Thermal parameter identification and permanent magnet temperature fast prediction method, device and medium

Abstract

The embodiment of the present disclosure provides a kind of thermal parameter identification and permanent magnet temperature fast prediction method, equipment and medium, belong to data identification technical field, specifically include: analysis motor structure, abstract main thermal path node;Initial thermal resistance network is constructed;Fitting copper consumption change formula;Fitting iron consumption change formula;According to initial thermal resistance network, copper consumption change formula and iron consumption change formula, construct transient temperature solution matrix;Motor is respectively carried out multiple different armature current, rotational speed temperature rise test;According to test result and transient temperature solution matrix, using particle swarm algorithm to the thermal resistance and heat capacity in initial thermal resistance network are carried out parameter identification and are optimized based on least square objective function, solve the target thermal resistance network of motor;Based on target thermal resistance network, copper consumption change formula and iron consumption change formula, combine motor working condition information, the permanent magnet temperature of motor is carried out real-time prediction.Based on the scheme of the present disclosure, improve the prediction efficiency, accuracy and real-time.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of measurement, and in particular to a thermal parameter identification and permanent magnet temperature rapid prediction method, device and medium. BACKGROUND

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

[0003] For a permanent magnet motor in which the permanent magnet is installed on the rotor, there are usually two methods for obtaining the temperature of the permanent magnet. One is to directly install a sensor near the permanent magnet, which directly measures the temperature of the permanent magnet, but also brings problems such as complex installation and signal transmission interference by the magnetic field. The second is to predict the temperature of the permanent magnet by a model prediction method. However, the establishment of the thermal network model in the current method is too idealistic, and it is usually assumed that the operating temperature of the motor is room temperature and remains unchanged, and the treatment of the loss and thermal resistance is also relatively simple. Most models assume that the thermal resistance remains constant, or only consider the change of the loss with the operating condition, but ignore the complex dynamic changes of the loss and thermal resistance in actual operation. At the same time, the dimension of part of the thermal resistance network matrix is huge, which brings great difficulty to the rapid prediction of the temperature of the permanent magnet, and it is difficult to meet the demand of real-time monitoring.

[0004] Therefore, there is an urgent need for a thermal parameter identification and permanent magnet temperature rapid prediction method with high prediction efficiency, accuracy and real-time performance. SUMMARY

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

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

[0007] Step 1, analyzing the structure of the motor and abstracting the main thermal path nodes;

[0008] Step 2, refining the corresponding thermal path according to the main thermal path nodes and constructing an initial thermal resistance network accordingly;

[0009] Step 3, fitting a copper loss change formula based on different armature current, rotational speed measured data and temperature condition experimental data;

[0010] Step 4, fitting an iron loss change formula based on different armature current, rotational speed simulation data and temperature condition experimental data;

[0011] Step 5, constructing a transient temperature solving 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 multiple different armature currents and rotating speeds respectively, and using sensors to record the temperature of the thermal circuit nodes at different times to obtain the test results of the motor;

[0013] Step 7, according to the test results and the transient temperature solving matrix, using a particle swarm algorithm to identify the thermal resistance and thermal capacity in the initial thermal resistance network and optimizing based on a least square 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, combining the motor operating condition information, real-time predicting the temperature of the permanent magnet of the motor.

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

[0016]

[0017] Wherein, 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 rotating speed of the motor, R w0 represents the resistance value of the armature winding at normal temperature, f1(θ w ) represents a correction function of the resistance of the armature winding changing with temperature, f2(n) represents a correction function of the resistance of the armature winding changing with rotating speed, and R w represents the resistance value of the motor armature winding.

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

[0019] Step 3.1, placing the winding of the motor to be tested into a temperature control box, dividing the temperature interval to be tested into multiple equal length intervals, gradually increasing the temperature in an equal step manner, and keeping the rotating speed of the motor as a fixed value;

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

[0021] Step 3.3, based on the temperature θ w obtained by sampling and the resistance value R w (θ w) and the data of the armature winding resistance with the temperature change is fitted by a linear fitting to build a correction function f1(θ w )

[0022] f1(θ w )=1+k1(θ w -θ0)

[0023] wherein k1 represents a proportional coefficient of the armature winding resistance with the temperature change;

[0024] Step 3.4, repeating steps 3.1 to 3.3, the influence of the skin effect with the change of the rotating speed on the resistance is fitted by a binomial equation to obtain a correction function f2(n) of the armature winding resistance with the change of the rotating speed

[0025]

[0026] wherein k2 represents a linear proportional coefficient of the armature winding resistance with the change of the rotating speed, k3 represents a quadratic proportional coefficient of the armature winding resistance with the change of the rotating speed, n max represents the highest rotating speed of the motor design.

[0027] According to a specific implementation manner 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] wherein P Fe_i represents the iron loss size of the i th node, and k4 is an iron loss temperature coefficient.

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

[0031] Δθ=C -1 Gθ+C -1 P

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

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

[0039]

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

[0041] at least one processor; and

[0042] a memory in communication connection with the at least one processor; wherein

[0043] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the thermal parameter identification and permanent magnet temperature fast prediction method in the foregoing first aspect or any implementation manner of the first aspect.

[0044] In a third aspect, the embodiment of the present disclosure further provides a non-transitory computer readable storage medium, which stores computer instructions for causing the computer to execute the thermal parameter identification and permanent magnet temperature fast prediction method in the foregoing first aspect or any implementation manner of the first aspect.

[0045] In a fourth aspect, the embodiment of the present disclosure further provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, when the program instructions are executed by a computer, causing the computer to execute the thermal parameter identification and permanent magnet temperature fast prediction method in the foregoing first aspect or any implementation manner of the first aspect.

[0046] The thermal parameter identification and permanent magnet temperature rapid prediction scheme in the embodiments of the present disclosure includes: step 1, analyzing the motor structure and abstracting main thermal path nodes; step 2, refining the corresponding thermal path according to the main thermal path nodes and constructing an initial thermal resistance network accordingly; step 3, fitting a copper loss change formula based on different armature currents, rotational speed measured data and temperature conditions and experimental data; step 4, fitting an iron loss change formula based on different armature currents, rotational speed simulation data and temperature conditions and experimental data; step 5, constructing a transient temperature solving 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 under different armature currents and rotational speeds respectively for multiple times, and recording the temperature conditions of the thermal path nodes at regular time intervals by using sensors to obtain the test results of the motor; step 7, according to the test results and the transient temperature solving matrix, using a particle swarm algorithm to identify the thermal resistance and thermal capacity in the initial thermal resistance network and optimizing based on a least square objective function to solve the target thermal resistance network of the motor; and step 8, based on the target thermal resistance network, the copper loss change formula and the iron loss change formula, combining motor operating condition information to perform real-time prediction on the permanent magnet temperature of the motor.

[0047] The beneficial effects of the embodiments of the present disclosure are: through the scheme of the present disclosure, in view of the problems of too large dimension of the traditional thermal resistance network, not considering the environmental temperature and time-varying loss, etc., by simplifying the motor into a low-dimensional thermal path model, regarding the environmental temperature as a time-varying variable, and regarding the loss, thermal resistance and thermal capacity of each node as dynamic variables related to the armature current, rotational speed and temperature, a fast and accurate permanent magnet temperature prediction model is constructed, and the prediction efficiency, accuracy and real-time performance are improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0049] Figure 1 A flowchart of a thermal parameter identification and permanent magnet temperature rapid prediction method provided by the embodiments of the present disclosure is shown in the figure;

[0050] Figure 2 A specific implementation process diagram of a thermal parameter identification and permanent magnet temperature rapid prediction method provided by the embodiments of the present disclosure is shown in the figure;

[0051] Figure 3 A certain motor cross-sectional view and thermal path diagram provided by the embodiments of the present disclosure is shown in the figure, wherein 1 is a cooling liquid node, 2 is a stator yoke node, 3 is an in-slot winding node, 4 is a stator tooth node, 5 is a permanent magnet and rotor node, and 6 is an air node.

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

[0053] Figure 5 A certain motor thermal resistance network configuration schematic diagram provided for an embodiment of the present disclosure;

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

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

[0056] The other advantages and effects of the present disclosure can be easily understood by those skilled in the art from the content disclosed in the specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, not all the embodiments. The present disclosure can also be implemented or applied by other different specific embodiments, and the details in the specification can be modified or changed based on different views 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 those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0057] It should be noted that the various aspects of the embodiments described below are within the scope of the appended claims. It should be apparent that the aspects described herein can be embodied in a wide variety of forms and that any specific structure and / or function described herein is merely illustrative. Based on the teachings provided herein one skilled in the art should be able to contemplate these and similar implementations without departing from the scope of the disclosure. For example, an apparatus could take the form of any of a number of types of devices involving structure for performing the functionality described herein, and / or a method could take the form of any of a number of types of methods involving performance of the function described herein. In addition, the aspects described herein could be implemented across many disparate software or hardware systems. For example, various aspects of the disclosure can be embodied in a processing system, such as a computer, a microprocessor, or a microcontroller.

[0058] It should also be noted that the drawings included in the following embodiments are only a schematic illustration of the basic idea of the present disclosure, and only show the components related to the present disclosure in the drawings, not drawn according to the number, shape and size of the components in actual implementation, and the shape, number and proportion of each component in actual implementation can be changed arbitrarily, and the layout pattern of the components can also be more complex.

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

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

[0061] See Figure 1 This is a schematic flowchart illustrating a method for thermal parameter identification and rapid prediction of permanent magnet temperature provided in an embodiment of this disclosure. Figure 1 and Figure 2 As shown, the method mainly includes the following steps:

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

[0063] In practical implementation, the structure of the motor can be analyzed, dividing the motor into n temperature nodes. The heat conduction between these nodes can then be analyzed to obtain the heat conduction paths between each node. During the analysis, this method only considers the main heat conduction paths of the motor to reduce the problem of excessively large matrices caused by high-dimensional models, thereby improving computational efficiency and ensuring the real-time nature of temperature prediction.

[0064] For example, for such Figure 3 The synchronous permanent magnet motor shown is analyzed for its main thermal path. Due to its high central symmetry, its overall thermal path can be considered the same as the structure of its local symmetrical minimum unit. For the stator yoke, i.e., 2, it forms a thermal path connection with the slot windings and stator teeth. Its heat is dissipated through external air cooling or water cooling. In this example, the motor uses water cooling, so node 1 corresponds to the temperature of the coolant. In this example, since the permanent magnet is embedded in the mover core, and solids have a high thermal conductivity, the mover core and permanent magnet are considered to be at the same temperature when analyzing the thermal path, i.e., merged into the same node 5. This also improves the calculation speed of subsequent temperature prediction. For the permanent magnet node 5, it forms a thermal path connection with the slot windings and stator teeth through air gaps, and its heat is dissipated through air 6.

[0065] Step 2: Extract the corresponding thermal paths based on the main thermal path nodes and construct the initial thermal resistance network accordingly;

[0066] In practical implementation, based on the analysis in step 1 above, the main thermal path of the motor is extracted, and based on this, a structure is constructed as follows: Figure 4The thermal resistance network is shown. The network consists of six nodes, where the temperature θ1 and θ6 of node 1 and node 6 are measured by corresponding temperature sensors and are used as reference temperatures for subsequent temperature prediction. Traditional thermal analysis often roughly assumes that the operating environment of the motor is room temperature, i.e. 20℃, and conducts temperature rise analysis of the motor based on this temperature. However, this method can cause temperature deviation caused by reference temperature deviation in permanent magnet temperature prediction. The running scene of the motor is diverse, and the environmental temperature cannot always be room temperature. The introduction of nodes 1 and 6 is to consider the influence of environmental temperature on the temperature of each part of the motor, so as to improve the accuracy of temperature prediction.

[0067] Step 3, based on different armature currents, rotational speed measured data and temperature conditions, the copper loss change formula is fitted;

[0068] Further, the copper loss change formula is

[0069]

[0070] Wherein, 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 rotational speed of the motor, R w0 represents the resistance value of the armature winding at room temperature, f1(θ w ) represents the correction function of the resistance of the armature winding with temperature, f2(n) represents the correction function of the resistance of the armature winding with rotational speed, and R w represents the resistance value of the motor armature winding.

[0071] On the basis of the above embodiment, before step 3, the method further comprises:

[0072] Step 3.1, the winding of the motor to be measured is placed in a temperature control box, the temperature interval to be measured is divided into a plurality of equal intervals, and the temperature is gradually increased in an equal step manner, and the rotational speed of the motor is kept as a fixed value;

[0073] Step 3.2, at each temperature point set above, the armature winding resistance R w (θ w ) at the corresponding temperature is measured by using a bridge device;

[0074] Step 3.3, based on the temperature θ w and the armature winding resistance value R w (θ w ) at the corresponding temperature obtained by sampling, the correction function f1(θ w ) of the resistance of the armature winding with temperature is constructed by linear fitting

[0075] f1(θw ) = 1 + k1(θ w - θ0)

[0076] Wherein, k1 represents the proportionality coefficient of the armature winding resistance varying with temperature;

[0077] Step 3.4, repeat steps 3.1 to 3.3, the influence of skin effect varying with rotating speed on resistance is fitted by binomial equation, to obtain the correction function f2(n) of constructing the armature winding resistance varying with rotating speed

[0078]

[0079] Wherein, k2 represents the linear proportionality coefficient of the armature winding resistance varying with rotating speed, k3 represents the quadratic proportionality coefficient of the armature winding resistance varying with rotating speed, n max represents the highest rotating speed of the motor design.

[0080] In specific implementation, generally speaking, the losses of each region of the motor are different under different operating conditions. Accurate calculation of the losses of each node in the thermal resistance network is crucial for the accuracy of temperature prediction. Therefore, it is necessary to fully consider the loss changes under different conditions to ensure that the thermal resistance network can truly reflect the distribution and transmission path of the internal heat of the motor, thereby improving the accuracy of temperature prediction. At the same time, the present application introduces a loss-temperature variation coefficient to calculate the time-varying loss varying with temperature, i.e. the loss is dynamically adjusted as a function of armature current, rotating speed and node temperature.

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

[0082]

[0083] In the formula, 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 a sensor; θ w represents the temperature of the armature winding; n represents the rotating speed of the motor; R w0 represents the resistance value of the armature winding at normal temperature; f1(θ w ) represents the correction function of the armature winding resistance varying with temperature, reflecting the resistance value change caused by the temperature variation of the resistivity of copper; f2(n) represents the correction function of the armature winding resistance varying with rotating speed, used to describe the influence of skin effect varying with rotating speed on resistance; R w (θ w , n) represents the function of the motor armature winding varying with temperature and rotating speed.

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

[0085] 1) Put the motor winding to be tested into the temperature control box, divide the temperature interval to be tested into multiple equal intervals, and gradually increase the temperature in equal steps while keeping the motor speed fixed (usually 0).

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

[0087] 3) Based on the temperature θ w and the armature winding resistance value R w (θ w ) at the corresponding temperature obtained by sampling above, construct the corresponding fitting function. Generally speaking, the resistivity and temperature show an approximate linear relationship, and the corresponding correction function f1(θ w ) can be obtained by linear fitting, 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), which is different from the above in that the effect of skin effect on resistance with speed change is usually fitted by a binomial equation, that is

[0090]

[0091] Step 4, based on different armature current, speed simulation data and temperature condition experimental data fitting iron loss change formula;

[0092] Further, the iron loss change formula is

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

[0094] Where P Fe_i represents the iron loss size of the i-th node, and k4 is the iron loss temperature coefficient.

[0095] In practical implementation, since the iron losses of this motor are applied to stator yoke node 2, stator tooth node 4, and permanent magnet node 5 respectively, it is necessary to calculate the losses of each node separately. Because measuring the iron losses of each part in practice is difficult, electromagnetic simulation is used for calculation. Based on the actual motor structural parameters, a corresponding finite element electromagnetic simulation model is constructed, and commercial software is used to simulate and calculate the loss values ​​of each node under different operating conditions, i.e., different combinations of armature current and speed, with uniform sampling. For example... Figure 5 As shown in the figure, the cross-shaped points are the calibrated loss sampling points. Based on these points, the armature current and rotational speed in the finite element simulation software are set respectively, and the corresponding node loss values ​​are obtained while keeping other conditions constant.

[0096] Similarly, based on the node loss values ​​obtained from the above simulation, the loss P at node 2 of the stator yoke of the motor 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 rotational speed, i.e.

[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), temperature must also be taken into account. Therefore, an iron loss temperature correction factor k4 is introduced here, i.e.

[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 the iron loss is difficult to measure, the iron loss temperature correction coefficient k4 is indirectly obtained as follows. The motor is placed in an oven, and a short-time measurement method is used. The short-time temperature rise is small, and can be approximately regarded as a constant value with a preset temperature.

[0105] According to the law of conservation of energy, the input electric 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 values at different temperatures can be obtained. By combining the above (7)-(10), the value of the iron loss temperature correction coefficient k4 can be obtained. Through this method, the relationship model of the iron loss of each node with the armature current, the speed and the temperature change is established.

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

[0110] Further, the expression of the transient temperature solving matrix is

[0111] Δθ=C -1 Gθ+C -1 P

[0112] Wherein, 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 of the thermal resistance network, and M represents the number of ambient temperature nodes of the thermal resistance network;

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

[0119] Δθ = C -1 Gθ + C -1 P (11)

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

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

[0122] Therefore, the above matrixes can be respectively represented as:

[0123] Δθ = [Δθ2Δθ3Δθ4Δθ5] T (12)

[0124] C = diag(C sy 1, C w 2, C st 3, C pm 4) (13)

[0125]

[0126]

[0127] Step 6, temperature rise tests are performed on the motor at different armature currents and speeds, and sensors are used to record the temperature of the nodes at different times to obtain the test results of the motor;

[0128] In specific implementation, the motor can be tested at different armature currents and speeds, and sensors can be used to record the temperature of the nodes at different times to obtain the test results of the motor.

[0129] Step 7, according to the test results and the transient temperature solving matrix, the particle swarm algorithm is used to identify the thermal resistance and heat capacity in the initial thermal resistance network, and the least square objective function is optimized to solve the target thermal resistance network of the motor;

[0130] Further, the expression of the least square objective function is

[0131]

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

[0133]

[0134] The objective function represents the sum of squares of errors between the k+1th sensor observation value and the k+1th model prediction data. With the function as the objective function and the value of the thermal resistance network matrix as the independent variable, a large amount of data is used to optimize the least square objective function based on the particle swarm optimization algorithm, so as 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 change of armature current, speed and temperature

[0135] In this paper, the parameters of the particle swarm are set 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, and combined with the motor working condition information, the permanent magnet temperature of the motor is predicted in real time.

[0139] In a specific implementation, based on the network parameters of the target thermal resistance network obtained above, the armature current, the rotating speed, and the temperature information of nodes 1 and 6 are identified in real time through a sensor, so as to predict the change of the permanent magnet temperature in the corresponding time. However, due to the limitation of the computer performance, the permanent magnet temperature prediction does not need to be updated and iterated at all times. Therefore, the permanent magnet temperature prediction adopts a discretization manner, that is, a fixed time period, that is, ΔT, is set. Every ΔT, the relevant information is observed, and the temperature change of the permanent magnet is iteratively predicted by using the corresponding information. The selection of ΔT needs to comprehensively consider the computer performance and the prediction accuracy. When ΔT is small, the prediction is more accurate but the computer performance requirement is higher. For this example, ΔT is 1s.

[0140] The corresponding temperature iteration equation is changed to

[0141]

[0142] θ(k+1)=θ(k)+Δθ(k) (17)

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

[0144] The thermal parameter identification and the fast permanent magnet temperature prediction method provided by the embodiment simplifies the motor into a low-dimensional thermal circuit model, regards the environmental temperature as a time-varying variable, and regards the loss, the thermal resistance, and the heat capacity of each node as dynamic variables related to the armature current, the rotating speed, and the temperature, so as to construct a fast and accurate permanent magnet temperature prediction model, and improve the prediction efficiency, the accuracy, and the real-time performance.

[0145] Referring to Figure 6 The electronic device 60 includes at least one processor and a memory connected with the at least one processor in communication. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the thermal parameter identification and the fast permanent magnet temperature prediction method in the foregoing method embodiments.

[0146] The embodiment of the disclosure also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the thermal parameter identification and the fast permanent magnet temperature prediction method in the foregoing method embodiments.

[0147] This disclosure also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the thermal parameter identification and permanent magnet temperature rapid prediction method described in the foregoing method embodiments.

[0148] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device 60 suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

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

[0150] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 60 to communicate wirelessly or wiredly with other devices to exchange data. Although an electronic device 60 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0151] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0152] It should be noted that the computer readable medium described above in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium may, for example, be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as part of a carrier wave, in which the computer readable program code is carried. Such a propagated data signal can take any of 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 can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium can be transmitted by any suitable medium, including, but not limited to, wire, cable, RF (radio frequency), or the like, or any suitable combination of the above.

[0153] The computer readable medium described above can be included in the electronic device described above; or can exist separately from the electronic device and be not assembled into the electronic device.

[0154] The computer readable medium described above carries one or more programs, which, when executed by the electronic device, enable the electronic device to perform the relevant steps of the method embodiment described above.

[0155] Alternatively, the computer-readable medium described above can carry one or more programs, which can be executed by the electronic device, and cause the electronic device to perform the relevant steps of the above method embodiments.

[0156] Computer program code for carrying out operations of the present disclosure can be written in any one or more programming languages, including object-oriented programming languages such as Java, Smalltalk, C++, or conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer can 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0157] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or in the opposite order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of dedicated hardware-based systems and computer instructions.

[0158] The units described in the embodiments of the present disclosure can be implemented by means of software, or by means of hardware.

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

[0160] The above merely provides the specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present disclosure, which should be covered in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A method for thermal parameter identification and fast prediction of permanent magnet temperature, characterized in that, The method comprises: Step 1, analyzing the motor structure and abstracting a thermal path node; Step 2, refining a corresponding thermal path according to the thermal path node and constructing an initial thermal resistance network according to the thermal path; Step 3, fitting a copper loss change formula based on experimental data under different armature currents, rotational speeds and temperature conditions; Step 4, fitting an iron loss change formula based on experimental data under different armature currents, simulation data and temperature conditions; Step 5, constructing a transient temperature solving matrix according to the initial thermal resistance network, the copper loss change formula and the iron loss change formula, wherein an expression of the transient temperature solving matrix is Δθ = C -1 Gθ + C -1 P wherein Δθ represents a temperature rise matrix, C is a heat capacity matrix in the thermal resistance network, G represents a thermal resistance matrix in the thermal resistance network, and P is the copper loss change formula and the iron loss change formula; Δθ = [Δθ1 Δθ2... ΔθN]T 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 a total number of nodes of the thermal resistance network, and M represents a number of environmental temperature nodes of the thermal resistance network; Step 6, performing temperature rise tests on the motor under different armature currents and rotational speeds respectively for multiple times, and recording temperatures of the thermal path nodes at different times by using sensors to obtain test results of the motor; Step 7, performing parameter identification on the thermal resistance and the heat capacity in the initial thermal resistance network by using a particle swarm algorithm based on the test results and the transient temperature solving matrix, and optimizing the initial thermal resistance network based on a least square objective function to solve a target thermal resistance network of the motor; Step 8, performing real-time prediction on a permanent magnet temperature of the motor based on the target thermal resistance network, the copper loss change formula and the iron loss change formula and combined with motor working condition information.

2. The method of claim 1, wherein, The copper loss change formula is where P cu represents copper loss of the winding, m represents the number of phases of the motor, I a represents current of the armature winding, θ w represents temperature of the armature winding, n represents motor speed, R w0 represents resistance value of the armature winding at normal temperature, f1(θ w ) represents a correction function of the armature winding resistance varying with temperature, f2(n) represents a correction function of the armature winding resistance varying with speed, R w represents resistance value of the motor armature winding.

3. The method of claim 2, wherein, Before the step 3, the method further comprises: Step 3.1, placing the winding of the motor to be tested into a temperature control box, dividing the temperature interval to be tested into multiple equal intervals, gradually increasing the temperature in an equal step manner, and keeping the rotational speed of the motor as a fixed value; Step 3.2, at each temperature point set above, the armature winding resistance R at the corresponding temperature is measured by using the bridge device w (θ w ); Step 3.

3. Obtain temperature θ based on sampling w corresponding to the temperature of the armature winding resistance R w (θ w ) of the data, by linear fitting to build the armature winding resistance correction function f1(θ w ) with temperature f1(θ w ) = 1 + k1(θ w - θ0) wherein k1 represents a proportional coefficient of the armature winding resistance changing with temperature; Step 3.4, repeating steps 3.1 to 3.3, and fitting the influence of the skin effect changing with the rotational speed on the resistance by using a binomial equation to obtain a correction function f2(n) of the armature winding resistance changing with the rotational speed wherein k2 represents a first proportional coefficient of the armature winding resistance with respect to the rotational speed, k3 represents a second proportional coefficient of the armature winding resistance with respect to the rotational speed, n max represents the highest rotational speed of the motor design.

4. The method of claim 3, wherein, The iron loss change formula is P Fe_i = P Fe_i (I a , θ0,n) · [1 + k4 · (θ i - θ0)] where P Fe_i represents the size of the iron loss of the i-th node, and k4 is the iron loss temperature coefficient.

5. The method of claim 4, wherein, An expression of the least square objective function is 6. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the thermal parameter identification and permanent magnet temperature rapid prediction method in any one of the preceding claims 1-5. 7.A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the thermal parameter identification and permanent magnet temperature rapid prediction method in any one of the preceding claims 1-5.

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