Thermal parameter identification and permanent magnet temperature rapid prediction method, equipment and medium

Abstract

The embodiment of the invention provides a thermal parameter identification and permanent magnet temperature rapid prediction method and device, and a medium, and belongs to the technical field of data identification, and the method specifically comprises the steps: analyzing a motor structure, and abstracting main thermal circuit nodes; constructing an initial thermal resistance network; fitting a copper loss change formula; fitting an iron loss change formula; constructing a transient temperature solving matrix according to the initial thermal resistance network, a copper loss change formula and an iron loss change formula; temperature rise tests of different armature currents and rotating speeds are carried out on the motor for multiple times; according to a test result and a transient temperature solving matrix, performing parameter identification on thermal resistance and thermal capacity in the initial thermal resistance network by using a particle swarm algorithm, and performing optimization based on a least square objective function to solve a target thermal resistance network of the motor; and based on the target thermal resistance network, the copper loss change formula and the iron loss change formula, combining the working condition information of the motor to predict the temperature of the permanent magnet of the motor in real time. Through the scheme of the invention, the prediction efficiency, precision and real-time performance are improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of measurement technology, and in particular to a method, device and medium for thermal parameter identification and rapid prediction of permanent magnet temperature. Background Art

[0002] At present, the temperature of the permanent magnet of the motor directly affects its magnetic properties. When the temperature is high, the permanent magnet may undergo irreversible demagnetization, resulting in a decrease in motor performance. Accurate and fast temperature prediction is crucial to optimizing the heat dissipation design and control strategy of the motor.

[0003] For permanent magnet motors with permanent magnets installed on the rotor, there are usually two methods to obtain the temperature of the permanent magnets. One is to install a sensor directly near the permanent magnet. This method directly measures the temperature of the permanent magnet, but it also brings problems such as complex installation and interference from the magnetic field when transmitting signals. The second is to predict the temperature of the permanent magnet through model prediction. However, the establishment of the thermal network model in the current method is too ideal. It is usually assumed that the operating temperature of the motor is room temperature and remains unchanged. At the same time, the treatment of losses and thermal resistance is also relatively simple. Most models assume that the thermal resistance remains constant, or only consider that the loss changes with the working conditions, but ignore the complex dynamic changes of losses and thermal resistance in actual operation. At the same time, the dimensions of some thermal resistance network matrices are huge, which brings great difficulties to the rapid prediction of the permanent magnet temperature and is difficult to meet the needs of real-time monitoring.

[0004] It can be seen that there is an urgent need for a thermal parameter identification method and a rapid prediction method for permanent magnet temperature with high prediction efficiency, accuracy and real-time performance. Summary of the invention

[0005] In view of this, the embodiments of the present disclosure provide a method, device and medium for thermal parameter identification and rapid prediction of permanent magnet temperature, which at least partially solve the problems of poor prediction efficiency, accuracy and real-time performance in the prior art.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for thermal parameter identification and permanent magnet temperature rapid prediction, comprising:

[0007] Step 1: Analyze the motor structure and abstract the main thermal circuit nodes;

[0008] Step 2, extract the corresponding thermal circuit according to the main thermal circuit nodes and construct the initial thermal resistance network accordingly;

[0009] Step 3, fitting the copper loss variation formula based on the experimental data under different armature current, speed measured data and temperature conditions;

[0010] Step 4, fitting the iron loss variation formula based on the experimental data under different armature current, speed simulation data and temperature conditions;

[0011] Step 5, constructing a transient temperature solution matrix according to the initial thermal resistance network, the copper loss change formula and the iron loss change formula;

[0012] Step 6, performing temperature rise tests on the motor at different armature currents and speeds for multiple times, and using sensors to periodically record the temperature conditions of the thermal circuit nodes to obtain the test results of the motor;

[0013] Step 7, according to the test results and the transient temperature solution matrix, use the particle swarm algorithm to identify the parameters of the thermal resistance and thermal capacitance in the initial thermal resistance network and optimize based on the least squares objective function to solve the target thermal resistance network of the motor;

[0014] Step 8: Based on the target thermal resistance network, the copper loss change formula and the iron loss change formula, combined with the motor operating condition information, the permanent magnet temperature of the motor is predicted in real time.

[0015] According to a specific implementation method of the embodiment of the present disclosure, the copper loss change formula is:

[0016]

[0017] Among them, P cu represents the copper loss of the winding, m is the number of phases of the motor, I a represents the armature winding current, θ w represents the temperature of the armature winding, n represents the motor speed, R w0 Indicates the resistance value of the armature winding at room temperature, f1(θ w ) represents the correction function of the armature winding resistance changing with temperature, f2(n) represents the correction function of the armature winding resistance changing with speed, R w Indicates the resistance of the motor armature winding.

[0018] According to a specific implementation of the embodiment of the present disclosure, before step 3, the method further includes:

[0019] Step 3.1, place the motor winding to be tested into a temperature control box, divide the temperature interval to be tested into multiple equal-length intervals, gradually increase the temperature in equal steps, and keep the motor speed at a fixed value;

[0020] Step 3.2: At each temperature point set above, use a bridge device to measure the armature winding resistance R at the corresponding temperature. w (θ w );

[0021] Step 3.3, based on the temperature θ obtained by the above sampling w The armature winding resistance R at the corresponding temperature w (θ w) data, and construct the correction function f1(θ w )

[0022] f1(θ w )=1+k1(θ w -θ0)

[0023] Where k1 represents the proportionality coefficient of the armature winding resistance changing with temperature;

[0024] Step 3.4, repeat steps 3.1 to 3.3, and use the binomial equation to fit the effect of skin effect on resistance as the speed changes, and obtain the correction function f2(n) for constructing the armature winding resistance as the speed changes.

[0025]

[0026] Among them, k2 represents the first-order proportional coefficient of the armature winding resistance changing with the speed, k3 represents the second-order proportional coefficient of the armature winding resistance changing with the speed, and n max Indicates the maximum design speed of the motor.

[0027] According to a specific implementation of the embodiment of the present disclosure, the iron loss change formula is:

[0028] P Fe_i =P Fe_i (I a ,θ0,n)·[1+k4·(θ i -θ0)]

[0029] Among them, P Fe_i It represents the iron loss of the ith node, and k4 is the iron loss temperature coefficient.

[0030] According to a specific implementation of the embodiment of the present disclosure, the expression of the transient temperature solution matrix is:

[0031] Δθ=C -1 Gθ+C -1 P

[0032] Among them, Δθ represents the temperature rise matrix, C is the heat capacity matrix in the thermal resistance network, G represents the thermal resistance matrix in the thermal resistance network, and P is the copper loss change formula and the iron loss change formula;

[0033] Δθ=[Δθ1Δθ2…Δθ N-M ] T

[0034] C=diag(C1,C2,…,C N-M )

[0035]

[0036] P=[P loss_1 +P eq_1 P loss_2 +P eq_2 …P loss_(N-M) +P eq_(N-M) ] T .

[0037] Where N represents the total number of nodes in the thermal resistance network, and M represents the number of ambient temperature nodes in the thermal resistance network;

[0038] According to a specific implementation of the embodiment of the present disclosure, the expression of the least squares objective function is:

[0039]

[0040] In a second aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0041] at least one processor; and,

[0042] a memory communicatively connected to the at least one processor; wherein,

[0043] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the thermal parameter identification and permanent magnet temperature rapid prediction method in the aforementioned first aspect or any implementation of the first aspect.

[0044] In the third aspect, the embodiments of the present disclosure also provide a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the thermal parameter identification and permanent magnet temperature rapid prediction method in the aforementioned first aspect or any implementation method of the first aspect.

[0045] In a fourth aspect, an embodiment of the present disclosure further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the thermal parameter identification and permanent magnet temperature rapid prediction method in the aforementioned first aspect or any implementation method of the first aspect.

[0046] The thermal parameter identification and permanent magnet temperature rapid prediction scheme in the disclosed embodiment includes: step 1, analyzing the motor structure and abstracting the main thermal circuit nodes; step 2, refining the corresponding thermal circuit according to the main thermal circuit nodes and constructing the initial thermal resistance network accordingly; step 3, fitting the copper loss change formula based on different armature currents, speed measured data and experimental data under temperature conditions; step 4, fitting the iron loss change formula based on different armature currents, speed simulation data and experimental data under temperature conditions; step 5, constructing a transient temperature prediction network according to the initial thermal resistance network, the copper loss change formula and the iron loss change formula. Solve the matrix; Step 6, perform temperature rise tests on the motor for multiple times with different armature currents and speeds, and use sensors to record the temperature conditions of the thermal circuit nodes at regular intervals to obtain the test results of the motor; Step 7, solve the matrix according to the test results and the transient temperature, use the particle swarm algorithm to identify the parameters of the thermal resistance and thermal capacitance in the initial thermal resistance network, and optimize based on the least squares objective function to solve the target thermal resistance network of the motor; Step 8, based on the target thermal resistance network, the copper loss change formula and the iron loss change formula, combined with the motor operating condition information, the permanent magnet temperature of the motor is predicted in real time.

[0047] The beneficial effects of the embodiments of the present disclosure are as follows: through the scheme of the present disclosure, in order to solve the problems of the traditional thermal resistance network having too large a dimension and not considering the ambient temperature and time-varying losses, by simplifying the motor into a low-dimensional thermal circuit model, the ambient temperature is regarded as a time-varying variable, and the loss, thermal resistance and thermal capacity of each node are regarded as dynamic variables related to the armature current, speed and temperature, a fast and accurate permanent magnet temperature prediction model is constructed, thereby improving the prediction efficiency, accuracy and real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A schematic diagram of a flow chart of a method for thermal parameter identification and permanent magnet temperature rapid prediction provided by an embodiment of the present disclosure;

[0050] Figure 2 A schematic diagram of a specific implementation process of a thermal parameter identification and permanent magnet temperature rapid prediction method provided by an embodiment of the present disclosure;

[0051] Figure 3 A cross-sectional view and thermal circuit diagram of a motor provided by an embodiment of the present disclosure, wherein 1 is a coolant node, 2 is a stator yoke node, 3 is a slot winding node, 4 is a stator tooth node, 5 is a permanent magnet and mover node, and 6 is an air node;

[0052] Figure 4 A schematic diagram of a thermal resistance network of a motor provided in an embodiment of the present disclosure;

[0053] Figure 5 A schematic diagram of iron loss sampling points under different working conditions provided by an embodiment of the present disclosure;

[0054] Figure 6 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0056] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0057] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0058] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0059] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0060] The disclosed embodiments provide a method for thermal parameter identification and rapid prediction of permanent magnet temperature, which can be applied to the monitoring of permanent magnet problems in electronic, electrical, mechanical, and transportation scenarios.

[0061] See also Figure 1 , is a flow chart of a method for thermal parameter identification and rapid prediction of permanent magnet temperature provided by an embodiment of the present disclosure. Figure 1 and Figure 2 As shown, the method mainly comprises the following steps:

[0062] Step 1: Analyze the motor structure and abstract the main thermal circuit nodes;

[0063] In specific implementation, the structure corresponding to the motor can be analyzed, the motor can be divided into n temperature nodes, the heat conduction between the temperature nodes can be analyzed, and then the heat conduction path between each node can be obtained. During the analysis process, this method only considers the main heat conduction path of the motor to reduce the problem of excessive matrix size caused by high-dimensional models, thereby improving calculation efficiency and ensuring the real-time performance of temperature prediction.

[0064] For example, for Figure 3 The synchronous permanent magnet motor shown in FIG. 1 is analyzed for its main thermal circuit. Due to its high central symmetry, its overall thermal circuit can be the same as the structure of the local symmetric minimum unit. For the stator yoke, that is, 2, it is connected to the winding in the slot and the stator teeth through a thermal circuit, and its heat is dissipated through external air cooling or water cooling. In this example, the motor adopts water cooling to dissipate heat, so node 1 corresponds to the temperature of the coolant. In this example, since the permanent magnet is embedded in the rotor core and the solid thermal conductivity is high, when analyzing the thermal circuit, the rotor core and the permanent magnet are regarded as objects of the same temperature, that is, they are merged into the same node 5. This also improves the calculation speed of subsequent temperature prediction. For the permanent magnet node 5, it is connected to the winding in the slot and the stator teeth through an air gap to form a thermal circuit, and at the same time, its heat is dissipated through air 6.

[0065] Step 2, extract the corresponding thermal circuit according to the main thermal circuit nodes and construct the initial thermal resistance network accordingly;

[0066] In the specific implementation, based on the analysis of step 1 above, the main thermal path of the motor is extracted, and based on this, the following is constructed: Figure 4The thermal resistance network shown in Figure 1 is composed of six nodes, where the temperatures θ1 and θ6 of nodes 1 and 6 are measured by corresponding temperature sensors and used as the reference temperatures for subsequent temperature predictions. Traditional thermal analysis often roughly assumes that the operating environment of the motor is room temperature, i.e. 20°C, and performs temperature rise analysis of the motor based on this temperature. However, this method will lead to temperature deviations caused by reference temperature deviations in the permanent magnet temperature prediction. The operating scenarios of the motor are diverse, and the ambient temperature cannot always be room temperature. The introduction of nodes 1 and 6 is precisely to consider the impact of ambient temperature on the temperature of each component of the motor, thereby improving the accuracy of temperature prediction.

[0067] Step 3, fitting the copper loss variation formula based on the experimental data under different armature current, speed measured data and temperature conditions;

[0068] Furthermore, the copper loss change formula is:

[0069]

[0070] Among them, P cu represents the copper loss of the winding, m is the number of phases of the motor, I a represents the armature winding current, θ w represents the temperature of the armature winding, n represents the motor speed, R w0 Indicates the resistance value of the armature winding at room temperature, f1(θ w ) represents the correction function of the armature winding resistance changing with temperature, f2(n) represents the correction function of the armature winding resistance changing with speed, R w Indicates the resistance of the motor armature winding.

[0071] Based on the above embodiment, before step 3, the method further includes:

[0072] Step 3.1, place the motor winding to be tested into a temperature control box, divide the temperature interval to be tested into multiple equal-length intervals, gradually increase the temperature in equal steps, and keep the motor speed at a fixed value;

[0073] Step 3.2: At each temperature point set above, use a bridge device to measure the armature winding resistance R at the corresponding temperature. w (θ w );

[0074] Step 3.3, based on the temperature θ obtained by the above sampling w The armature winding resistance R at the corresponding temperature w (θ w ) data, and construct the correction function f1(θ w )

[0075] f1(θw )=1+k1(θ w -θ0)

[0076] Where k1 represents the proportionality coefficient of the armature winding resistance changing with temperature;

[0077] Step 3.4, repeat steps 3.1 to 3.3, and use the binomial equation to fit the effect of skin effect on resistance as the speed changes, and obtain the correction function f2(n) for constructing the armature winding resistance as the speed changes.

[0078]

[0079] Among them, k2 represents the first-order proportional coefficient of the armature winding resistance changing with the speed, k3 represents the second-order proportional coefficient of the armature winding resistance changing with the speed, and n max Indicates the maximum design speed of the motor.

[0080] In specific implementation, generally speaking, the motor has different losses in different areas under different operating conditions. Accurately calculating the losses of each node in the thermal resistance network is crucial to the accuracy of temperature prediction. Therefore, it is necessary to fully consider the loss changes under different operating conditions to ensure that the thermal resistance network can truly reflect the distribution and transfer path of heat inside the motor, thereby improving the accuracy of temperature prediction. At the same time, the present invention introduces a loss-temperature variation coefficient to calculate the time-varying loss that changes with temperature, that is, the loss is dynamically adjusted as a function of the armature current, speed and node temperature.

[0081] The copper loss of the motor can be obtained by the following formula:

[0082]

[0083] Where P cu represents the copper loss of the winding; m is the number of phases of the motor, which is 3 in this example; I a Represents the current of the armature winding, which is directly measured by the sensor; θ w Indicates the temperature of the armature winding; n indicates the motor speed; R w0 Indicates the resistance value of the armature winding at room temperature; f1(θ w ) represents the correction function of the armature winding resistance changing with temperature, reflecting the change of copper resistivity due to temperature change; f2(n) represents the correction function of the armature winding resistance changing with speed, which is used to describe the influence of skin effect on resistance with speed change; R w (θ w ,n) represents the function of the motor armature winding changing with temperature and speed.

[0084] For the above formula, it is necessary to design experiments to fit the above f1(θ w) and f2(n). w ) as an example, it can be obtained by the following method:

[0085] 1) Place the motor winding to be tested in a temperature control box, divide the temperature range to be tested into multiple equal-length ranges, gradually increase the temperature in equal steps, and keep the motor speed at a fixed value (usually 0).

[0086] 2) At each temperature point set above, use a bridge device to measure the armature winding resistance R at the corresponding temperature w (θ w ).

[0087] 3) Based on the temperature θ obtained from the above sampling w The armature winding resistance R at the corresponding temperature w (θ w ) data to construct the corresponding fitting function. Generally speaking, the resistivity and temperature show an approximately linear relationship, and the corresponding correction function f1(θ w ), that is

[0088] f1(θ w )=1+k1(θ w -θ0) (2)

[0089] The same method can also be used to obtain the speed correction function f2(n). The difference from the above is that the effect of skin effect on resistance with speed change is usually fitted by the binomial equation, that is,

[0090]

[0091] Step 4, fitting the iron loss variation formula based on the experimental data under different armature current, speed simulation data and temperature conditions;

[0092] Furthermore, the iron loss change formula is:

[0093] P Fe_i =P Fe_i (I a ,θ0,n)·[1+k4·(θ i -θ0)]

[0094] Among them, P Fe_i It represents the iron loss of the ith node, and k4 is the iron loss temperature coefficient.

[0095] In specific implementation, since the iron loss of the motor is applied to the stator yoke node 2, the stator tooth node 4 and the permanent magnet node 5 respectively, it is necessary to calculate the loss of each node separately. Since it is difficult to measure the iron loss of each part, electromagnetic simulation is used for calculation. Based on the actual motor structural parameters, the corresponding finite element electromagnetic simulation model is constructed, and commercial software is used to simulate and calculate the different working conditions of the motor, that is, the loss values ​​of each node under different armature current and speed combinations are uniformly sampled. Figure 5 As shown in the figure, the cross-shaped point is the calibrated loss sampling point. Based on this point, the armature current and speed in the finite element simulation software are set respectively, and the corresponding loss value of each node is obtained while keeping other conditions unchanged.

[0096] Similarly, based on the node loss values ​​obtained from the above simulation, the motor stator yoke node 2 loss P is established. loss_sy 、Stator tooth node 4 loss P loss_st and permanent magnet node 5 loss P pm The relationship between armature current and speed is

[0097] P loss_sy =P loss_sy (I a ,θ0,n) (4)

[0098] P loss_st =P loss_st (I a ,θ0,n) (5)

[0099] P pm =P pm (I a ,θ0,n) (6)

[0100] Since the above losses are all derived from a fixed temperature (usually room temperature), the temperature must also be considered. Here, a core loss temperature correction coefficient k4 is introduced, that is,

[0101] P loss_sy (I a ,θ sy ,n)=P loss_sy (I a ,θ0,n)·[1+k4·(θ sy -θ0)] (7)

[0102] P loss_st (I a ,θ st ,n)=P loss_st (I a ,θ0,n)·[1+k4·(θ st -θ0)] (8)

[0103] P pm (I a ,θ pm ,n)=P pm (I a ,θ0,n)·[1+k4·(θ pm -θ0)] (9)

[0104] Since iron loss is difficult to measure, the iron loss temperature correction coefficient k4 is indirectly calculated using the following method: Place the motor in a constant temperature box and use a short-time measurement method. The short-time temperature rise is small and the temperature can be approximately regarded as a preset constant value.

[0105] According to the law of conservation of energy, the input power is equal to the motor output power plus the loss, that is,

[0106] P input (I a ,θ s ,n)=P out +P cu (I a ,θ s ,n)+P loss_sy (I a ,θ s ,n)

[0107] +P loss_st (I a ,θ s ,n)+P pm (I a ,θ s ,n)(10)

[0108] Thus, the iron loss value at different temperatures can be obtained. By combining the above (7)-(10), the value of the iron loss temperature correction coefficient k4 can be solved. Through this method, the relationship model of the iron loss of each node with the armature current, speed and temperature can be established.

[0109] Step 5, constructing a transient temperature solution matrix according to the initial thermal resistance network, the copper loss change formula and the iron loss change formula;

[0110] Furthermore, the expression of the transient temperature solution matrix is:

[0111] Δθ=C -1 Gθ+C -1 P

[0112] Among them, Δθ represents the temperature rise matrix, C is the heat capacity matrix in the thermal resistance network, G represents the thermal resistance matrix in the thermal resistance network, and P is the copper loss change formula and the iron loss change formula;

[0113] Δθ=[Δθ1Δθ2…ΔθN-M ] T

[0114] C=diag(C1,C2,…,C N-M )

[0115]

[0116] P=[P loss_1 +P eq_1 P loss_2 +P eq_2 …P loss_(N-M) +P eq_(N-M) ] T .

[0117] Where N represents the total number of nodes in the thermal resistance network, and M represents the number of ambient temperature nodes in the thermal resistance network;

[0118] In specific implementation, based on the initial thermal resistance network, copper loss change formula and iron loss change formula, the transient temperature solution matrix shown below can be established.

[0119] Δθ=C -1 Gθ+C -1 P (11)

[0120] Wherein, Δθ represents the temperature rise matrix, C represents the heat capacity matrix in the thermal resistance network, G represents the thermal resistance matrix in the thermal resistance network, and P represents the copper loss change formula and the iron loss change formula.

[0121] In this example, the temperatures of nodes 1 and 6 are measured by sensors in real time. The six-node network in this example can be simplified to a four-node network, that is, the heat flows between nodes 1 and 2, and between nodes 5 and 6 are regarded as heat sources directly loaded on nodes 2 and 6, respectively.

[0122] Therefore, the above matrices can be expressed as:

[0123] Δθ=[Δθ2Δθ3Δθ4Δθ5] T (12)

[0124] C=diag(C sy ,C w ,C st ,C pm ) (13)

[0125]

[0126]

[0127] Step 6, performing temperature rise tests on the motor at different armature currents and speeds for multiple times, and using sensors to periodically record the temperature conditions of the thermal circuit nodes to obtain the test results of the motor;

[0128] In specific implementation, the motor can be subjected to multiple temperature rise tests at different armature currents and rotation speeds, and sensors can be used to periodically record the temperature conditions of the above nodes to obtain the test results of the motor.

[0129] Step 7, according to the test results and the transient temperature solution matrix, use the particle swarm algorithm to identify the parameters of the thermal resistance and thermal capacitance in the initial thermal resistance network and optimize based on the least squares objective function to solve the target thermal resistance network of the motor;

[0130] Furthermore, the expression of the least squares objective function is:

[0131]

[0132] In specific implementation, the least squares objective function is established, that is,

[0133]

[0134] This objective function represents the square sum of the errors between the k+1th sensor observation and the k+1th model prediction. This function is used as the objective function, and the value of the thermal resistance network matrix is ​​used as the independent variable. A large amount of data is used to minimize the objective function based on the particle swarm optimization algorithm to obtain a set of thermal resistance network parameters that can make the thermal analysis results consistent with the sensor observations. Repeat the experiment to establish the relationship between the thermal resistance network and the armature current, speed and temperature.

[0135] The parameter settings of the particle swarm in this paper are as follows:

[0136] Table 1

[0137]

[0138] Step 8: Based on the target thermal resistance network, the copper loss change formula and the iron loss change formula, combined with the motor operating condition information, the permanent magnet temperature of the motor is predicted in real time.

[0139] In the specific implementation, based on the network parameters of the target thermal resistance network obtained above, the armature current, speed and temperature information of nodes 1 and 6 are identified in real time by sensors, and the change of permanent magnet temperature in the corresponding time can be predicted. However, due to the performance limitations of the computer itself, it is not necessary to update and iterate the permanent magnet temperature prediction at all times. Therefore, a discretization method is adopted for the permanent magnet temperature prediction, that is, a fixed time period is set, that is, ΔT. Every ΔT period, the relevant information is observed once, and the corresponding information is used to iteratively predict the temperature change of the permanent magnet. The selection of ΔT should comprehensively consider the computer performance and the accuracy of the prediction. When ΔT is small, the prediction is more accurate but the computer performance requirements are higher. For this example, the value of ΔT is 1s.

[0140] The corresponding temperature iteration equation becomes

[0141]

[0142] θ(k+1)=θ(k)+Δθ(k) (17)

[0143] That is, at the kth generation, the armature current, speed and temperature information of nodes 1 and 6 are obtained, and based on the temperature information of each node of the kth generation predicted in the previous step, the heat capacity, thermal conductivity and heat source network in the corresponding thermal resistance network are calculated, and then the temperature information of each node of the k+1th generation is calculated. In this way, the rapid prediction of the permanent magnet temperature considering the initial temperature and time-varying loss can be achieved.

[0144] The thermal parameter identification and permanent magnet temperature rapid prediction method provided in this embodiment simplifies the motor into a low-dimensional thermal circuit model, regards the ambient temperature as a time-varying variable, and regards the loss, thermal resistance and thermal capacity of each node as dynamic variables related to the armature current, speed and temperature, thereby constructing a fast and accurate permanent magnet temperature prediction model, thereby improving the prediction efficiency, accuracy and real-time performance.

[0145] See also Figure 6 The embodiment of the present disclosure further provides an electronic device 60, which includes: at least one processor and a memory connected to the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the thermal parameter identification and permanent magnet temperature rapid prediction method in the above method embodiment.

[0146] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the thermal parameter identification and permanent magnet temperature rapid prediction method in the aforementioned method embodiment.

[0147] The embodiments of the present disclosure also provide a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the thermal parameter identification and permanent magnet temperature rapid prediction method in the aforementioned method embodiment.

[0148] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device 60 suitable for implementing the embodiment of the present disclosure. The electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0149] like Figure 6 As shown, the electronic device 60 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0150] Typically, the following devices may be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 609. The communication device 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 60 with various devices is shown in the figure, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0151] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0152] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0153] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0154] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can execute the relevant steps of the method embodiment.

[0155] Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can perform the relevant steps of the method embodiment.

[0156] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0157] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0158] The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware.

[0159] It should be understood that various parts of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

[0160] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for thermal parameter identification and rapid prediction of permanent magnet temperature, characterized in that: include: Step 1: Analyze the motor structure and abstract the main thermal circuit nodes; Step 2, extract the corresponding thermal circuit according to the main thermal circuit nodes and construct the initial thermal resistance network accordingly; Step 3, fitting the copper loss variation formula based on the experimental data under different armature current, speed measured data and temperature conditions; Step 4, fitting the iron loss variation formula based on the experimental data under different armature current, speed simulation data and temperature conditions; Step 5, constructing a transient temperature solution matrix according to the initial thermal resistance network, the copper loss change formula and the iron loss change formula; Step 6, performing temperature rise tests on the motor at different armature currents and speeds for multiple times, and using sensors to periodically record the temperature conditions of the thermal circuit nodes to obtain the test results of the motor; Step 7, according to the test results and the transient temperature solution matrix, use the particle swarm algorithm to identify the parameters of the thermal resistance and thermal capacitance in the initial thermal resistance network and optimize based on the least squares objective function to solve the target thermal resistance network of the motor; Step 8: Based on the target thermal resistance network, the copper loss change formula and the iron loss change formula, combined with the motor operating condition information, the permanent magnet temperature of the motor is predicted in real time.

2. The method according to claim 1, characterized in that The copper loss change formula is: Among them, P cu represents the copper loss of the winding, m is the number of phases of the motor, I a represents the current of the armature winding, θ w represents the temperature of the armature winding, n represents the motor speed, R w0 Indicates the resistance value of the armature winding at room temperature, f1(θ w ) represents the correction function of the armature winding resistance changing with temperature, f2(n) represents the correction function of the armature winding resistance changing with speed, R w Indicates the resistance of the motor armature winding.

3. The method according to claim 2, characterized in that Before step 3, the method further includes: Step 3.1, place the motor winding to be tested into a temperature control box, divide the temperature interval to be tested into multiple equal-length intervals, gradually increase the temperature in equal steps, and keep the motor speed at a fixed value; Step 3.2: At each temperature point set above, use a bridge device to measure the armature winding resistance R at the corresponding temperature. w (θ w ); Step 3.3, based on the temperature θ obtained by the above sampling w The armature winding resistance R at the corresponding temperature w (θ w ) data, and construct the correction function f1(θ w ) f1(θ w )=1+k1(θ w -θ0) Where k1 represents the proportionality coefficient of the armature winding resistance changing with temperature; Step 3.4, repeat steps 3.1 to 3.3, and use the binomial equation to fit the effect of skin effect on resistance as the speed changes, and obtain the correction function f2(n) for constructing the armature winding resistance as the speed changes. Among them, k2 represents the first-order proportional coefficient of the armature winding resistance changing with the speed, k3 represents the second-order proportional coefficient of the armature winding resistance changing with the speed, and n max Indicates the maximum design speed of the motor.

4. The method according to claim 3, characterized in that The iron loss change formula is: P Fe_i =P Fe_i (I a ,θ0,n)·[1+k4·(θ i -θ0)] Among them, P Fe_i It represents the iron loss of the ith node, and k4 is the iron loss temperature coefficient.

5. The method according to claim 4, characterized in that The expression of the transient temperature solution matrix is: Δθ=C -1 Gθ+C -1 P Among them, Δθ represents the temperature rise matrix, C is the heat capacity matrix in the thermal resistance network, G represents the thermal resistance matrix in the thermal resistance network, and P is the copper loss change formula and the iron loss change formula; Δθ=[Δθ1Δθ2…Δθ N-M ] T C=diag(C1,C2,…,C N-M ) P=[P loss_1 +P eq_1 P loss_2 +P eq_2 …P loss_(N-M) +P eq_(N-M) ] T 。 Wherein, N represents the total number of nodes of the thermal resistance network, and M represents the number of ambient temperature nodes of the thermal resistance network.

6. The method according to claim 5, characterized in that The expression of the least squares objective function is:

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the thermal parameter identification and permanent magnet temperature rapid prediction method described in any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the thermal parameter identification and permanent magnet temperature rapid prediction method according to any one of claims 1 to 6.

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