Variable reluctance motor temperature distribution prediction method and prediction system

Abstract

The invention provides a variable reluctance motor temperature distribution prediction method and prediction system. The method comprises the following steps: dividing and modeling network nodes based on the structure and network precision requirements of a variable reluctance motor; calculating conduction thermal resistance and thermal capacity according to geometric parameters and material parameters of the motor; calculating air gap convection thermal resistance according to the motor working condition and the air gap viscosity coefficient; considering the relative position relationship between the armature winding and the excitation winding and establishing a complete variable reluctance motor thermal network model; calculating iron loss and copper loss by using an empirical formula based on state variables of actual operation of the motor, and taking the iron loss and the copper loss as input sources of the variable reluctance motor thermal network model; and iteratively solving the thermal network model of the variable reluctance motor by using a thermal transient equation to obtain predicted temperature distribution of the variable reluctance motor. The invention provides a complete modeling and prediction method suitable for temperature distribution of the variable reluctance motor, the calculation efficiency and precision are both considered, and powerful support is provided for multi-physics field joint optimization.

Description

Technical Field

[0001] The present invention belongs to the field of motor optimization design, and specifically relates to a temperature distribution prediction method and prediction system for a variable reluctance motor. Background Art

[0002] Thanks to the development of rare earth permanent magnet materials, permanent magnet synchronous motors (PMSMs) with high efficiency and high torque density have been rapidly researched and developed. However, PMSMs still face challenges such as high cost, limited operating temperature, and irreversible demagnetization, which limit their further application. To overcome these shortcomings, an electromagnetism field modulation motor (EMM) has been proposed in recent years, which uses excitation windings instead of permanent magnets. This motor is a doubly salient electromagnetism synchronous motor that eliminates the slip rings and brushes of traditional rotor EMMs. Its operating principle can be explained by magnetic field modulation theory. Due to its continuous excitation, EMM motors also have low torque ripple.

[0003] A variable reluctance motor has been proposed as a special electrically excited magnetic field modulation motor. The rotor structure of this motor is simple and can achieve brushless operation. At the same time, the excitation winding and armature winding of the variable reluctance motor are installed in the same stator teeth. Compared with the traditional switched reluctance motor, the variable reluctance motor has higher rotor robustness and torque density, so it has broad application prospects in traction and more electric aircraft.

[0004] The electromagnetic field analysis of variable reluctance motors has attracted widespread attention, but few studies have focused on their thermal models. Currently, there are three main methods for predicting the thermal behavior of motors: finite element analysis, computational fluid dynamics, and lumped thermal parameter models. Finite element analysis predicts the temperature distribution of the motor by solving thermodynamic equations, while computational fluid dynamics uses numerical methods to discretize the original Navier-Stokes equations to obtain a numerical solution to the fluid field. However, compared to the lumped thermal parameter model, both of these methods use a more precise calculation method and very fine time division, resulting in a large number of computational iterations before an accurate solution is finally obtained. Their calculation speed is highly dependent on the computer hardware level, posing a challenge to the multi-physics joint optimization of the motor.

[0005] The equivalent variable reluctance motor thermal network model balances computational efficiency with reasonable accuracy, enabling efficient and reliable motor design. However, current research on variable reluctance motors has limited focus on electromagnetic analysis. Therefore, methods for predicting temperature distribution in variable reluctance motors remain valuable. Summary of the Invention

[0006] To address the above technical issues, the present invention proposes a variable reluctance motor temperature distribution prediction method and prediction system, which can improve the computational efficiency of the motor's thermal field and provide strong support for the joint optimization of multiple physical fields. To achieve the above technical objectives, the technical solutions adopted are as follows:

[0007] A method for predicting temperature distribution of a variable reluctance motor, characterized by comprising the following steps:

[0008] Step S1, modeling each component of the variable reluctance motor;

[0009] Step S2, calculating the thermal resistance and thermal capacity between the components of the variable reluctance motor;

[0010] Step S3, using the Steinmetz formula and the resistance loss formula to calculate the iron loss and copper loss of each component in step S1 respectively;

[0011] Step S4: establishing a complete variable reluctance motor thermal network model based on the connection relationship of the components in the actual topology in step S1 and the thermal resistance and thermal capacity calculated in step S2;

[0012] Step S5: Using the iron loss and copper loss of each component obtained in step S3 as input sources of the variable reluctance motor thermal network model in step S4, iteratively solving to obtain the final time and space distribution of the predicted temperature of the variable reluctance motor.

[0013] Beneficial effects: The variable reluctance motor thermal network model and prediction system provided by the present invention are analogous to traditional electrical network methods in the links of network construction and calculation and solution. They adopt the simplified means of time discretization of thermodynamic equations and transient thermal network solution, avoiding the solution of complex nonlinear thermal field equations of actual variable reluctance motor systems. At the same time, the parameter calculation method, component model simplification, network construction method and iterative solution method provided in the present invention can ensure the solution accuracy of the constructed thermal network model. Compared with the traditional finite element method, the method proposed in the present invention can efficiently predict the distribution of variable reluctance motor temperature in time and space within a reasonable error range.

[0014] In an optional embodiment, step S1 specifically includes the following sub-steps:

[0015] Step S11, defining geometric parameters and material parameters of each component of the variable reluctance motor, including size parameters, thermal conductivity and specific heat capacity;

[0016] Step S12: simplify the modeling of the copper wire and insulation in the stator slots and regard them as a mixture;

[0017] Step S13 : dividing the nodes of the variable reluctance motor thermal network model according to the required variable reluctance motor thermal network model accuracy and the motor structure.

[0018] Beneficial effects: Furthermore, based on the actual physical model of the variable reluctance motor, this technical solution simplifies the modeling assumption of winding mixtures, avoiding the modeling of complex and disordered winding coils, thereby reducing the model complexity and further improving the computational efficiency of the system.

[0019] In an optional embodiment, in step S2, the thermal resistance R and thermal capacity C between the components of the variable reluctance motor are respectively expressed as:

[0020]

[0021] C=m c c p (2)

[0022] Where L is the length of the heat path; A is the cross-sectional area of ​​the heat path; for conduction thermal resistance, k is the thermal conductivity of the heat path material, and for convection thermal resistance, k is the heat convection coefficient h of the air gap g ;m c is the mass of the heat-conducting object; c p is the specific heat capacity of the heat-conducting object; the thermal resistance between components includes convection thermal resistance, tangential conduction thermal resistance, radial conduction thermal resistance, axial conduction thermal resistance and air gap conduction thermal resistance, which are all calculated using the above formula.

[0023] Beneficial effects: Furthermore, this technical solution provides a method for calculating thermal network parameters, realizing the transformation from an actual physical system to a thermal network prediction system. The method is concise and easy to solve directly, providing designers with an efficient and concise modeling tool.

[0024] In an optional embodiment, step S3 specifically includes the following sub-steps:

[0025] Step S31, obtaining the core loss factor, winding resistance, excitation current amplitude, armature current effective value, air gap flux density amplitude and rotor speed;

[0026] Step S32: Calculate the iron loss of the components that can generate loss sources in the variable reluctance motor according to Steinmetz formula (3).

[0027] p fe =k h f(B m ) 2 +k e (fB m ) 2 (3)

[0028] Among them, k h =513.8Ws / (T 2 m 3 ) and k e=0.4675Ws 2 / (T 2 m 3 ) represent the hysteresis loss coefficient and eddy current loss coefficient of the core, B m is the magnitude of the magnetic flux density, f is the electrical frequency of the variable reluctance motor;

[0029] Step S33: Calculate the copper loss of the components that can generate loss sources in the variable reluctance motor according to the resistance loss formula (4).

[0030]

[0031] Among them, I f is the excitation current amplitude, I arms is the effective value of the armature current, m is the number of phases of the AC winding of the motor, R f (T) and R a (T) are the resistance values ​​of the excitation winding and armature winding at temperature T respectively.

[0032] Beneficial effects: As a preferred solution of the technical solution of the present invention, this technical solution directly calculates the iron loss and copper loss of components that can generate loss sources in the variable reluctance motor through empirical formulas, and can be combined with the magnetic network to efficiently solve the copper loss and iron loss, thereby avoiding time-consuming and complex thermoelectric joint simulation, and further improving the calculation efficiency of this method.

[0033] In an optional embodiment, in step S4, the connection relationship between the nodes in the thermal network model of the variable reluctance motor is determined based on the actual topology of the variable reluctance motor and the positional relationship between the armature winding and the excitation winding, and based on the thermal resistance and thermal capacity obtained in step S2, they are used as connection parameters between the nodes in the thermal network model to establish a complete thermal network model of the variable reluctance motor.

[0034] Beneficial effect: As a preferred solution of the technical solution of the present invention, this technical solution establishes a thermal network model by analogy with the modeling method of the electrical network, so that an efficient and reliable electrical network solution algorithm can be applied to the established thermal network model, ensuring the calculation efficiency and accuracy of this method.

[0035] In an optional embodiment, step S5 specifically includes the following sub-steps: Step S51, discretizing the heat conduction differential equation to establish a thermal transient equation of the variable reluctance motor thermal network model, expressed as:

[0036]

[0037] Wherein, T(t+Δt) is the temperature matrix of the variable reluctance motor thermal network model at t+Δt, P(t) is the power matrix at t, Y is the admittance matrix of the variable reluctance motor thermal network model, C is the heat capacity matrix of the variable reluctance motor thermal network model, and T(t) is the temperature matrix of the variable reluctance motor thermal network model at t;

[0038] Step S52: Using the losses of each component obtained in step S3 as the input source of the variable reluctance motor thermal network model obtained in step S4, formula (5) is iteratively solved to obtain the distribution of the predicted temperature of the variable reluctance motor in time and space.

[0039] Beneficial effect: As a preferred solution of the technical solution of the present invention, this technical solution derives the thermal transient equation of the thermal network model by analogy with the basic node voltage formula of the electrical network model, thereby realizing the complete equivalence of the thermal network and the electrical network in a mathematical sense, so that an efficient and reliable electrical network solution algorithm can be applied to the established thermal network model, ensuring the computational efficiency and accuracy of this method.

[0040] The present invention further discloses a variable reluctance motor temperature distribution prediction system, comprising:

[0041] Thermal parameter calculation module: Based on the prediction accuracy requirements and the actual structure of the variable reluctance motor, the various components of the variable reluctance motor are modeled and the network nodes are divided. Based on the geometric parameters, material parameters and operating conditions of the variable reluctance motor, the thermal resistance and heat capacity between different components are calculated, that is, the thermal parameters of the variable reluctance motor are obtained;

[0042] Power parameter calculation module: Based on the state variables under the actual operating conditions of the variable reluctance motor and the physical model and material parameters of the actual motor, the iron loss and copper loss of each component of the variable reluctance motor are calculated using empirical formulas. In other words, the power parameters of the variable reluctance motor are obtained and used as the input source of the variable reluctance motor thermal network model;

[0043] Model prediction module: Based on the obtained thermal resistance and thermal capacity parameters between different components of the variable reluctance motor, as well as the variable reluctance motor power parameters and the connection method between the various motor components, a complete variable reluctance motor thermal network model is established. Then, based on the discrete thermal transient equation, the predicted temperature distribution at any time is obtained through iterative solution.

[0044] In summary, the variable reluctance motor thermal network model and prediction system provided by the present invention are intuitive and easy to implement. They can simplify the multi-physical field joint optimization design of the variable reluctance motor and improve the design efficiency. Using the variable reluctance motor thermal network model and prediction system provided by the present invention, the temperature distribution of the variable reluctance motor can be efficiently predicted within a reasonable error range. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flow chart of a method for predicting temperature distribution of a variable reluctance motor;

[0046] Figure 2 Thermal model for variable reluctance motor stator slots;

[0047] Figure 3 It is the topological structure diagram of the variable reluctance motor;

[0048] Figure 4 A simplified casing model used in finite element thermal simulation;

[0049] Figure 5(a) is a schematic diagram showing the DCAC arrangement of the field winding and armature winding positions;

[0050] Figure 5(b) is a schematic diagram showing the ACDC arrangement of the field winding and armature winding positions;

[0051] Figure 6 A lumped parameter variable reluctance motor thermal network model for a 12 / 10 variable reluctance motor with a DCAC winding arrangement;

[0052] Figure 7(a) shows the comparison of the winding temperature predicted by the finite element and variable reluctance motor thermal network models;

[0053] Figure 7(b) shows the temperature comparison between the core and the casing predicted by the finite element and variable reluctance motor thermal network models, where the motor drive mode is BLAC and i d =0 control, the winding arrangement is DCAC, the rotor speed is 1500rpm, and the RMS value of the current density of the excitation winding and the armature winding is 5A / mm 2 ;

[0054] Figure 8(a) shows the comparison of the temperature predicted by the finite element and variable reluctance motor thermal network models of the windings;

[0055] Figure 8(b) shows the temperature comparison between the core and the casing predicted by the finite element and variable reluctance motor thermal network models, where the motor drive mode is BLAC and i d =0 control, the winding arrangement is ACDC, the rotor speed is 1500rpm, and the RMS value of the current density of the excitation winding and the armature winding is 5A / mm 2 . DETAILED DESCRIPTION

[0056] The following is a more detailed description of the variable reluctance motor temperature distribution prediction method and system of the present invention, with reference to schematic diagrams. Preferred embodiments of the present invention are shown, and it should be understood that those skilled in the art may modify the present invention described herein while still achieving the beneficial effects of the present invention. Therefore, the following description should be understood as a general guideline for those skilled in the art and is not intended to limit the present invention.

[0057] like Figure 1 As shown in the flowchart of the variable reluctance motor temperature distribution prediction method, a variable reluctance motor temperature distribution prediction method includes the following steps:

[0058] Step S1: Modeling the various components of the variable reluctance motor, specifically including the following steps:

[0059] Step S11, defining the geometric parameters and material parameters of each component of the motor, including size parameters, thermal conductivity, specific heat capacity, etc.;

[0060] Specifically including the following components: casing middle, casing ends, stator yoke, stator teeth, armature winding, slot filling, excitation winding, slot wedge, air gap, rotor teeth, rotor yoke, shaft, bearings and end covers;

[0061] Step S12: Simplify the modeling of the copper wire and insulation in the stator slots and regard them as a mixture, such as Figure 2 The thermal model of the variable reluctance motor stator slots is shown;

[0062] Step S13: dividing the nodes of the variable reluctance motor thermal network model according to the required variable reluctance motor thermal network model accuracy and the motor structure;

[0063] The results are as follows Figure 3 As shown in the variable reluctance motor topology diagram, the number of pole slots of the motor analyzed in this embodiment is 12 / 10. Since the variable reluctance motor is not designed with pole shoes, in order to facilitate the fixing of the winding, slot wedges are required to block the stator slots to prevent the winding from overflowing the stator. In order to install the slot wedges, slots need to be cut on the stator.

[0064] Step S2: Calculate thermal resistance and thermal capacity.

[0065] Thermal resistance includes convection thermal resistance, tangential conduction thermal resistance, radial conduction thermal resistance, axial conduction thermal resistance and air gap conduction thermal resistance;

[0066] Thermal resistance R and heat capacity C can be expressed as:

[0067]

[0068] C=m c c p (2)

[0069] Where L is the length of the heat path; A is the cross-sectional area of ​​the heat path; for conduction thermal resistance, k is the thermal conductivity of the heat path material, and for convection thermal resistance, k is the heat convection coefficient h of the air gap g ;m c is the mass of the heat-conducting object; c p is the specific heat capacity of the heat conducting object.

[0070] Thermal convection coefficient h of the air gap g The Nusselt number N of the air gap ug Decision, to get N ug , first calculate the Taylor number T of the air gap ag and the geometric factor F g , respectively expressed as:

[0071]

[0072] Among them, n r is the rotor speed, v air =2×10 -5 represents the kinematic viscosity of air, S can be expressed as:

[0073]

[0074] Thus N ug According to T ag 2 / F g 2 Expressed as:

[0075]

[0076] Therefore, the heat convection coefficient of the air gap h g It can be calculated by formula (13):

[0077]

[0078] Among them, k air is the thermal conductivity of air.

[0079] Step S3: Calculate the iron loss and copper loss of each part using empirical formulas based on the state variables of the motor during operation;

[0080] Step S31, obtaining the core loss parameters, winding resistance and motor operation state variable values, including excitation current amplitude, armature current effective value, air gap flux density amplitude, rotor speed, etc.;

[0081] Step S32: Calculate the iron loss of the components that can generate loss sources in the variable reluctance motor according to the following formula:

[0082] p fe =k h f(Bm ) 2 +k e (fB m ) 2 (3)

[0083] Among them, k h =513.8Ws / (T 2 m 3 ) and k e =0.4675Ws 2 / (T 2 m 3 ) represent the hysteresis loss coefficient and eddy current loss coefficient of the core, B m is the magnitude of the magnetic flux density, f is the electrical frequency of the variable reluctance motor;

[0084] Step S33: Calculate the copper loss of the components that can generate loss sources in the variable reluctance motor according to the following formula:

[0085]

[0086] Among them, I f is the excitation current amplitude, I arms is the effective value of the armature current, m is the number of phases of the AC winding of the motor, R f (T) and R a (T) are the resistance values ​​of the field winding and armature winding at temperature T respectively;

[0087] Step S34: Using the calculated iron loss and copper loss as input sources of the variable reluctance motor thermal network model.

[0088] Step S4: Considering the relative positions of the armature winding and the field winding, and based on the thermal resistance and thermal capacity calculated in step S2, a complete variable reluctance motor thermal network model is obtained.

[0089] like Figure 7(a) and 7(b) As shown in the figure, considering the influence of the relative position of the excitation winding and the armature winding on the temperature distribution, the motor with the excitation winding closer to the air gap of the variable reluctance motor is called a DCAC motor, while the motor with the armature winding closer to the air gap is called an ACDC motor;

[0090] The complete variable reluctance motor thermal network model is established, as shown in Figure 8(a) and Figure 8(b). The winding arrangement of the model is DCAC, where R x_cov , R x_t , R x_r , R x_z , R x_con They represent the convection thermal resistance, tangential conduction thermal resistance, radial conduction thermal resistance, axial conduction thermal resistance and air gap conduction thermal resistance of x, respectively. xrepresents the heat capacity of x, P x represents the input power of x, where x represents the nodes of each part of the motor, and x = h_middle, h_end, sb, st, ac, fill, dc, wedge, air, rt, rb, sh, b, and ec represent the housing middle, housing end, stator yoke, stator teeth, armature winding, slot fill, field winding, slot wedge, air gap, rotor teeth, rotor yoke, shaft, bearings, and end covers, respectively.

[0091] Step S5: Based on the copper loss and iron loss calculated in step S3, the copper loss and iron loss are used as input sources of the variable reluctance motor thermal network model to obtain the final predicted temperature distribution, which specifically includes the following steps:

[0092] Step S51: Establish a thermal transient equation of the variable reluctance motor thermal network model based on the heat conduction differential equation, which is expressed as:

[0093]

[0094] Wherein, T(t+Δt) is the temperature matrix of the variable reluctance motor thermal network model at t+Δt, P(t) is the power matrix at t, Y is the admittance matrix of the variable reluctance motor thermal network model, C is the heat capacity matrix of the variable reluctance motor thermal network model, and T(t) is the temperature matrix of the variable reluctance motor thermal network model at t;

[0095] Step S52: Based on the variable reluctance motor thermal network model obtained in step S4 and the thermal transient equation (5), iteratively solve to obtain the predicted temperature distribution of each part of the variable reluctance motor;

[0096] The temperature curves of the variable reluctance motor predicted by the finite element method and the variable reluctance motor thermal network model for DCAC and ACDC winding structures are shown in Figures 7(a), 7(b) and 8(a), 8(b), respectively. The finite element temperature here is the average temperature. It can be seen from the figure that the maximum absolute error between the finite element model and the lumped variable reluctance motor thermal network model is within 5°C, verifying the accuracy of the thermal model.

[0097] The relative position of the excitation winding and the armature winding has an impact on the steady-state temperature of the excitation winding and the armature winding, but has little effect on the steady-state temperature of the stator, rotor and casing. When the winding arrangement is DCAC, the steady-state temperature of the DC winding is higher than the steady-state temperature of the AC winding, while the result is the opposite when the winding arrangement is ACDC, because the winding close to the air gap is far away from the casing, resulting in poor heat dissipation performance and higher temperature.

[0098] The above description is merely a preferred embodiment of the present invention and does not limit the present invention in any way. Any person skilled in the art who, without departing from the scope of the present invention, makes any equivalent substitution, modification, or other changes to the technical solution and technical content disclosed in the present invention shall be deemed to be within the scope of the present invention and still fall within the scope of protection of the present invention.

Claims

1. A method for predicting temperature distribution of a variable reluctance motor, characterized in that: The following steps are involved: Step S1, modeling each component of the variable reluctance motor; Step S2, calculating the thermal resistance and thermal capacity between the components of the variable reluctance motor; Step S3, using the Steinmetz formula and the resistance loss formula to calculate the iron loss and copper loss of each component in step S1 respectively; Step S4: establishing a complete variable reluctance motor thermal network model based on the connection relationship of the components in the actual topology in step S1 and the thermal resistance and thermal capacity calculated in step S2; Step S5: Using the iron loss and copper loss of each component obtained in step S3 as input sources of the variable reluctance motor thermal network model in step S4, iteratively solving to obtain the final time and space distribution of the predicted temperature of the variable reluctance motor.

2. The method for predicting temperature distribution of a variable reluctance motor according to claim 1, wherein: Step S1 specifically includes the following sub-steps: Step S11, defining geometric parameters and material parameters of each component of the variable reluctance motor, including size parameters, thermal conductivity and specific heat capacity; Step S12: simplify the modeling of the copper wire and insulation in the stator slots and regard them as a mixture; Step S13: Divide the nodes of the variable reluctance motor thermal network model according to the required variable reluctance motor thermal network model accuracy and the motor structure.

3. The method for predicting temperature distribution of a variable reluctance motor according to claim 1, wherein: In step S2, the thermal resistance R and thermal capacity C between the components of the variable reluctance motor are respectively expressed as: C=m c c p #(2) Where L is the length of the heat path; A is the cross-sectional area of ​​the heat path; for conduction thermal resistance, k is the thermal conductivity of the heat path material, and for convection thermal resistance, k is the heat convection coefficient h of the air gap g ;m c is the mass of the heat-conducting object; c p is the specific heat capacity of the heat-conducting object; the thermal resistance between components includes convection thermal resistance, tangential conduction thermal resistance, radial conduction thermal resistance, axial conduction thermal resistance and air gap conduction thermal resistance, which are all calculated using the above formula.

4. The method for predicting temperature distribution of a variable reluctance motor according to claim 1, wherein: Step S3 specifically includes the following sub-steps: Step S31, obtaining the core loss factor, winding resistance, excitation current amplitude, armature current effective value, air gap flux density amplitude and rotor speed; Step S32: Calculate the iron loss of the components that can generate loss sources in the variable reluctance motor according to Steinmetz formula (3). p fe =k h f(B m ) 2 +k e (fB m ) 2 # (3) Among them, k h =513.8Ws / (T 2 m 3 ) and k e =0.4675Ws 2 / (T 2 m 3 ) represent the hysteresis loss coefficient and eddy current loss coefficient of the core, B m is the magnitude of the magnetic flux density, f is the electrical frequency of the variable reluctance motor; Step S33: Calculate the copper loss of the components that can generate loss sources in the variable reluctance motor according to the resistance loss formula (4). Among them, I f is the excitation current amplitude, I arms is the effective value of the armature current, m is the number of phases of the AC winding of the motor, R f (T) and R a (T) are the resistance values ​​of the excitation winding and armature winding at temperature T respectively.

5. The method for predicting temperature distribution of a variable reluctance motor according to claim 1, wherein: In step S4, the connection relationship between the nodes in the thermal network model of the variable reluctance motor is determined according to the actual topology of the variable reluctance motor and the positional relationship between the armature winding and the excitation winding. Based on the thermal resistance and thermal capacity obtained in step S2, they are used as the connection parameters between the nodes in the thermal network model to establish a complete thermal network model of the variable reluctance motor.

6. The method for predicting temperature distribution of a variable reluctance motor according to claim 1, wherein: Step S5 specifically includes the following sub-steps: Step S51, discretizing the heat conduction differential equation to establish the thermal transient equation of the variable reluctance motor thermal network model, expressed as: Wherein, T(t+Δt) is the temperature matrix of the variable reluctance motor thermal network model at t+Δt, P(t) is the power matrix at t, Y is the admittance matrix of the variable reluctance motor thermal network model, C is the heat capacity matrix of the variable reluctance motor thermal network model, and T(t) is the temperature matrix of the variable reluctance motor thermal network model at t; Step S52: Using the losses of each component obtained in step S3 as the input source of the variable reluctance motor thermal network model obtained in step S4, formula (5) is iteratively solved to obtain the distribution of the predicted temperature of the variable reluctance motor in time and space.

7. A variable reluctance motor temperature distribution prediction system, characterized in that: include: Thermal parameter calculation module: Based on the prediction accuracy requirements and the actual structure of the variable reluctance motor, the various components of the variable reluctance motor are modeled and the network nodes are divided. Based on the geometric parameters, material parameters and operating conditions of the variable reluctance motor, the thermal resistance and heat capacity between different components are calculated, that is, the thermal parameters of the variable reluctance motor are obtained; Power parameter calculation module: Based on the state variables under the actual operating conditions of the variable reluctance motor and the physical model and material parameters of the actual motor, the iron loss and copper loss of each component of the variable reluctance motor are calculated using empirical formulas. In other words, the power parameters of the variable reluctance motor are obtained and used as the input source of the variable reluctance motor thermal network model; Model prediction module: Based on the obtained thermal resistance and thermal capacity parameters between different components of the variable reluctance motor, as well as the variable reluctance motor power parameters and the connection method between the various motor components, a complete variable reluctance motor thermal network model is established. Then, based on the discrete thermal transient equation, the predicted temperature distribution at any time is obtained through iterative solution.

Images