeration performance of the refrigeration compressor and the efficiency of the disc motor. The weight coefficients ω1 and ω2 are the weight coefficients for adjusting the compressor power and the motor power respectively. t1 and t2 are the ratios of the single disc torque to the total torque. The optimization process also needs to meet a series of constraints gj(x), such as intake temperature, exhaust temperature, refrigerant flow, compressor volumetric gas delivery and torque limit, and ensure that the variable intake and exhaust pressures are between their upper and lower limits.

[0221] Finally, the number of particle swarms was set to 10, the maximum number of iterations was set to 50, and the optimal value was found after 500 iterations.

[0222] In summary, the torque collaborative optimization control strategy based on the multi-objective particle swarm algorithm can perform optimal torque distribution for the two motor modules in real time at different motor speeds and torques. Compared with the torque equalization control strategy, the collaborative optimization control strategy based on the particle swarm algorithm can distribute torque according to the optimal efficiency of the refrigeration compressor and the optimal efficiency of the disc motor. The torque distribution ratio is not fixed, which can effectively improve the system efficiency.

[0223] The above is a schematic scheme of a refrigeration compressor optimization method based on a modular disc motor in this embodiment. It should be noted that the technical scheme of the refrigeration compressor optimization system based on a modular disc motor and the technical scheme of the refrigeration compressor optimization method based on a modular disc motor belong to the same concept. The details of the technical scheme of the refrigeration compressor optimization system based on a modular disc motor in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the refrigeration compressor optimization method based on a modular disc motor.

[0224] This embodiment also proposes a refrigeration compressor optimization system based on a modular disc motor, including:

[0225] Establish modules, control modules, and allocate modules;

[0226] The establishment module is used to complete the gas compression process based on the reciprocating motion of the piston in the cylinder and establish a mathematical model of the refrigeration compressor;

[0227] The control module is used to achieve full-speed precision control by adopting sensorless control technology through low-speed and high-frequency signal injection and medium- and high-speed sliding mode observer method;

[0228] The distribution module is used for the torque collaborative optimization control strategy based on the MOPSO algorithm to perform optimal torque distribution under different speeds and torques.

[0229] This embodiment further provides a computing device, which is applicable to the case of optimizing a refrigeration compressor based on a modular disc motor, and includes:

[0230] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the refrigeration compressor optimization method based on modular disc motor as proposed in the above embodiment.

[0231] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing a refrigeration compressor based on a modular disc motor as proposed in the above embodiment is implemented.

[0232] The storage medium proposed in this embodiment and the method for optimizing a refrigeration compressor based on a modular disc motor proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0233] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0234] Example 2

[0235] As the second embodiment of the present invention, a refrigeration compressor optimization method based on a modular disc motor is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0236] In order to test the system performance, a speed point is selected every 10kw from the refrigeration compressor input power range of 10kw to 210kw, for a total of 20 power points; at each speed point, a torque point is selected every 10Nm from the output torque of 10Nm to 150Nm, for a total of 14 torque points. Corresponding to different speeds and torques, a total of 280 efficiency collection points are set, and the output torque T and refrigeration compressor input power pm at each efficiency point are collected to obtain the spectrum.

[0237] The area with system efficiency above 93% using torque sharing control system accounts for 18.25%, the area with system efficiency above 95% accounts for 8.80%, and the area with system efficiency above 97% accounts for 0.52%. If the torque distribution method optimized by MOPSO algorithm is used, the area with system efficiency above 93% accounts for 22.11%, the area with system efficiency above 95% accounts for 14.08%, and the area with system efficiency above 97% accounts for 3.25%.

[0238] Through the analysis of the test data of system performance, the torque distribution optimized by the MOPSO algorithm shows a significant improvement in efficiency compared with the traditional torque split method. In the efficiency range above 93%, the area increased by 3.86%; in the efficiency range above 95%, the area increased by 5.28%; in the efficiency range above 97%, the area increased by 2.73%. These data show that the MOPSO algorithm optimization can significantly improve the operating efficiency of the system under different working conditions, making the system more stable in a wider efficiency range, thereby improving the overall system performance and energy saving effect.

[0239] Table 1 Comparison of system efficiency using torque sharing control and MOPSO algorithm torque distribution control

[0240]

[0241] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0242] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0243] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0244] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0245] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A refrigeration compressor optimization method based on a modular disc motor, characterized in that: include, Based on the reciprocating motion of the piston in the cylinder to complete the gas compression process, a mathematical model of the refrigeration compressor is established; Adopt sensorless control technology to achieve precise control in the full speed range through low-speed and high-frequency signal injection and medium- and high-speed sliding mode observer method; The torque collaborative optimization control strategy based on the MOPSO algorithm performs optimal torque distribution under different speeds and torques.

2. The refrigeration compressor optimization method based on modular disc motor according to claim 1, characterized in that: The reciprocating motion includes three basic processes in each stage, namely, suction, compression and exhaust, and the processes are combined together to form a complete cycle; The mathematical model of the refrigeration compressor is established as follows: λ=λ V l p l T l t Where, λ is the gas transmission coefficient; Q is the refrigerant flow rate; λ is the compressor capacity; v i is the inlet specific volume of the refrigeration compressor; V is the volumetric air delivery of the compressor; Where D is the cylinder diameter; S is the piston path; n is the speed; Z is the number of compressor cylinders; Where, c is the relative clearance volume; m is the polynomial index; ε is the compression ratio; Among them, λ p is the pressure coefficient; T is the temperature coefficient; Among them, λ t is the leakage coefficient; T2 is the compressor exhaust temperature; Where T1 is the suction temperature; p d is the exhaust pressure; p s is the inspiratory pressure; P i To indicate power; Among them, η m is the mechanical efficiency; η i is the electrical efficiency; P m is the input power; Q=q m (h e -h V ) Among them, q m is the mass flow rate; h e is the enthalpy at the evaporator outlet; h v is the enthalpy at the evaporator inlet; Among them, COP is the coefficient of performance; Q is the cooling capacity.

3. The refrigeration compressor optimization method based on modular disc motor according to claim 2, characterized in that: The suction phase includes the rearward movement of the piston, which reduces the pressure in the cylinder, causing the gas entering the cylinder to enter through the suction valve; The compression stage includes the piston moving forward, the gas in the cylinder is compressed, and the intake valve is closed and the exhaust valve is opened, so that the gas is compressed to a high pressure state; The exhaust stage includes that after compression is completed, the gas is discharged from the cylinder through the exhaust valve and transported to the condenser, thereby completing a cycle.

4. The method for optimizing a refrigeration compressor based on a modular disc motor according to claim 3, characterized in that: The low-speed high-frequency signal injection includes: Injected rotating high frequency signal frequency ω h Ratio to power supply fundamental angular frequency ω e At the same time, a symmetrical three-phase high-frequency rotating voltage is injected into the motor stator winding, which is transformed into a sinusoidal waveform with a phase difference of 90° in a two-phase stationary coordinate system and the high-frequency voltage amplitude is set to V inj , expressed as: Due to the cross-coupling term of the motor in the low-speed domain, the back EMF has little effect, and the equivalent model is: Written in vector form: Define the positive phase sequence amplitude I cp and the negative phase sequence amplitude I cn ; The modulation of high-frequency rotating current is affected by the motor salient pole position information, and the negative phase sequence component contains the rotor angle position information; therefore, after filtering, the component is modulated using the heterodyne method, which is expressed as: The Luenberger observer is used to estimate the rotor position.

5. The refrigeration compressor optimization method based on modular disc motor according to claim 4, characterized in that: The medium- and high-speed sliding mode observer method includes: The voltage equation is converted into the current state equation, expressed as: Construct an improved integral sliding surface, expressed as: According to the equivalent control principle: The design improves the sliding mode approach rate and adjusts the weights of k1 and k2, which can be expressed as: Differentiate the sliding surface and substitute into Z eq The improved control rate is expressed as: By applying the phase-locked loop to the observation of the motor position angle and speed and utilizing its closed-loop control characteristics, the position angle estimation value can quickly converge to the actual value. The motor angle related quantity ε is obtained and the rotor position is estimated using the Luenberger observer.

6. The method for optimizing a refrigeration compressor based on a modular disc motor according to claim 5, characterized in that: The torque collaborative optimization control strategy based on the MOPSO algorithm performs optimal torque distribution at different speeds and torques, including: Based on the double-stator single-rotor disc motor, the total output torque is T, where the output torques of the two motor modules are T1 and T2 respectively; there is a certain relationship between the refrigerant flow Q and the compression ratio r and the motor torque T, and a relationship model between the intake pressure, exhaust pressure and motor torque is established: p d =k·T·p s Where k is a constant, representing the proportionality coefficient between torque and compression ratio; The refrigeration compressor system is optimized based on the particle swarm optimization algorithm, which includes: Set algorithm parameters: set learning factors c1, c2 and weight coefficient w parameters; initialize the population to ensure that the number of particles, position X and velocity V are within the limit; set the Archive set to ensure that better and better non-inferior solutions are continuously added during the iteration process, and set the maximum archive size; set the number of iterations k = 1; Calculate the fitness value of each particle; Update the individual optimal position Pbest of each particle; According to the particle dominance relationship, the non-inferior solution set is selected and stored in the Archive set, and the density information of the particles is calculated at the same time; In the Archive set, the global optimal particle Gbest is selected according to the particle density information provided by the adaptive grid method; Update the position and velocity of the particle according to the following formula, so that the particle changes its position in the search space by following Pbest and Gbest, and continuously approaches the global optimal solution: v i (t+1)=w·v i (t)+c1·r1·(PBest i -x i (t))+c2·r2·(GBest-x i (t)) x i (t+1)=x i (t)+v i (t+1) Update the Archive set and release the new generation of excellent and non-inferior games into the archive; If the termination condition is met, the loop will be jumped out and the next step will be entered; if not, the next loop will be entered to continue the optimization; Output a set of Pareto optimal solutions in the Archive set: The optimization objective function of the refrigeration compressor system is expressed as: Among them, the optimization objective function F(x) comprehensively considers the refrigeration performance of the refrigeration compressor and the efficiency of the disc motor. The weight coefficients ω1 and ω2 are the weight coefficients for adjusting the compressor power and the motor power respectively; t1 and t2 are the ratios of the single disc torque to the total torque; the optimization process satisfies a series of constraints g j (x), including but not limited to inlet temperature, exhaust temperature, refrigerant flow, compressor volumetric air delivery and torque limit, and ensuring that the variable inlet and exhaust pressures are between their upper and lower limits. Finally, the number of particle swarms was set to 10, the maximum number of iterations was set to 50, and the optimal value was found after 500 iterations.

7. A system using the refrigeration compressor optimization method based on modular disc motor according to any one of claims 1 to 6, characterized in that: Including establishment module, control module and allocation module; The establishment module is used to complete the gas compression process based on the reciprocating motion of the piston in the cylinder and establish a mathematical model of the refrigeration compressor; The control module is used to achieve full-speed precision control by adopting sensorless control technology through low-speed and high-frequency signal injection and medium- and high-speed sliding mode observer method; The distribution module is used for the torque collaborative optimization control strategy based on the MOPSO algorithm to perform optimal torque distribution under different speeds and torques.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the refrigeration compressor optimization method based on a modular disc motor according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the refrigeration compressor optimization method based on a modular disc motor according to any one of claims 1 to 6 are implemented.

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