A method, system, device and medium for optimizing a refrigeration compressor based on a modular disc motor

Abstract

The application discloses a refrigeration compressor optimization method, system, equipment and medium based on a modular disc motor, relates to the refrigeration compressor optimization technical field based on the modular disc motor, and comprises the following steps: a compression process of gas is completed based on reciprocating motion of a piston in a cylinder, and a mathematical model of the refrigeration compressor is established; sensorless control technology is adopted, low-speed high-frequency signal injection and a medium-speed and high-speed sliding mode observer method are used to realize accurate control in a full speed range; and a torque collaborative optimization control strategy based on an MOPSO algorithm is used to realize optimal torque distribution under different rotating speeds and torques. According to the method, the torque collaborative optimization control strategy based on the multi-objective particle swarm optimization algorithm can realize real-time optimal torque distribution of two groups of motor modules under different rotating speeds and torques of the motor.

Description

Technical Field

[0001] This invention relates to the field of modular disc motor technology, and in particular to an optimization method, system, equipment and medium for refrigeration compressors based on modular disc motors. Background Technology

[0002] With increasing global demands for energy efficiency and environmental protection, refrigeration, nitrogen production, and hydrogen production systems are being used more and more widely across various industries. These systems play a crucial role in food cold chain, medical refrigeration, chemical production, and energy storage. However, while traditional compressor systems have been widely adopted in industrial, commercial, and residential sectors, they still face problems such as low energy efficiency, high noise levels, and complex maintenance. The mechanical structure of traditional compressors generates friction and heat loss during operation, leading to low energy efficiency, especially under long-term operation and high-load conditions.

[0003] With ever-increasing demands for high efficiency and environmental protection, traditional single-motor systems can no longer meet the high-efficiency and high-power requirements of modern refrigeration, nitrogen, and hydrogen compressors. Therefore, research has gradually shifted towards modular motor systems. Each sub-motor in a modular motor can be controlled using traditional three-phase motor control methods, simplifying the control process. At the same power rating, each phase winding of each module in a modular motor carries a smaller current, allowing for the application of lower-power but higher-performance power devices in high-power equipment. Furthermore, modular motors feature redundant module design; when a sub-module fails, the faulty sub-module can be disconnected, and the remaining normal sub-modules can operate at reduced derating. Combining these advantages, modular motors are poised to become a popular choice for high-power refrigeration, nitrogen, and hydrogen compressors.

[0004] Disc motors offer several significant advantages, including high torque density and flexible space design. These advantages stem from the characteristics of permanent magnet synchronous motors. Furthermore, their rotor and stator possess a unique flattened structure characteristic of axial motors, facilitating modularization. By stacking multiple modules, greater power can be achieved, and system safety performance can be improved. In addition, the control methods for disc motors are essentially the same as those for traditional column-type permanent magnet synchronous motors, giving them a clear advantage in applications. Therefore, disc motors also have broad application prospects in high-power refrigeration, nitrogen generation, and hydrogen production compressor systems. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

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

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

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an optimization method for a refrigeration compressor based on a modular disc motor, which includes establishing a mathematical model of the refrigeration compressor based on the gas compression process completed by the reciprocating motion of the piston in the cylinder;

[0009] Employing sensorless control technology, precise control across the entire speed range is achieved through low-speed, high-frequency signal injection and a medium-to-high-speed sliding mode observer method.

[0010] A torque cooperative optimization control strategy based on the MOPSO algorithm is used to achieve optimal torque allocation under different speeds and torques.

[0011] The medium-to-high-speed sliding mode observer method includes a sliding mode observation method with an improved structure:

[0012] The voltage equation is transformed into a current state equation, which can be expressed as:

[0013]

[0014] Among them, i α i β These are the currents along the α and β axes, respectively; A is the coefficient matrix; u α u β These are the voltages along the α and β axes, respectively; e α e β These are the back electromotive forces along the α and β axes, respectively; ω e It is the electric angular velocity; Rs is the equivalent resistance of the system; L d It is the d-axis inductance of the system; L q It is the q-axis inductance of the system;

[0015] Assume the error current is defined as i s =i 实际 -i 参考 The error voltage is defined as u s The back electromotive force error is e s Therefore, based on the current state equation, the dynamic equation is:

[0016]

[0017] An improved integral sliding surface is constructed, denoted as:

[0018]

[0019] Where c is a constant, s is the improved integral sliding surface, and t is time;

[0020] Taking the time derivative of the integral sliding surface, we get:

[0021]

[0022] in, It is the result of differentiating the integral sliding surface;

[0023] Substituting the system dynamic equations, we get:

[0024]

[0025] According to the sliding mode equivalent control principle, as the system approaches the sliding surface, |s| gradually approaches 0. It will also gradually approach 0; in order to ensure stable operation of the system on the sliding surface, it is assumed that... Substituting matrix A into the equation, we get:

[0026]

[0027] Because the selected disc motor does not have salient pole effect, and L d =L q Ignore coupling term ω e (L d -L q From this, we can obtain:

[0028] u eq =(R s -cL d )i s +e s

[0029] Among them, u eq It is the equivalent control part of sliding mode;

[0030] The design improves the sliding mode convergence rate, and appropriately adjusts the weights of k1 and k2 to reduce the high-frequency chattering problem caused by traditional sliding mode:

[0031]

[0032] That is, the sliding mode switching part; where k1>0, k2>0, 1>α>0; and sat(s) is the saturation function.

[0033]

[0034] Where p>0; σ represents the boundary layer;

[0035] To verify that the improved sliding mode reaching law can satisfy the stability required for sliding mode control, we use the Lyapunov equation:

[0036]

[0037] Conditions met:

[0038]

[0039] Where V is the Lyapunov equation; To find the derivative of the Lyapunov equation;

[0040] The system is asymptotically stable on the sliding surface and the sliding mode exists;

[0041] Combining the improved sliding mode switching part and the equivalent control law part yields the improved control law Z. eq :

[0042] Z eq =(R s -cL d )i s +e s -(-k1|s| α sat(s)-k2s).

[0043] As a preferred embodiment of the refrigeration compressor optimization method based on modular disc motor described in this invention, the reciprocating motion includes three basic processes in each stage: suction stage, compression stage, and exhaust stage, which are combined to form a complete cycle.

[0044] The establishment of the mathematical model for the refrigeration compressor includes:

[0045]

[0046] λ=λ V λ p λ T λ t

[0047] Where λ is the gas delivery coefficient; Q is the refrigerant flow rate; V v V represents the compressor volume. i This refers to the inlet specific volume of the refrigeration compressor.

[0048]

[0049] Among them, V g Where: is the compressor volumetric gas delivery capacity; D is the cylinder diameter; S is the piston path; n is the rotational speed; Z is the number of compressor cylinders;

[0050]

[0051] Where c is the relative clearance volume; m is the polytropic index; and ε is the compression ratio.

[0052]

[0053] Where, λ p λ is the pressure coefficient; T Temperature coefficient;

[0054]

[0055] Where, λ t T1 is the leakage coefficient; T2 is the compressor discharge temperature;

[0056]

[0057] Where T1 is the intake temperature; p d p is the exhaust pressure; s P is the inhalation pressure; i To indicate power;

[0058]

[0059] Where, η m For mechanical efficiency; η i For electrical efficiency; P m Input power;

[0060] Q = q m (h e -h V )

[0061] Where, q m For mass flow rate; h e h is the enthalpy value at the evaporator outlet. v This refers to the enthalpy value at the evaporator inlet.

[0062]

[0063] Where COP is the coefficient of performance; Q is the cooling capacity.

[0064] As a preferred embodiment of the refrigeration compressor optimization method based on modular disc motor described in this invention, the suction stage includes the piston moving backward, the pressure inside the cylinder decreasing, causing the gas entering the cylinder to enter through the suction valve;

[0065] The compression stage includes the piston moving forward, the gas in the cylinder being compressed, the intake valve being closed and the exhaust valve being opened, so that the gas is compressed to a high pressure state.

[0066] The exhaust phase includes the process where, after compression, the gas is discharged from the cylinder through the exhaust valve and delivered to the condenser, thus completing one cycle.

[0067] As a preferred embodiment of the refrigeration compressor optimization method based on a modular disc motor described in this invention, the low-speed high-frequency signal injection includes:

[0068] Injected high-frequency rotating signal frequency ω h Compared to the fundamental angular frequency ω of the power supply e Simultaneously, a symmetrical three-phase high-frequency rotating voltage is injected into the stator winding of the motor, transforming it into a sinusoidal waveform with a 90° phase difference in a two-phase stationary coordinate system, and setting the amplitude of the high-frequency voltage to V. inj , represented as:

[0069]

[0070] Among them, u αh u βh It is a high-frequency injection voltage;

[0071] Since the back electromotive force has a small impact due to the cross-coupling terms in the low-speed domain motor, the equivalent model is:

[0072]

[0073] Among them, u dh u qh It is the high-frequency voltage after coordinate transformation; C 2r / 2s i is a simplified matrix for coordinate transformation; dh i qh It is the high-frequency current after coordinate transformation; θ is the angle of the corresponding coordinate transformation;

[0074] In vector form:

[0075]

[0076] in, High-frequency current vector form; L is the average inductance (L d +L q ) / 2; ΔL is the half-differential inductance (L d -L q ) / 2;

[0077] Define the forward phase sequence magnitude I cp and negative phase sequence amplitude I cn The modulation of the high-frequency rotating current is affected by the salient pole position information of the motor, and the negative phase sequence component contains the rotor angular position information; therefore, after filtering, this component is modulated using the heterodyne method, expressed as:

[0078]

[0079] Where ε is the location information component;

[0080] The rotor position is estimated using a Luenberger observer.

[0081] As a preferred embodiment of the refrigeration compressor optimization method based on a modular disc motor described in this invention, the torque collaborative optimization control strategy based on the MOPSO algorithm includes optimal torque allocation under different speeds and torques, comprising:

[0082] Based on a disc motor with a dual stator and a single rotor, 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 rate Q and the compression ratio r and the motor torque T. A model is established to represent the relationship between the intake pressure, exhaust pressure, and motor torque.

[0083] p d =k·T·p s

[0084] Where k is a constant representing the proportionality coefficient between torque and compression ratio;

[0085] Optimization of refrigeration compressor systems based on particle swarm optimization algorithm includes,

[0086] Set algorithm parameters: Set learning factors c1, c2 and weight coefficient w; initialize the population to ensure that the number of particles, position X and velocity V are all within the limits; set the Archive set to ensure that better non-dominated solutions are added during the iteration process, and set the maximum archive size; set the number of iterations k = 1;

[0087] Calculate the fitness value for each particle;

[0088] Update the individual optimal position Pbest for each particle;

[0089] The non-dominated solution set is selected based on the particle dominance relationship and stored in the Archive set, while the particle density information is calculated.

[0090] In the Archive set, the globally optimal particle Gbest is selected based on the particle density information provided by the adaptive grid method.

[0091] The particle's position and velocity are updated according to the following formula, allowing the particle to change its position in the search space by following Pbest and Gbest, and continuously approach the global optimum:

[0092] v i (t+1)=w·v i (t)+c1·r1·(PBest i-x i (t))+c2·r2·(GBest i -x i (t))

[0093] x i (t+1)=x i (t)+v i (t+1)

[0094] Where, x i (t) represents the position of the particle at time t; v i (t) represents the velocity of the particle at time t; x i (t+1) represents the position of the particle at time t+1; v i (t+1) represents the particle velocity at time t+1; r1 and r2 represent different compression ratios;

[0095] Update the Archive collection to include the new generation of excellent non-inferior items in the archive.

[0096] If the termination condition is met, the loop exits and proceeds to the next step; if not, the loop continues to search for optimization.

[0097] Output a set of Pareto optimal solutions in the Archive set:

[0098]

[0099] Where A represents a set of optimal solutions to the objective; P o η is the output power of the motor; η is the system efficiency; η1 and η2 are the efficiencies of the two sets of motor modules respectively; a1 and a2 are the torque distribution coefficients of the two sets of motor modules respectively; T is the total torque of the motor; ω is the speed of the motor.

[0100] The objective function for optimizing a refrigeration compressor system is expressed as:

[0101]

[0102] The objective function F(x) comprehensively considers the refrigeration performance of the compressor and the efficiency of the disc motor. The weighting coefficients ω1 and ω2 are the weighting coefficients for adjusting the compressor power and motor power, respectively. t1 and t2 are the ratios of single disc torque to total torque. The optimization process satisfies a series of constraints g. j (x), including but not limited to intake temperature, exhaust temperature, refrigerant flow rate, compressor volumetric gas delivery capacity and torque limit, and ensuring that the variable intake and exhaust pressures are between their upper and lower limits;

[0103] The final particle swarm size was set to 10, and the maximum number of iterations was set to 50. After 500 iterations, the optimal value was found.

[0104] Another objective of this invention is to provide an optimized system for a refrigeration compressor based on a modular disc motor. This system can establish a mathematical model of the refrigeration compressor by completing the gas compression process based on the reciprocating motion of the piston in the cylinder. It employs sensorless control technology, achieving precise control across the entire speed domain through low-speed high-frequency signal injection and a medium-to-high-speed sliding mode observer method. Based on the MOPSO algorithm, a torque collaborative optimization control strategy is used to achieve optimal torque allocation under different speeds and torques, thus solving the problem of poor optimization in current refrigeration compressors based on modular disc motors.

[0105] As a preferred embodiment of the refrigeration compressor optimization system based on modular disc motor described in this invention, it includes: a setup module, a control module, and an allocation module;

[0106] The establishment module is used to establish a mathematical model of the refrigeration compressor based on the reciprocating motion of the piston in the cylinder to complete the gas compression process.

[0107] The control module is used to achieve precise control across the entire speed range by employing sensorless control technology, through low-speed high-frequency signal injection and medium-to-high-speed sliding mode observer method.

[0108] The allocation module is used for torque cooperative optimization control strategy based on MOPSO algorithm to perform optimal torque allocation under different speeds and torques.

[0109] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement a method for optimizing a refrigeration compressor based on a modular disc motor.

[0110] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of an optimization method for a refrigeration compressor based on a modular disc motor.

[0111] The beneficial effects of this invention are as follows: This invention, through a torque collaborative optimization control strategy based on a multi-objective particle swarm optimization algorithm, can achieve real-time optimal torque allocation for two sets of motor modules under different motor speeds and torques. Compared to a torque equalization control strategy, the collaborative optimization control strategy based on the particle swarm optimization algorithm can allocate torque according to the optimal efficiency of the refrigeration compressor and the optimal efficiency of the disc motor. The torque allocation ratio is not fixed, which can effectively improve system efficiency. Attached Figure Description

[0112] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0113] Figure 1 This is a schematic diagram of the refrigeration device in operation.

[0114] Figure 2 This is a structural diagram of a modular disc motor.

[0115] Figure 3 This is a block diagram of a refrigeration compressor driven by a disc motor.

[0116] Figure 4 This is a topology diagram of a single-drive motor system.

[0117] Figure 5 This is a topology diagram of a three-module dual-stator single-rotor disc motor drive system.

[0118] Figure 6 This is a diagram of the internal structure of the torque distributor. Detailed Implementation

[0119] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0120] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0121] 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 phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0122] Example 1

[0123] Reference Figures 1-6 As one embodiment of the present invention, a method for optimizing a refrigeration compressor based on a modular disc motor is provided, comprising:

[0124] 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.

[0125] Working principle of a refrigeration compressor:

[0126] A reciprocating compressor compresses gas by the reciprocating motion of a piston within a cylinder. Each stage (or phase) consists of three basic processes: intake, compression, and exhaust. These processes combine to form a complete cycle.

[0127] Intake phase: The piston moves backward, the pressure inside the cylinder decreases, causing gas (such as refrigerant) to enter the cylinder through the intake valve.

[0128] Compression stage: The piston moves forward, the gas in the cylinder is compressed, the intake valve is closed and the exhaust valve is opened, so that the gas is compressed to a high pressure state.

[0129] Exhaust stage: After compression, the gas is discharged from the cylinder through the exhaust valve and sent to the condenser, thus completing one cycle.

[0130] Mathematical model of refrigeration compressor:

[0131]

[0132] λ=λ V λ p λ T λ t (2)

[0133] Where λ is the gas delivery coefficient; Q is the refrigerant flow rate; V v V represents the compressor volume. i This refers to the inlet specific volume of the refrigeration compressor.

[0134]

[0135] Among them, V g Where: is the compressor volumetric gas delivery capacity; D is the cylinder diameter; S is the piston path; n is the rotational speed; Z is the number of compressor cylinders;

[0136]

[0137] Where c is the relative clearance volume; m is the polytropic index; and ε is the compression ratio.

[0138]

[0139] Where, λ p λ is the pressure coefficient; TAs a temperature coefficient, the effect of temperature increase during the intake process on the delivery coefficient is an important consideration. When the refrigerant enters the compressor, it exchanges heat with the hot compressor walls and the surrounding air. This results in a lower density of the working fluid entering the compressor because it is heated and thus expands. In contrast, the working fluid has a higher density at the compressor inlet because it has not yet been heated.

[0140]

[0141] Where, λ t T1 is the leakage coefficient; T2 is the compressor discharge temperature.

[0142]

[0143] Where T1 is the intake temperature; p d p is the exhaust pressure; s P is the inhalation pressure; i To indicate power, the power is the work done by the gas on the piston within the cylinder during the compression process of the compressor. This is a theoretical power value that reflects the internal energy conversion during the change of gas state, without considering actual mechanical losses.

[0144]

[0145] Where, η m For mechanical efficiency; η i For electrical efficiency; P m Input power;

[0146] Q = q m (h e -h V (10)

[0147] Where, q m For mass flow rate; h e h is the enthalpy value at the evaporator outlet. v This refers to the enthalpy value at the evaporator inlet.

[0148]

[0149] Where COP is the coefficient of performance; Q is the cooling capacity.

[0150] Reference Figure 1 This is a schematic diagram of the refrigeration device in operation.

[0151] Furthermore, the high-temperature, high-pressure refrigerant vapor discharged from the reciprocating compressor first enters the oil separator for oil-gas separation, then condenses into liquid through the air-cooled condenser and enters the liquid receiver for storage. Next, the liquid refrigerant passes through a dryer filter and a regenerator to remove impurities and moisture, improving refrigeration efficiency. After being throttled and depressurized by the thermostatic expansion valve, it becomes a low-temperature, low-pressure liquid refrigerant. Subsequently, the refrigerant enters the air cooler inside the cold storage, absorbs heat from the storage, and vaporizes into low-temperature, low-pressure refrigerant vapor, finally returning to the compressor for compression, restarting the cycle.

[0152] It should be noted that the modular disc motor structure is as follows:

[0153] Modular disc motor structures typically consist of a double-stator single-rotor structure that is an integer multiple of the original structure. Figure 2 A schematic diagram of a modular motor with a dual-stator, single-rotor structure is shown. By coaxially connecting multiple sets of axial sub-motors, greater torque and power can be obtained without increasing the complexity of the mechanical structure, better meeting the needs of high-probability compressors.

[0154] Furthermore, taking a disc motor with a single dual-stator, single-rotor structure as an example:

[0155] The flux linkage equation is:

[0156]

[0157] In the formula L AA L BB L CC The self-inductance of the three-phase windings A, B, and C are respectively; M AB and M BA M AC and M CA M BC and M CB ψ represents the mutual inductance between stator windings AB, AC, and BC, respectively; r θ is the rotor permanent magnet flux linkage; θ is the rotor position angle.

[0158] The voltage equation can be derived from the flux linkage equation as follows:

[0159]

[0160] In the formula u A u B and u C i A i B and i C These represent the stator voltage and stator current in the ABC coordinate system, respectively, ψ A ψ B and ψ C Let R be the stator flux linkage in the ABC coordinate system. sThis is the stator winding resistance.

[0161] Electromagnetic torque equation:

[0162] T e =N p ψ s ×i s (14)

[0163] In the formula T e N is the electromagnetic torque. p ψ is the number of pole pairs of the torque winding; s i is the stator flux linkage vector; s This is the stator current vector.

[0164] Equations of motion:

[0165]

[0166] In the formula, J is the moment of inertia of the motor; B is the viscosity coefficient; T L ω is the load torque. r ω is the rotor angular velocity.

[0167] d-axis and q-axis voltage equations:

[0168]

[0169] In the formula, u d i d The components of the stator voltage and current vectors along the d-axis; u q i q These are the components of the stator voltage and current vectors on the q-axis.

[0170] d-axis and q-axis stator flux linkage equations:

[0171]

[0172] In the formula, ψf is the flux linkage of the permanent magnet's 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 Let be the self-inductance of the d-axis and q-axis coils.

[0173] Electromagnetic torque equation:

[0174]

[0175] Since the disc motor selected in this invention does not have a salient pole effect (it is an existing structure of motor that does not have a salient pole effect, so it will not be described in detail), and L d =L q Therefore, we obtain the torque equation:

[0176]

[0177] When modeling and analyzing a disc motor system with two stators and one rotor, since both stators drive one rotor disc, structurally it is equivalent to two motors shaft-connected. Therefore, ωr = ωr1 = ωr2. Because the reference coordinate systems corresponding to the stators are consistent, the shaft-connected motors can be analyzed under the same d, q reference coordinate system. The total electromagnetic torque can be obtained as:

[0178]

[0179] It should be noted that the modular disc motor direct drive system is as follows:

[0180] According to the requirements of the host computer, the frequency converter converts the input voltage into a pulse width modulation voltage with adjustable amplitude and frequency, thereby controlling the speed of the disc motor and driving the compressor to operate efficiently.

[0181] To construct a refrigeration compressor system, this invention employs a modular disc motor system with dual stators and a single rotor. The motor features an axial flux design, resulting in high power density and high efficiency, making it suitable for compact and high-performance applications. Figure 3 This demonstration showcases a refrigeration / nitrogen / hydrogen compressor drive system using a single drive motor. A PC-based host computer connects to the motor controller via a CAN bus. The host computer sends torque setpoints, forward / reverse commands, etc., to the motor controller, which in turn feeds back data such as motor speed, current, voltage, and temperature to the host computer. After the motor drives the compressor, parameters such as temperature and pressure are fed back to the host computer.

[0182] For multi-motor systems, a collaborative optimization controller needs to be introduced between the host computer and the motor controller. Figure 4 This paper demonstrates the topology of a modular dual-stator single-rotor disc motor drive system. One motor controller manages one module's motor, effectively controlling two motor modules. The host computer needs a collaborative optimization controller to receive control signals and torque data, which are then transmitted to the motor controller via an appropriate communication protocol (such as CAN communication) for independent control of each motor module. The motor controller then returns current motor status information to the collaborative optimization controller, which integrates the data before sending it to the PC host computer.

[0183] 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 is easier to modularize and stack the system, so that the various sub-motor modules do not affect each other.

[0184] Reference Figure 6 For refrigeration compressors, higher power is required under high-load operating conditions. Therefore, modular disc motors are used to achieve a greater cooling effect. Furthermore, the number of modules activated can be flexibly determined based on changes in load and operating conditions, or the number of modular motors can be increased. Modules operate simultaneously to provide sufficient torque; while under low-load or partial-load conditions, only one module can be activated to save energy.

[0185] It should be noted that, referring to Figures 3-6 The overall control strategy allows the modular disc motor design to flexibly adjust the number of operating motors according to actual needs. Under high load conditions on the refrigeration compressor, two motor modules can be started simultaneously to meet high power demands, thereby saving energy. Simultaneously, adjustments are made to torque distribution; traditional control methods distribute torque equally between the two motor modules to drive the refrigeration compressor.

[0186] S2: Employs sensorless control technology, achieving precise control across the entire speed range through low-speed high-frequency signal injection and medium-to-high-speed sliding mode observer method.

[0187] Sensorless control algorithm:

[0188] The application of sensorless control technology in disc motor refrigeration compressor systems is of great significance, especially in refrigeration systems. Sensorless control technology allows for precise control of the motor without the use of physical position sensors, which offers significant advantages in improving system reliability, reducing costs, and simplifying structure.

[0189] The refrigeration compressor control system is designed for long-term, efficient, and stable operation in the mid-to-high frequency range, with lower performance requirements in the low-frequency range. Its drive control strategy involves accelerating the compressor motor to a suitable frequency range upon startup, followed by long-term stable operation in the mid-to-high frequency range. Combining sensorless control technology, this paper proposes the following full-speed-range operation control strategy: a high-frequency signal injection method is used in the low-speed range; a sliding mode observer method is used in the mid-to-high-speed range to achieve smooth operation.

[0190] (1) High-frequency voltage injection method is used in the low-speed domain:

[0191] Injected high-frequency rotating signal frequency ω h Compared to the fundamental angular frequency ω of the power supply e Much larger, and simultaneously injecting symmetrical three-phase high-frequency rotating voltage into the motor stator windings can transform it into a sinusoidal waveform with a 90° phase difference injected in a two-phase stationary coordinate system, with the high-frequency voltage amplitude set to V. inj .

[0192]

[0193] Among them, u αh u βh It is a high-frequency injection voltage;

[0194] Due to the relatively small impact of back electromotive force on the low-speed domain motor cross-coupling terms, the equivalent model is shown in the figure below:

[0195]

[0196] Among them, u dh u qh It is the high-frequency voltage after coordinate transformation; C 2r / 2s i is a simplified matrix for coordinate transformation; dh i qh It is the high-frequency current after coordinate transformation; θ is the angle of the corresponding coordinate transformation;

[0197] When written in vector form:

[0198]

[0199] in, High-frequency current vector form; L is the average inductance (L d +L q ) / 2; ΔL is the half-differential inductance (L d -L q ) / 2;

[0200] Define the forward phase sequence magnitude I cp and negative phase sequence amplitude I cn The modulation of high-frequency rotating current is affected by the salient pole position information of the motor, and the negative phase sequence component contains rotor angular position information. Therefore, this component is modulated using the heterodyne method after filtering.

[0201]

[0202] Where ε is the location information component;

[0203] Finally, the Luenberger observer is used to estimate the rotor position.

[0204] (2) A novel sliding mode observer method is used in the medium-to-high speed domain:

[0205] Transform the voltage equation into a current state equation:

[0206]

[0207] Among them, i α i β These are the currents along the α and β axes, respectively; A is the coefficient matrix; u α u β These are the voltages along the α and β axes, respectively; eα e β These are the back electromotive forces along the α and β axes, respectively; ω e It is the electric angular velocity; Rs is the equivalent resistance of the system; L d It is the d-axis inductance of the system; L q It is the q-axis inductance of the system;

[0208] Assume the error current is defined as i s =i 实际 -i 参考 The error voltage is defined as u s The back electromotive force error is e s Therefore, based on the current state equation, the dynamic equation is:

[0209]

[0210] An improved integral sliding surface is constructed, denoted as:

[0211]

[0212] Where c is a constant, s is the improved integral sliding surface, and t is time;

[0213] It should be noted that the improved integral sliding surface enhances the system's anti-interference capability. By introducing an integral term for the current error, high-frequency noise and interference can be better filtered out, improving the system's robustness. The integral term helps smooth the error response, avoids abrupt changes in transient response, and improves system stability. It effectively reduces chattering caused by high-frequency noise, making the system more stable. It also solves the noise sensitivity problem of traditional sliding mode control.

[0214] Substituting the system dynamic equations, we get:

[0215]

[0216] According to the sliding mode equivalent control principle, as the system approaches the sliding surface, |s| gradually approaches 0. It will also gradually approach 0; in order to ensure stable operation of the system on the sliding surface, it is assumed that... Substituting matrix A into the equation, we get:

[0217]

[0218] Because the selected axial motor does not have salient pole effect, and L d =L q Ignore coupling term ω e (L d -L q From this, we can obtain:

[0219] u eq =(Rs -cL d )i s +e s (30)

[0221] Among them, u eq It is the equivalent control part of sliding mode;

[0222] It should be noted that equivalent control u eq This represents the equivalent dynamic behavior of the system near the sliding surface.

[0223] In the formula, R s It is the equivalent resistance of the system, L d It is the d-axis inductance of the system, i s It is the current error, e s This refers to the back electromotive force error. Equivalent control allows for a more precise description of the system's dynamic behavior on the sliding surface, providing a basis for control law design. Through equivalent control calculations, the sliding mode controller can more accurately track and control the system's dynamic behavior, improving the accuracy and effectiveness of sliding mode controller design.

[0224] The design improves the sliding mode convergence rate, and appropriately adjusts the weights of k1 and k2 to reduce the high-frequency chattering problem caused by traditional sliding mode:

[0225]

[0226] That is, the sliding mode switching part; where k1>0, k2>0, 1>α>0; and sat(s) is the saturation function.

[0227]

[0228] Where p>0; σ represents the boundary layer;

[0229] It should be noted that traditional sliding mode control generates high-frequency chattering near the switching surface, affecting system stability. Traditional sliding mode control also lacks robustness to parameter variations and external disturbances. This is addressed by introducing a nonlinear term |s|. α sat(s) can effectively suppress high-frequency jitter.

[0230] To verify that the improved sliding mode reaching law can satisfy the stability required for sliding mode control, we use the Lyapunov equation:

[0231]

[0232] Conditions met:

[0233]

[0234] Where V is the Lyapunov equation; To find the derivative of the Lyapunov equation;

[0235] The system is asymptotically stable on the sliding surface and the sliding mode exists;

[0236] Combining the improved sliding mode switching part and the equivalent control law part yields the improved control law Z. eq :

[0237] Z eq =(R s -cL d )i s +e s -(-k1|s| α sat(s)-k2s) (35)

[0238] It should be noted that the equation combines equivalent control and a novel sliding mode control law, defining the final control law Z. eq ; Includes the dynamic change term i of the current s And improved sliding mode control terms. Overall, the goal is to achieve a better control strategy, improve the overall performance of the system, and maintain high performance and high stability when facing complex operating conditions.

[0239] S3: A torque co-optimization control strategy based on the MOPSO algorithm to achieve optimal torque allocation under different speeds and torques.

[0240] MOPSO torque distribution algorithm:

[0241] Taking the dual-stator single-rotor disc motor studied in this invention as an example, the total output torque is T, ranging from [0, 500 Nm], where the output torques of the two motor modules are T1 and T2, both ranging from [0, 250 Nm]. The motor torque directly affects the compressor's operation, thus affecting the refrigerant flow and compression process within the system, and consequently, 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, allowing the establishment of a relationship model between the intake pressure, exhaust pressure, and motor torque:

[0242] p d =k·T·p s (36)

[0243] Where k is a constant representing the proportionality coefficient between torque and compression ratio.

[0244] The MOPSO algorithm maintains a set of individual best solutions (PBest) and global best solutions (GBest) for each particle, utilizing an archive to avoid losing optimal solutions. In the initial phase, basic parameters are set and the particle swarm is initialized; fitness is calculated and the results saved to the archive. Subsequently, the particle's velocity and position are updated based on the current individual best and global best solutions, fitness is recalculated, and individual best solutions are updated again. By updating the archive, a global best solution is selected, and the algorithm iterates repeatedly until a termination condition is met to find the optimal solution space exploration path.

[0245] The basic steps of MOPSO are as follows:

[0246] The optimization steps for a refrigeration compressor system based on the particle swarm optimization algorithm are as follows:

[0247] 1. Set algorithm parameters: Set learning factors c1, c2 and weight coefficients w, etc.; initialize the population to ensure that the number of particles, position X and velocity V are all within the limits; set the Archive set to ensure that better non-dominated solutions are added during the iteration process, and set the maximum archive size; let the number of iterations k = 1.

[0248] 2. Fitness Calculation: Calculate the fitness value for each particle.

[0249] 3. Update the individual historical best particle Pbest: Update the individual best position Pbest for each particle.

[0250] 4. Filtering non-dominated solution set: Filter non-dominated solution set according to particle dominance relationship and store it in Archive set, while calculating particle density information.

[0251] 5. Select the globally optimal particle Gbest: In the Archive set, select the globally optimal particle Gbest based on the particle density information provided by the adaptive mesh method.

[0252] 6. Update position X and velocity V: Update the particle's position and velocity according to the following formulas, so that the particle changes its position in the search space by following Pbest and Gbest, and continuously approaches the global optimum:

[0253] v i (t+1)=w·v i (t)+c1·r1·(PBest i -x i (t))+c2·r2·(GBest-x i (t)) (37)

[0254] x i (t+1)=x i (t)+vi (t+1) (38)

[0255] 7. Update Archive Collection: Unlock the new generation of excellent non-inferior collections into the archive.

[0256] 8. Determine the termination condition: Determine if the algorithm can terminate. If the termination condition is met, exit the loop and proceed to step 9; otherwise, enter the next loop to continue the optimization and return to step 2.

[0257] 9. Output Optimal Solution: Output a set of Pareto optimal solutions in the Archive set.

[0258]

[0259] As can be seen from equation (11), to find the maximum value of the performance coefficient, we need to find the minimum value of the input power.

[0260] To improve system efficiency, the total input power must be reduced. To find the maximum value of motor efficiency η, we need to find the minimum value of the denominator of equation (23).

[0261] 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 minimizing Pm under the condition of constant refrigeration capacity of the refrigeration compressor and how to allocate the torque ratio of the two sets of motor modules under a given torque to minimize the value of A.

[0262] The objective function can be obtained as shown below:

[0263]

[0264] In the optimization of a disc-type electric drive system for a refrigeration compressor, the objective function F(x) comprehensively considers the refrigeration performance of the compressor and the efficiency of the disc motor. Weighting coefficients ω1 and ω2 are the weighting coefficients for adjusting the compressor power and motor power, respectively. t1 and t2 are the ratios of single-disc torque to total torque. The optimization process also needs to satisfy a series of constraints gj(x), such as inlet temperature, outlet temperature, refrigerant flow rate, compressor volumetric gas delivery capacity, and torque limits, while ensuring that the variable inlet and outlet pressures are within their upper and lower limits.

[0265] Finally, with the particle swarm size set to 10 and the maximum number of iterations to 50, the optimal value was found after 500 iterations.

[0266] In summary, the torque collaborative optimization control strategy based on the multi-objective particle swarm optimization algorithm can achieve optimal torque allocation for the two sets of motor modules in real time under different motor speeds and torques. Compared to the torque equal distribution control strategy, the collaborative optimization control strategy based on the particle swarm optimization algorithm can allocate torque according to the optimal efficiency of the refrigeration compressor and the optimal efficiency of the disc motor. The torque allocation ratio is not fixed, which can effectively improve system efficiency.

[0267] The above is an illustrative scheme of a refrigeration compressor optimization method based on a modular disc motor according to this embodiment. It should be noted that the technical solution of this refrigeration compressor optimization system based on a modular disc motor belongs to the same concept as the technical solution of the aforementioned refrigeration compressor optimization method based on a modular disc motor. Details not described in detail in the technical solution of the refrigeration compressor optimization system based on a modular disc motor in this embodiment can be found in the description of the technical solution of the aforementioned refrigeration compressor optimization method based on a modular disc motor.

[0268] This embodiment also proposes an optimized refrigeration compressor system based on a modular disc motor, including:

[0269] Create modules, control modules, allocate modules;

[0270] The establishment module is used to establish a mathematical model of the refrigeration compressor based on the reciprocating motion of the piston in the cylinder to complete the gas compression process.

[0271] The control module is used to achieve precise control across the entire speed range by employing sensorless control technology, through low-speed high-frequency signal injection and medium-to-high-speed sliding mode observer method.

[0272] The allocation module is used for torque cooperative optimization control strategy based on MOPSO algorithm to perform optimal torque allocation under different speeds and torques.

[0273] This embodiment also provides a computing device suitable for optimizing refrigeration compressors based on modular disc motors, including:

[0274] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the optimized method for a refrigeration compressor based on a modular disc motor, as proposed in the above embodiments.

[0275] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the optimization method for a refrigeration compressor based on a modular disc motor as proposed in the above embodiments.

[0276] The storage medium proposed in this embodiment and the optimization method for refrigeration compressors based on modular disc motors proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0277] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0278] Example 2

[0279] As a second embodiment of the present invention, an optimization method for a refrigeration compressor based on a modular disc motor is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0280] To test system performance, 20 power points were selected at 10kW intervals from the refrigeration compressor input power range of 10kW to 210kW. At each speed point, 14 torque points were selected at 10Nm intervals from the output torque range of 10Nm to 150Nm. Corresponding to different speeds and torques, a total of 280 efficiency sampling points were set. The output torque T and refrigeration compressor input power pm were collected at each efficiency point to obtain a pulse spectrum.

[0281] The area with a system efficiency of 93% or higher using the torque-sharing control system accounts for 18.25%, the area with a system efficiency of 95% or higher accounts for 8.80%, and the area with a system efficiency of 97% or higher accounts for 0.52%. If the torque distribution method optimized using the MOPSO algorithm is applied, the area with a system efficiency of 93% or higher accounts for 22.11%, the area with a system efficiency of 95% or higher accounts for 14.08%, and the area with a system efficiency of 97% or higher accounts for 3.25%.

[0282] Analysis of system performance test data shows that the torque distribution optimized using the MOPSO algorithm exhibits a significant efficiency improvement compared to the traditional torque sharing 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%; and in the efficiency range above 97%, the area increased by 2.73%. These data demonstrate that MOPSO algorithm optimization can significantly improve system operating efficiency under different operating conditions, making the system more stable over a wider efficiency range, thereby improving overall system performance and energy efficiency.

[0283] Table 1 Comparison of system efficiency using torque split control and MOPSO algorithm torque distribution control

[0284]

[0285] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0286] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0287] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0288] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0289] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An optimization method for a refrigeration compressor based on a modular disc motor, characterized in that: include, A mathematical model of a refrigeration compressor is established based on the reciprocating motion of the piston in the cylinder to complete the gas compression process. Employing sensorless control technology, precise control across the entire speed range is achieved through low-speed, high-frequency signal injection and a medium-to-high-speed sliding mode observer method. A torque cooperative optimization control strategy based on the MOPSO algorithm is used to achieve optimal torque allocation under different speeds and torques. The medium-to-high-speed sliding mode observer method includes a sliding mode observation method with an improved structure: The voltage equation is transformed into a current state equation, which can be expressed as: Among them, i α i β These are the currents along the α and β axes, respectively; A is the coefficient matrix; u α u β These are the voltages along the α and β axes, respectively; e α e β These are the back electromotive forces along the α and β axes, respectively; ω e It is the electric angular velocity; Rs is the equivalent resistance of the system; L d It is the d-axis inductance of the system; L q It is the q-axis inductance of the system; Assume the error current is defined as =i 实际 -i 参考 The error voltage is defined as u s The back electromotive force error is e s Therefore, based on the current state equation, the dynamic equation is: An improved integral sliding surface is constructed, denoted as: Where c is a constant, s is the improved integral sliding surface, and t is time; Taking the time derivative of the integral sliding surface, we get: in, It is the result of differentiating the integral sliding surface; Substituting the system dynamic equations, we get: According to the sliding mode equivalent control principle, as the system approaches the sliding surface, |s| gradually approaches 0. It will also gradually approach 0; in order to ensure stable operation of the system on the sliding surface, it is assumed that... =0, substituting matrix A, we get: Because the selected surface-mount disc motor does not have salient pole effect, and L d =L q， Ignore coupling term ω e (L d -L q From this, we can obtain: Among them, u eq It is the equivalent control part of sliding mode; The design improves the sliding mode convergence rate, and appropriately adjusts the weights of k1 and k2 to reduce the high-frequency chattering problem caused by traditional sliding mode: That is, the sliding mode switching part; where k1>0, k2>0, 1>α>0; and sat(s) is the saturation function. Where p>0; For boundary layer; To verify that the improved sliding mode reaching law can meet the stability requirements of sliding mode control. From the Lyapunov equation: Conditions met: Where V is the Lyapunov equation; To find the derivative of the Lyapunov equation; The system is asymptotically stable on the sliding surface and the sliding mode exists; Combining the improved sliding mode switching part and the equivalent control law part yields the improved control law Z. eq : ； The torque cooperative optimization control strategy based on the MOPSO algorithm includes optimal torque allocation under different speeds and torques, including: Based on a disc motor with a dual stator and a single rotor, 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 rate Q and the compression ratio r and the motor torque T. A model is established to represent the relationship between the intake pressure, exhaust pressure, and motor torque. Where k is a constant representing the proportionality coefficient between torque and compression ratio; Optimization of refrigeration compressor systems based on particle swarm optimization algorithm includes, Set algorithm parameters: Set learning factors c1, c2 and weight coefficient w; initialize the population to ensure that the number of particles, position X and velocity V are all within the limits; set the Archive set to ensure that better non-dominated solutions are added during the iteration process, and set the maximum archive size; set the number of iterations k=1; Calculate the fitness value for each particle; Update the individual optimal position Pbest for each particle; The non-dominated solution set is selected based on the particle dominance relationship and stored in the Archive set, while the particle density information is calculated. In the Archive set, the globally optimal particle Gbest is selected based on the particle density information provided by the adaptive grid method. The particle's position and velocity are updated according to the following formula, allowing the particle to change its position in the search space by following Pbest and Gbest, and continuously approach the global optimum: Where, x i (t) represents the position of the particle at time t; v i (t) represents the velocity of the particle at time t; x i (t+1) represents the position of the particle at time t+1; v i (t+1) represents the particle velocity at time t+1; r1 and r2 represent different compression ratios; Update the Archive collection to include the new generation of excellent non-inferior items in the archive. If the termination condition is met, the loop exits and proceeds to the next step; if not, the loop continues to search for optimization. Output a set of Pareto optimal solutions in the Archive set: Where A represents a set of optimal solutions to the objective; P o η is the output power of the motor; η is the system efficiency; η1 and η2 are the efficiencies of the two sets of motor modules respectively; a1 and a2 are the torque distribution coefficients of the two sets of motor modules respectively; T is the total torque of the motor; ω is the speed of the motor. The objective function for optimizing a refrigeration compressor system is expressed as: The objective function F(x) comprehensively considers the refrigeration performance of the compressor and the efficiency of the disc motor. The weighting coefficients ω1 and ω2 are the weighting coefficients for adjusting the compressor power and motor power, respectively. t1 and t2 are the ratios of single disc torque to total torque. The optimization process satisfies a series of constraints g. j (x), including but not limited to intake temperature, exhaust temperature, refrigerant flow rate, compressor volumetric gas delivery capacity and torque limit, and ensuring that the variable intake and exhaust pressures are between their upper and lower limits; The final particle swarm size was set to 10, and the maximum number of iterations was set to 50. After 500 iterations, the optimal value was found.

2. The optimization method for a refrigeration compressor based on a modular disc motor as described in claim 1, characterized in that: The reciprocating motion includes three basic processes in each stage: the intake stage, the compression stage, and the exhaust stage. These processes are combined to form a complete cycle. The establishment of the mathematical model for the refrigeration compressor includes: Where λ is the gas delivery coefficient; Q is the refrigerant flow rate; V v V represents the compressor volume; i This refers to the inlet specific volume of the refrigeration compressor. Among them, V g Where: is the compressor volumetric gas delivery capacity; D is the cylinder diameter; S is the piston path; n is the rotational speed; Z is the number of compressor cylinders; Where c is the relative clearance volume; m is the polytropic index; and ε is the compression ratio. Where, λ p λ is the pressure coefficient; T Temperature coefficient; Where, λ t T1 is the leakage coefficient; T2 is the compressor discharge temperature; Where T1 is the intake temperature; p d p is the exhaust pressure; s P is the inhalation pressure; i To indicate power; Where, η m For mechanical efficiency; η i For electrical efficiency; P m Input power; Where, q m For mass flow rate; h e h is the enthalpy value at the evaporator outlet. v This refers to the enthalpy value at the evaporator inlet. Where COP is the coefficient of performance; Q is the cooling capacity.

3. The method for optimizing a refrigeration compressor based on a modular disc motor as described in claim 2, characterized in that: The intake phase includes the piston moving backward, the pressure inside the cylinder decreasing, causing the gas entering the cylinder to enter through the intake valve; The compression stage includes the piston moving forward, the gas in the cylinder being compressed, the intake valve being closed and the exhaust valve being opened, so that the gas is compressed to a high pressure state. The exhaust phase includes the process where, after compression, the gas is discharged from the cylinder through the exhaust valve and delivered to the condenser, thus completing one cycle.

4. The method for optimizing a refrigeration compressor based on a modular disc motor as described in claim 3, characterized in that: The low-speed, high-frequency signal injection includes, Injected rotating high-frequency signal frequency Frequency relative to the fundamental frequency of the power supply Simultaneously, a symmetrical three-phase high-frequency rotating voltage is injected into the stator winding of the motor, transforming it into a sinusoidal waveform with a 90° phase difference in a two-phase stationary coordinate system, and setting the amplitude of the high-frequency voltage to be [value missing]. , is represented as: Among them, u αh u βh It is a high-frequency injection voltage; Since the back electromotive force has a small impact due to the cross-coupling terms in the low-speed domain motor, the equivalent model is: Among them, u dh u qh It is the high-frequency voltage after coordinate transformation; C 2r / 2s i is a simplified matrix for coordinate transformation; dh i qh It is the high-frequency current after coordinate transformation; θ is the angle of the corresponding coordinate transformation; In vector form: in, High-frequency current vector form; L is the average inductance (L d +L q ) / 2; ΔL is the half-differential inductance (L d -L q ) / 2; Define forward phase sequence magnitude and negative phase sequence amplitude The modulation of the high-frequency rotating current is affected by the salient pole position information of the motor, and the negative phase sequence component contains the rotor angular position information; therefore, after filtering, this component is modulated using the heterodyne method, expressed as: Where ε is the location information component; The rotor position is estimated using a Luenberger observer.

5. A refrigeration compressor optimization system based on a modular disc motor, employing the refrigeration compressor optimization method based on a modular disc motor as described in any one of claims 1 to 4, characterized in that, Includes: a setup module, a control module, and an allocation module; The establishment module is used to establish a mathematical model of the refrigeration compressor based on the reciprocating motion of the piston in the cylinder to complete the gas compression process. The control module is used to achieve precise control across the entire speed range by employing sensorless control technology, through low-speed high-frequency signal injection and medium-to-high-speed sliding mode observer method. The allocation module is used for torque cooperative optimization control strategy based on MOPSO algorithm to perform optimal torque allocation under different speeds and torques.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the refrigeration compressor optimization method based on a modular disc motor as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the refrigeration compressor optimization method based on a modular disc motor as described in any one of claims 1 to 4.

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