Periodic short-time high-overload motor temperature field analysis method, system and device and storage medium

Abstract

The objective of the invention is to provide an analysis method, system and device for a periodic short-time high-overload motor temperature field, and a storage medium, and the method comprises the steps: building an electromagnetic torque model considering the temperature of a permanent magnet, the nonlinearity of a magnetic circuit, and the cross coupling effect of a rectangular axis; a lumped parameter thermal network model considering temperature and permanent magnet demagnetization is established based on the proposed electromagnetic torque model, and temperature rise calculation of the permanent magnet motor under the periodic short-time high overload condition is realized. According to the method, the influences of the temperature effect, magnetic circuit nonlinearity and permanent magnet demagnetization are considered, and the calculation precision of the transient temperature field of the periodic short-time high-overload motor can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of motors, and in particular relates to a method, system, device and storage medium for analyzing the temperature field of a periodic short-term high-overload motor. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) boast compact design and lightweight construction, exceptional output torque and power density, and the ability to withstand short-term high overloads. Consequently, they have gained widespread adoption and recognition in a wide range of applications, including robotic joints. However, the motors used in robotic joints often operate under conditions such as frequent starts and stops, acceleration and deceleration, variable loads, and multiple overloads. These challenging operating conditions subject the motors to frequent transient high currents and alternating hot and cold cycles, which in turn impact their output characteristics, overall efficiency, and temperature rise. Furthermore, ignoring the temperature of the motor's permanent magnets under high overload conditions can increase the risk of irreversible demagnetization. This is because after demagnetization, the motor's input current rises sharply to maintain constant output torque, leading to a rapid increase in motor copper losses and elevated temperatures in the windings and rotor permanent magnets, exacerbating the risk of irreversible demagnetization. Therefore, to avoid this problem, accurately calculating the temperature rise of PMSMs under periodic overload conditions is essential.

[0003] Currently, among motor temperature field calculation methods, the finite element method (FEM) offers high accuracy and can accurately calculate local temperatures, but it has strict boundary condition requirements and long calculation times. Computational fluid dynamics (CFD) offers high accuracy, but is computationally complex and requires high hardware. While the equivalent thermal network method (ETN) cannot calculate local temperatures, it can effectively predict overall temperatures by rationally partitioning temperature nodes. Its computational speed is relatively fast, making it widely used for predicting motor temperature rise. Analysis of motor temperature fields primarily focuses on steady-state motor operation, while little research has examined the temperature rise under cyclical overload conditions. Furthermore, most thermal network models fail to consider the effects of temperature, magnetic circuit nonlinearity, and permanent magnet demagnetization. This can prevent accurate current measurements under cyclical short-duration high-overload conditions, leading to errors in the calculation of heat sources within the thermal network and affecting the accuracy of the thermal network model's temperature rise calculations under cyclical overload conditions. Therefore, calculating the temperature rise of motors under cyclical short-duration high-overload conditions during the design phase is crucial for improving motor performance and reliability.

[0004] Chinese patent application publication number CN115270380 A discloses a method and device for real-time estimation of motor temperature rise based on an equivalent thermal network model. This method constructs an equivalent thermal network model of the motor and, based on the losses and equivalent thermal resistance of each node, establishes a heat balance equation and solves it to obtain the temperature rise of each temperature-rise node of the motor. This method incorporates the concept of heat capacity in heat transfer and adds heat capacity as a heat capacity to the motor equivalent thermal network model, thus achieving real-time estimation of the motor's temperature rise. However, this method does not consider the effects of motor magnetic circuit saturation, temperature effects, and cross-coupling between orthogonal axes on the motor's output torque during short-term high overload. Consequently, it is unable to accurately determine the motor's input current at the required torque, which in turn affects the calculation of the heat source in the lumped parameter thermal network, ultimately leading to inaccurate temperature calculation results. Summary of the Invention

[0005] In order to address the deficiencies in the above-mentioned prior art, the purpose of the present invention is to provide a method, system, device and storage medium for analyzing the temperature field of a periodic short-term high-overload motor. The method takes into account the influence of temperature effects, magnetic circuit nonlinearity and permanent magnet demagnetization, and can improve the calculation accuracy of the transient temperature field of a periodic short-term high-overload motor.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for analyzing the temperature field of a periodic short-term high overload motor, comprising:

[0008] S1, calculating the input current of the motor according to the torque demand of the motor and the electromagnetic torque model; the electromagnetic torque model is an electromagnetic torque model that takes into account the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling;

[0009] S2, inputting the input current into a lumped parameter thermal network model of the motor, calculating the heat source of each temperature node in the lumped parameter thermal network model; solving the heat balance equation of each temperature node according to the heat source, equivalent thermal resistance and heat capacity of each temperature node, and obtaining the temperature field distribution of each temperature node;

[0010] S3, calculate the temperature difference of the temperature nodes between two adjacent time steps, and determine whether the convergence error is reached based on the temperature difference of the temperature nodes. If the convergence error is not reached, obtain the current winding temperature and permanent magnet temperature based on the temperature field distribution, correct the winding resistance in the lumped parameter thermal network model based on the winding temperature, and return to S1 to recalculate the input current based on the permanent magnet temperature until the convergence error is met.

[0011] Preferably, in S1, the electromagnetic torque model is specifically:

[0012]

[0013] Where, ψ d (i d ,i q ,T),ψ q (i d ,i q , T) are the d-axis and q-axis flux linkages considering the permanent magnet temperature and cross-coupling effects; L d (i d ,i q ,T),L q (i d ,i q , T) are the d-axis and q-axis inductances considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling; L qd (i d ,i q ,T),L dq (i d ,i q , T) are the d-axis and q-axis mutual inductances considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling; ψ f (i d ,i q ,T) is the permanent magnet flux linkage considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling.

[0014] Preferably, the lumped parameter thermal network model of the motor is constructed by the following method:

[0015] The motor components are divided into temperature nodes, and each component in the motor is replaced by an equivalent thermal resistance. A lumped parameter thermal network model is established based on the divided temperature nodes and the equivalent thermal resistance, heat capacity and heat source of the temperature nodes.

[0016] Furthermore, the equivalent thermal resistance includes the conduction thermal resistance, contact thermal resistance and convection thermal resistance of the motor component; considering the motor component as a cylindrical component, the conduction thermal resistance includes the axial thermal resistance and the radial thermal resistance;

[0017] The axial thermal resistance is:

[0018]

[0019] Where: ry_a is the axial thermal conductivity of the cylindrical component; L ry is the length of the cylindrical component; r ryo and r ryi are the outer and inner diameters of the cylindrical component, respectively;

[0020] The radial thermal resistance is:

[0021]

[0022] Where: ry_r is the radial thermal conductivity of the cylindrical component;

[0023] The convection thermal resistance is:

[0024]

[0025] Where: A re is the contact area between the rotor end and the air inside the motor; h re_a is the convection heat transfer coefficient between the rotor end and the internal air; δ is the air gap thickness; λ air is the thermal conductivity of air; Nu is the Nusselt coefficient;

[0026] The contact thermal resistance includes:

[0027] The contact thermal resistance between the stator core, resin and casing is:

[0028]

[0029] Where: air is the thermal conductivity of air; L s_h The length of the contact surface between the housing and the stator; the stator outer diameter r syo As the benchmark, r hi is the inner diameter of the casing;

[0030] The contact thermal resistance between the casing and the end covers on both sides is:

[0031]

[0032] Where: σ h_ec A is the thickness of the contact gap between the housing and the end cover; h_ec is the contact area between the housing and the end cover.

[0033] Furthermore, the calculation formula of heat capacity is expressed as:

[0034] C=ρ×c×V

[0035] Where C is heat capacity; c is specific heat capacity; V is volume; ρ is density;

[0036] The calculation expression of heat capacity in the process of endothermic and exothermic is expressed as:

[0037] Q=C×ΔT

[0038] Where Q is the amount of heat and ΔT is the temperature change.

[0039] Furthermore, the heat sources include: basic copper loss, iron loss, permanent magnet eddy current loss and mechanical loss;

[0040] The expression of the basic copper loss is as follows:

[0041] P Cu =mI 2 R

[0042] Where: m is the number of motor phases, in this embodiment, m=3; I is the effective value of the winding phase current; R is the resistance value of the winding;

[0043] The expression of the iron loss is:

[0044]

[0045] Where: P Fe is the core loss; P hys is the hysteresis loss; P cla is the eddy current loss; P exc is the additional loss; k hys is the hysteresis loss coefficient; k cla is the eddy current loss coefficient; k exc is the additional loss coefficient; G Fe is the core weight; f is the frequency of the sinusoidal magnetic flux density; B m is the sinusoidal magnetic flux density amplitude;

[0046] The expression of the permanent magnet loss is as follows:

[0047]

[0048] Where: P edd is the eddy current loss of the permanent magnet; V PM is the volume of the permanent magnet; E PM is the electric field strength in the permanent magnet; J PM is the current density in the permanent magnet; σ PM is the electrical conductivity of the permanent magnet;

[0049] The formula for the mechanical loss is:

[0050] P fw =kC v ρ0πω m 3 r 4 L

[0051]

[0052] Where: k is the surface roughness of the rotor; C v is the friction coefficient, which depends on the rotor speed and the properties of the surrounding air; ρ0 is the density of the surrounding medium; ω m is the rotor angular velocity; r is the rotor radius; L is the rotor radius; F β Calculate the load for the bearing; D m is the bearing diameter; v beais the peripheral speed of the ball center.

[0053] Preferably, in S3, the winding resistance in the lumped parameter thermal network model is corrected according to the winding temperature, specifically:

[0054] R1=R0[1+(T1-T0)μ]

[0055] Where: T0 is the initial temperature of the winding; R0 is the winding resistance at T0 temperature; μ is the temperature coefficient of the copper material.

[0056] The present invention provides a system for analyzing the temperature field of a periodic short-term high-overload motor, comprising:

[0057] A current calculation module is used to calculate the input current of the motor based on the torque demand of the motor and an electromagnetic torque model; the electromagnetic torque model is an electromagnetic torque model that takes into account the effects of permanent magnet temperature, magnetic circuit nonlinearity, and orthogonal axis cross-coupling;

[0058] a temperature field distribution calculation module, configured to input the input current into a lumped parameter thermal network model of the motor, calculate the heat source of each temperature node in the lumped parameter thermal network model; solve the heat balance equation of each temperature node according to the heat source, equivalent thermal resistance, and heat capacity of each temperature node, and obtain the temperature field distribution of each temperature node;

[0059] The judgment module is used to calculate the temperature difference of the temperature nodes between two adjacent time steps, and judge whether the convergence error is reached according to the temperature difference of the temperature nodes. If the convergence error is not reached, the current winding temperature and permanent magnet temperature are obtained according to the temperature field distribution, the winding resistance in the lumped parameter thermal network model is corrected according to the winding temperature, and the permanent magnet temperature is transmitted to the current calculation module to correct the input current.

[0060] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The device is characterized in that when the processor executes the computer program, the steps of the method for analyzing the temperature field of a periodic short-term high-overload motor as described in any one of the above items are implemented.

[0061] The present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for analyzing the temperature field of a periodic short-term high-overload motor as described above.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention establishes an electromagnetic torque model that takes into account the effects of permanent magnet temperature, magnetic circuit nonlinearity, and orthogonal axis cross-coupling. Based on the proposed electromagnetic torque model, a lumped parameter thermal network model is established that takes into account temperature and permanent magnet demagnetization, enabling the calculation of the temperature rise of a permanent magnet motor under periodic short-term high overload conditions. Under short-term high overload conditions, the motor's stator core is relatively saturated and the magnetic circuit is relatively complex. The nonlinearity of the magnetic circuit can lead to an increase in load back EMF, copper loss, and changes in inductance characteristics, thereby affecting the motor's output characteristics, overall efficiency, and temperature rise. An increase in motor temperature increases the risk of permanent magnet demagnetization. After permanent magnet demagnetization, the input current rises sharply to maintain constant output torque, resulting in a rapid increase in motor copper loss and an increase in winding and rotor permanent magnet temperature, exacerbating the risk of irreversible demagnetization of the permanent magnet. Under high overload conditions, magnetic circuit saturation, armature reaction, temperature effects, and cross-coupling caused by magnetic circuit saturation have a complex impact on the electromagnetic torque. Therefore, the present invention establishes an electromagnetic torque model that takes into account the effects of permanent magnet temperature, magnetic circuit nonlinearity, and orthogonal axis cross-coupling. Based on this model, the input current under the periodic torque demand of the motor can be obtained more accurately, thereby establishing a current function and using it in the lumped parameter thermal network model to calculate the heat source (expressed as loss). The proposed temperature analysis method takes into account the influence of temperature effects and permanent magnet demagnetization, corrects the winding resistance according to the winding temperature, updates the calculated input current according to the permanent magnet temperature, and then updates the heat source, thereby more accurately calculating the temperature rise of the motor under periodic short-term high overload conditions. By considering the temperature effect of the permanent magnet, the error caused by ignoring demagnetization is avoided, and the accuracy of the temperature field calculation is improved. Although the constructed lumped parameter thermal network model cannot calculate local temperature, it can quickly predict the overall temperature change by rationally dividing the nodes and using a simplified triangular thermal network structure. The calculation speed is faster than the finite element method, making it suitable for temperature rise prediction in the design stage. This method is particularly suitable for short-term high overload conditions and can quickly and accurately simulate the temperature changes of the motor under frequent start-stop and variable load, with strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 Schematic diagram of heat flow and heat transfer form of the permanent magnet synchronous motor of the present invention.

[0066] Figure 2 It is a two-dimensional schematic diagram of the temperature node distribution of the lumped parameter thermal network model of the permanent magnet synchronous motor of the present invention.

[0067] Figure 3 This is a diagram of the electromagnetic torque model considering temperature and cross-coupling of the present invention.

[0068] Figure 4 This is the target torque diagram for a single overload cycle of the short-term high overload motor of the present invention.

[0069] Figure 5 This is a calculation flow chart of the lumped parameter thermal network model considering the demagnetization effect of permanent magnets of the present invention.

[0070] Figure 6 2 is a comparison chart of the simulation results and experimental results of the present invention. DETAILED DESCRIPTION

[0071] The following describes the embodiments of the present invention through specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention.

[0072] It should be noted that the process equipment or devices not specifically specified in the following embodiments are all conventional equipment or devices in the art.

[0073] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Furthermore, unless otherwise specified, the numbering of each method step is merely a convenient tool for identifying each method step, and is not intended to limit the order of arrangement of each method step or to define the scope of the invention. Changes or adjustments to their relative relationships, without substantially changing the technical content, should also be considered within the scope of the invention.

[0074] The present invention establishes a lumped parameter thermal network model that takes into account temperature effects and permanent magnet demagnetization, and an electromagnetic torque model that takes into account permanent magnet temperature, magnetic circuit nonlinearity, and orthogonal axis cross-coupling. Based on the established electromagnetic torque model and according to the periodic torque demand of the motor, the input current of the motor under periodic torque changes is obtained. The equivalent thermal resistance, heat source (motor loss), and heat capacity of the nodes are calculated according to the structural parameters of the motor and parameters such as the input current. The heat balance equation of each node in the lumped parameter thermal network is solved based on the equivalent thermal resistance, heat source, and heat capacity to obtain the temperature field distribution of each node. According to the node temperature change, error judgment is performed, and the input current of the motor is adjusted according to the temperature feedback of the permanent magnet, the winding resistance is adjusted according to the winding temperature, the calculated value of the heat source in the lumped parameter thermal network is corrected, and the temperature field distribution is calculated again until the temperature converges. The present invention verifies the correctness and feasibility of the lumped parameter thermal network model through experiments.

[0075] According to the periodic torque requirements of the motor, the present invention establishes an electromagnetic torque model based on the consideration of permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross-coupling, and obtains a periodic current function, thereby achieving accurate acquisition of the motor input current under periodic short-term high overload conditions, and then using it for temperature rise calculation of the lumped parameter thermal network model.

[0076] The lumped parameter thermal network model takes into account the demagnetization effect of the permanent magnet at different temperatures. Under constant torque, temperature will cause the permanent magnet to demagnetize. After the permanent magnet is demagnetized, the motor needs to increase the input current to maintain constant torque and constant speed output.

[0077] The influence of temperature on the remanence of permanent magnets is reflected in:

[0078] The residual magnetic induction intensity of the permanent magnet at the operating temperature t1 is:

[0079]

[0080] Where: B rt0 is the residual magnetic induction intensity at the initial temperature t0; L is the irreversible loss rate; α Br is the temperature coefficient of the reversible change of the permanent magnet.

[0081] The present invention also takes temperature effects into consideration. Temperature affects the copper loss of the motor. The winding resistance and input current are corrected according to the temperature change of the motor, thereby correcting the heat source.

[0082] By considering the demagnetization and temperature effects of the permanent magnet, the motor input current after the permanent magnet demagnetization can be obtained. The motor loss at different temperatures is then updated and used again in the calculation of the lumped parameter thermal network, further improving the calculation accuracy of the lumped parameter thermal network.

[0083] The present invention is further described below in conjunction with the embodiments, and the specific implementation steps of the present invention are as follows:

[0084] Step 1: Based on a three-dimensional model of a short-term high-overload motor, the motor structure is simplified according to heat transfer principles. Key components, such as the stator, rotor, windings, permanent magnets, housing, and resin, are simplified based on the motor's physical structure. Based on the required accuracy for temperature calculations, several nodes are divided, including temperature nodes and heat source nodes. These simplified components are replaced with equivalent thermal resistances. Heat conduction, convection, and radiation between components are represented by corresponding thermal resistance and heat capacity parameters. A lumped parameter thermal network model is constructed based on the divided nodes and the equivalent thermal resistances, heat sources, and heat capacities of key components. This lumped parameter thermal network model is a three-dimensional thermal network. A triangular thermal network is used for the windings of components with internal heat sources. The stator core losses are not considered in the internal heat source model because their values ​​are much smaller than the winding losses. Using a triangular thermal network reduces the number of nodes and increases computational speed.

[0085] (1) The motor is divided into the housing, stator yoke, winding, stator teeth, air gap, permanent magnet, rotor core, shaft, motor end cover, bearing and resin. Figure 1 The short-term high overload motor 1 / 2 model parts heat transfer process distribution diagram is shown. The motor parts are divided into nodes, and each component in the motor is replaced by an equivalent thermal resistance to determine the temperature node, heat source node and heat capacity node. The two-dimensional schematic diagram of the node distribution is shown in Figure 2 There are 35 temperature nodes in total, all of which are heat capacity nodes, of which temperature nodes 8-10 and 13-28 are heat source nodes, used to input the motor loss.

[0086] (2) Based on the parameters of the short-term high overload motor, preliminarily calculate the equivalent thermal resistance, heat source and / or heat capacity of each component. The equivalent thermal resistance is divided into conduction thermal resistance, contact thermal resistance and convection thermal resistance.

[0087] 1) Calculation of equivalent thermal resistance

[0088] Since most of the components inside the motor can be regarded as cylinders, based on the thermal resistance principle, the axial and radial conduction thermal resistance of each component can be solved using the cylindrical thermal resistance model.

[0089] Axial thermal resistance:

[0090]

[0091] Where: ry_a is the axial thermal conductivity of the cylindrical component; L ry is the length of the cylindrical component; r ryo and r ryi are the outer and inner diameters of the cylindrical component, respectively.

[0092] Radial thermal resistance:

[0093]

[0094] Where: ry_r is the radial thermal conductivity of the cylindrical component;

[0095] Since the motor of this embodiment is used inside a robot joint, which is a nearly enclosed space, there is essentially no axial airflow. In this case, when the gas flow velocity in the air gap is low, the airflow is in a laminar state, and when it is high, the airflow transitions to a turbulent state. The rotor surface convection heat transfer coefficient is analyzed with reference to the formula, and the air gap convection heat transfer coefficient is as follows:

[0096]

[0097] Where: δ is the air gap thickness; λ air is the thermal conductivity of air; Nu is the Nusselt coefficient, and its value is determined by formula (4).

[0098]

[0099] Where, Taylor coefficient T a for:

[0100]

[0101] Where: R e is the Reynolds number; g is the acceleration due to gravity; P r is the Prandtl number; R r is the radius of the rotating part (i.e. rotor).

[0102] The convection thermal resistance between the rotor end (shaft and rotor yoke) and the internal air is:

[0103]

[0104] Where: A re h is the contact area between the rotor end and the air inside the motor, that is, the effective contact area between the shaft and rotor yoke and the air inside the motor; re_a is the convective heat transfer coefficient between the rotor end and the internal air, which is calculated according to formulas (3)-(5).

[0105] Although the components are in close contact, there are contact gaps, which are related to the material hardness, contact pressure, contact surface roughness and atmospheric pressure of the contact surface. The air in the contact gap causes contact thermal resistance.

[0106] The contact thermal resistance between the stator core, the end potting resin and the casing is:

[0107]

[0108] Where: air is the thermal conductivity of air; L s_h The length of the contact surface between the housing and the stator; the stator outer diameter r syo As the benchmark, r hi is the inner diameter of the casing.

[0109] The contact thermal resistance between the casing and the end covers on both sides is:

[0110]

[0111] Where: σ h_ec A is the thickness of the contact gap between the housing and the end cover; h_ec is the contact area between the housing and the end cover.

[0112] 2) Calculation of node heat capacity

[0113] Since this embodiment is limited to situations that do not involve complex heat capacity calculations such as phase change phenomena and mixed gases, assuming that the specific heat capacity of the material is independent of temperature, the heat capacity calculation formula can be expressed as:

[0114] C=c×m (7)

[0115] Where C is heat capacity, c is specific heat, and m is mass.

[0116] Furthermore, the mass m can be defined by the product of the volume V and the density ρ, so the calculation formula of the heat capacity can be expressed accordingly:

[0117] C=ρ×c×V (8)

[0118] The calculation expression of heat capacity in the process of heat absorption and heat release can be expressed as:

[0119] Q=C×ΔT (9)

[0120] Where Q is the amount of heat and ΔT is the temperature change.

[0121] This embodiment mainly optimizes the configuration of heat capacitance by adjusting the distribution of thermal resistance, thereby providing a more suitable environment for calculating heat exchange related to heat capacitance.

[0122] 3) Calculation of heat source

[0123] The operating frequency of the motor in this embodiment is not very high, the diameter of the winding wire is not large, and the skin effect under the fundamental current can be ignored. Therefore, the additional copper loss is no longer considered. Only the basic copper loss caused by the phase current on the wire resistance is calculated. The expression of the basic copper loss is as follows:

[0124] P Cu=m1I 2 R1 (10)

[0125] Wherein: m1 is the number of motor phases, in this embodiment m1 = 3; I is the effective value of the winding phase current; R1 is the resistance value of the winding at temperature T1.

[0126] The relationship between the resistance and temperature of pure copper is usually described by the temperature coefficient of resistance. The expression of the winding resistance R1 at temperature T1 can be written as:

[0127] R1=R0[1+(T1-T0)μ] (11)

[0128] Where: T0 is the initial temperature; R0 is the resistance at T0 temperature; μ is the temperature coefficient of copper material.

[0129] R0 is calculated as follows:

[0130]

[0131] Where: ρ1 is the resistivity of copper; L av is the average full turn of the winding; N is the number of turns of the winding; N t is the number of parallel windings; a is the number of parallel branches; d is the diameter of the copper wire.

[0132] The iron loss of the motor rotor is usually separated by a formula, which includes hysteresis loss, eddy current loss and additional loss. The expression of iron loss is:

[0133]

[0134] Where: P Fe is the iron loss; P hys is the hysteresis loss; P cla is the eddy current loss; P exc is the additional loss. hys is the hysteresis loss coefficient; k cla is the eddy current loss coefficient; k exc is the additional loss coefficient; G Fe is the core weight; f is the frequency of the sinusoidal magnetic flux density; B m is the amplitude of the sinusoidal magnetic flux density.

[0135] The motor of this embodiment uses neodymium iron boron permanent magnets embedded in the outer surface of the rotor. The cause of its loss is basically the same as that of the rotor core, which is caused by the harmonic magnetic field generated by the time harmonics in the current and the space harmonics caused by the stator slots. Its expression is as follows:

[0136]

[0137] Where: P edd is the eddy current loss of the permanent magnet; VPM is the volume of the permanent magnet; E PM is the electric field strength in the permanent magnet; J PM is the current density in the permanent magnet; σ PM is the electrical conductivity of the permanent magnet.

[0138] The motor of this embodiment adopts a natural cooling method. Therefore, the mechanical losses generated during operation mainly include wind friction loss and bearing loss, which are obtained by the following empirical formula:

[0139]

[0140] Where: k is the surface roughness of the rotor; C v is the friction coefficient, which depends on the rotor speed and the properties of the surrounding air; ρ0 is the density of the surrounding medium; ω m is the rotor angular velocity; r is the rotor radius; l is the length of the rotor; F β Calculate the load for the bearing; D m is the bearing diameter; v bea is the peripheral speed of the ball center.

[0141] Step 2: Establish an electromagnetic torque model that takes into account permanent magnet temperature, magnetic circuit nonlinearity, and orthogonal axis cross-coupling

[0142] The conventional linear analysis model of permanent magnet synchronous motor assumes that the magnetic permeability of silicon steel sheet is infinite, that is, the magnetic circuit saturation and cross coupling are ignored. The magnetic flux equation in the d and q axis coordinate system is:

[0143]

[0144] Where, ψ d , ψ q are the d-axis and q-axis magnetic fluxes respectively; L d , L q are the d-axis and q-axis inductances respectively; i d 、i q are d-axis and q-axis currents respectively; ψ f is the permanent magnet flux.

[0145] The electromagnetic torque equation of the motor is:

[0146]

[0147] Where p is the number of pole pairs of the motor.

[0148] When the motor is in a high overload condition, the magnetic circuit saturation and cross-coupling inside it are mainly manifested as the orthogonal axis inductance and permanent magnet flux being affected by the orthogonal axis current. That is, the two are in a nonlinear functional relationship. Therefore, the flux equation of the present invention, which takes into account the effects of permanent magnet temperature, magnetic circuit nonlinearity, and orthogonal axis cross-coupling, is rewritten as:

[0149]

[0150] Where, ψ d (i d ,i q ,T),ψ q (i d ,i q , T) are the d-axis and q-axis flux linkages considering the permanent magnet temperature and cross-coupling effects; L d (i d ,i q ,T),L q (i d ,i q , T) are the d-axis and q-axis inductances considering the permanent magnet temperature and cross-coupling, respectively, and are obtained by fitting the simulation data; L qd (i d ,i q ,T),L dq (i d ,i q , T) are the d-axis and q-axis mutual inductances under the influence of permanent magnet temperature and cross coupling, respectively, obtained by fitting the simulation data; ψ f (i d ,i q ,T) is the permanent magnet flux linkage considering the permanent magnet temperature and cross-coupling, which is obtained by fitting the simulation data.

[0151] Furthermore, the electromagnetic torque considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling is:

[0152]

[0153] The electromagnetic model obtained considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling is as follows: Figure 3 shown.

[0154] Step 3: Based on the torque demand of the motor in a single cycle, combined with the established electromagnetic torque model that considers the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross-coupling, the input current of the motor during each loading is obtained and input into the constructed lumped parameter thermal network model to calculate the heat source of the node; according to step 1, the equivalent thermal resistance of the node is calculated based on the thermal conductivity and convective heat transfer coefficient, and the heat capacity of the node is calculated.

[0155] Step 4: Write the heat balance equation of each temperature node according to the heat source, equivalent thermal resistance and heat capacity of each temperature node, and combine them in matrix form. The temperature of each temperature node is obtained by solving the matrix;

[0156] Step 5: Calculate the temperature difference of the temperature nodes before and after each time step, and judge whether the convergence error is reached according to the temperature difference of the temperature nodes. If the convergence error is not reached, it is necessary to output the winding temperature and permanent magnet temperature at this time according to the temperature field distribution, which is used to correct the winding resistance (Formula (11)) at this temperature and the input current of the motor at this temperature, and further modify the node heat source in the lumped parameter thermal network. Then repeat the above step 4 to continue the calculation of the thermal network until the convergence error is met.

[0157] Since the temperature rise of permanent magnets will affect their performance, it is necessary to provide real-time feedback on the current excitation of the motor based on the permanent magnet temperature during the simulation process. The flow chart of the centralized parameter thermal network calculation program is as follows: Figure 5 shown.

[0158] Since temperature can cause demagnetization of permanent magnets, thereby affecting the input current of the motor under periodic short-term high overload, in the constructed lumped parameter thermal network model, by considering the demagnetization and temperature effects of the permanent magnets, the periodic current input of the lumped parameter thermal network is updated in real time and used to calculate the heat source parameters in the lumped parameter thermal network, thereby updating the node temperature of the motor.

[0159] In this embodiment, the motor temperature rise needs to be calculated under 20 cycles. The torque output required in a single cycle is shown in the following diagram: Figure 4 As shown in the figure, in the last 4 seconds of a cycle, the overload is increased to 5.2 times every 1 second. During this overload cycle, the speed remains constant.

[0160] Based on the method of the present invention, the calculated temperature rise of the motor under periodic short-term high overload conditions and the experimental results are as follows: Figure 6 As shown in the figure, during a high overload cycle, the time required for heat transfer from the windings to the housing causes the motor winding temperature rise and the housing temperature rise to reach their maximum values ​​at different times. Ultimately, over 20 cycles, the calculated results of the lumped parameter thermal network model closely matched the experimental results. This demonstrates the feasibility of the lumped parameter thermal network model, which considers permanent magnet demagnetization, under cyclic overload conditions and can provide some assistance in the optimization process of motor design.

[0161] During simulation, real-time feedback on the motor input current is provided based on the permanent magnet temperature, correcting the current to maintain periodic torque output. Winding resistance is adjusted based on winding temperature and used to calculate winding losses. This feedback mechanism improves the consistency between simulation and actual operating conditions.

[0162] The following are device embodiments of the present invention, which can be used to perform the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0163] In yet another embodiment of the present invention, a system for analyzing the temperature field of a periodic short-term high overload motor is provided, which can be used to implement the above-mentioned method for analyzing the temperature field of a periodic short-term high overload motor. Specifically, the system for analyzing the temperature field of a periodic short-term high overload motor includes:

[0164] A current calculation module is used to calculate the input current of the motor based on the torque demand of the motor and an electromagnetic torque model; the electromagnetic torque model is an electromagnetic torque model that takes into account the effects of permanent magnet temperature, magnetic circuit nonlinearity, and orthogonal axis cross-coupling;

[0165] a temperature field distribution calculation module, configured to input the input current into a lumped parameter thermal network model of the motor, calculate the heat source of each node in the lumped parameter thermal network model; solve the heat balance equation of each node according to the heat source, equivalent thermal resistance, and heat capacity of each node, and obtain the temperature field distribution of each node;

[0166] The judgment module is used to calculate the node temperature difference between two adjacent time steps, and judge whether the convergence error is reached according to the node temperature difference. If the convergence error is not reached, the current winding temperature and permanent magnet temperature are obtained according to the temperature field distribution, the winding resistance in the lumped parameter thermal network model is corrected according to the winding temperature, and the permanent magnet temperature is transmitted to the current calculation module to correct the input current.

[0167] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory, wherein the memory is used to store a computer program, wherein the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the analysis method of the temperature field of a periodic short-term high overload motor.

[0168] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the terminal's operating system. In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the analysis method for the temperature field of a periodic short-term high-overload motor in the above embodiment.

[0169] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the temperature field of a periodic short-term high overload motor, characterized in that: include: S1, calculate the input current of the motor according to the torque demand of the motor and the electromagnetic torque model; The electromagnetic torque model is an electromagnetic torque model that takes into account the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling; S2, inputting the input current into the lumped parameter thermal network model of the motor, calculating the heat source of each temperature node in the lumped parameter thermal network model; solving the thermal balance equation of each temperature node according to the heat source, equivalent thermal resistance and heat capacity of each temperature node, and obtaining the temperature field distribution of each temperature node; S3, calculate the temperature difference of the temperature nodes between two adjacent time steps, and judge whether the convergence error is reached according to the temperature difference of the temperature nodes. If the convergence error is not reached, obtain the current winding temperature and permanent magnet temperature according to the temperature field distribution, correct the winding resistance in the lumped parameter thermal network model according to the winding temperature, and return to S1 to recalculate the input current according to the permanent magnet temperature until the convergence error is met.

2. The method for analyzing the temperature field of a periodic short-term high overload motor according to claim 1, characterized in that: In S1, the electromagnetic torque model is specifically: In the formula, ψ d (i d ,i q ,T),ψ q (i d ,i q , T) are the d-axis and q-axis flux linkages considering the permanent magnet temperature and cross-coupling effects; L d (i d ,i q ,T),L q (i d ,i q , T) are the d-axis and q-axis inductances considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling; L qd (i d ,i q ,T),L dq (i d ,i q ,T) are the d-axis and q-axis mutual inductances considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling; ψ f (i d ,i q ,T) is the permanent magnet flux linkage considering the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling.

3. The method for analyzing the temperature field of a periodic short-term high overload motor according to claim 1, characterized in that: The lumped parameter thermal network model of the motor is constructed by the following method: The motor components are divided into temperature nodes, and each component in the motor is replaced by an equivalent thermal resistance. A lumped parameter thermal network model is established based on the divided temperature nodes and the equivalent thermal resistance, heat capacity and heat source of the temperature nodes.

4. The method for analyzing the temperature field of a periodic short-term high overload motor according to claim 3, characterized in that: The equivalent thermal resistance includes the conduction thermal resistance, contact thermal resistance and convection thermal resistance of the motor component; considering the motor component as a cylindrical component, the conduction thermal resistance includes the axial thermal resistance and the radial thermal resistance; The axial thermal resistance is: Where: ry_a is the axial thermal conductivity of the cylindrical component; L ry is the length of the cylindrical component; r ryo and r ryi are the outer and inner diameters of the cylindrical component, respectively; The radial thermal resistance is: Where: ry_r is the radial thermal conductivity of the cylindrical component; The convection thermal resistance is: Where: A re h is the contact area between the rotor end and the air inside the motor; re_a is the convective heat transfer coefficient between the rotor end and the internal air; δ is the air gap thickness; λ air is the thermal conductivity of air; Nu is the Nusselt coefficient; The contact thermal resistance includes: The contact thermal resistance between the stator core, resin and casing is: Where: air is the thermal conductivity of air; L s_h is the length of the contact surface between the housing and the stator component; the stator outer diameter r syo As the benchmark, r hi is the inner diameter of the casing; The contact thermal resistance between the housing and the end covers on both sides is: Where: h_ec A is the contact gap thickness between the housing and the end cover; h_ec is the contact area between the housing and the end cover.

5. The method for analyzing the temperature field of a periodic short-term high overload motor according to claim 3, characterized in that: The calculation formula of heat capacity is expressed as: C=ρ×c×V In the formula, C is heat capacity; c is specific heat capacity; V is volume; ρ is density; The calculation expression of heat capacity in the process of endothermic and exothermic is expressed as: Q=C×ΔT Where Q is the heat and ΔT is the temperature change.

6. The method for analyzing the temperature field of a periodic short-term high overload motor according to claim 3, characterized in that: The heat sources include: basic copper loss, iron loss, permanent magnet eddy current loss and mechanical loss; The expression of the basic copper loss is as follows: P Cu =mI 2 R Wherein: m is the number of motor phases, in this embodiment, m=3; I is the effective value of the winding phase current; R is the resistance value of the winding; The expression of the iron loss is: Where: P Fe is the core loss; P hys is the hysteresis loss; P cla is the eddy current loss; P exc is the additional loss; k hys is the hysteresis loss coefficient; k cla is the eddy current loss coefficient; k exc is the additional loss coefficient; G Fe is the core weight; f is the frequency of the sinusoidal magnetic flux density; B m is the sinusoidal magnetic flux density amplitude; The expression of the permanent magnet loss is as follows: Where: P edd is the eddy current loss of permanent magnet; V PM is the volume of permanent magnet; E PM is the electric field strength in the permanent magnet; J PM is the current density in the permanent magnet; σ PM is the electrical conductivity of the permanent magnet; The formula for the mechanical loss is: P fw =kC v I swear m 3 r 4 L Where: k is the rotor surface roughness; C v is the friction coefficient, which depends on the rotor speed and the properties of the surrounding air; ρ0 is the density of the surrounding medium; ω m is the rotor angular velocity; r is the rotor radius; L is the rotor radius; F β Calculate the load for the bearing; D m is the bearing diameter; v bea is the circumferential speed of the ball center.

7. The method for analyzing the temperature field of a periodic short-term high overload motor according to claim 1, characterized in that: In S3, the winding resistance in the lumped parameter thermal network model is corrected according to the winding temperature, specifically: R1=R0[1+(T1-T0)μ] Where: T0 is the initial temperature of the winding; R0 is the winding resistance at T0 temperature; μ is the temperature coefficient of the copper material.

8. A system for analyzing the temperature field of a periodic short-term high overload motor, characterized in that: include: A current calculation module is used to calculate the input current of the motor according to the torque demand of the motor and the electromagnetic torque model; The electromagnetic torque model is an electromagnetic torque model that takes into account the permanent magnet temperature, magnetic circuit nonlinearity and orthogonal axis cross coupling; A temperature field distribution calculation module is used to input the input current into the lumped parameter thermal network model of the motor, calculate the heat source of each temperature node in the lumped parameter thermal network model; solve the thermal balance equation of each temperature node according to the heat source, equivalent thermal resistance and heat capacity of each temperature node, and obtain the temperature field distribution of each temperature node; The judgment module is used to calculate the temperature difference of the temperature nodes between two adjacent time steps, and judge whether the convergence error is reached according to the temperature difference of the temperature nodes. If the convergence error is not reached, the current winding temperature and permanent magnet temperature are obtained according to the temperature field distribution, the winding resistance in the lumped parameter thermal network model is corrected according to the winding temperature, and the permanent magnet temperature is transmitted to the current calculation module to correct the input current.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for analyzing the temperature field of a periodic short-term high overload motor as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for analyzing the temperature field of a periodic short-term high overload motor as claimed in any one of claims 1 to 7 are implemented.

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