Air-cooled motor winding temperature prediction method and device

Abstract

The invention provides an air-cooled motor winding temperature prediction method and device, relates to the technical field of electric driving, and is used for predicting the temperature of an air-cooled motor winding in scenes such as electric aircrafts and electric automobiles. The method mainly comprises the following steps: modeling a motor winding temperature of each rotating speed stable section by using an index approaching model, and solving undetermined parameters of the index approaching model; based on a convective heat transfer formula, the heat dissipation coefficient of each rotating speed stable section is calculated through the motor heat loss, the environment temperature and the steady-state temperature; fitting the steady-state temperature, the time constant and the heat dissipation coefficient under different powers by using a polynomial fitting function to obtain the steady-state temperature, the time constant and the heat dissipation coefficient under a given power; and correcting the steady-state temperature of the given power according to the new environment temperature, and predicting the motor winding temperature under the given power through the corrected steady-state temperature of the given power by using an exponential approaching model.

Description

Technical Field

[0001] This application relates to the field of electric drive technology, and in particular to a method and apparatus for predicting the winding temperature of an air-cooled motor. Background Technology

[0002] Electric aircraft and electric vehicles place high demands on the energy density of their electric drive systems, thus limiting their heat dissipation capacity due to both weight and size constraints. Overheating of the motor can severely compromise the safety of the vehicle and passengers. Therefore, understanding the motor's temperature characteristics is crucial for safe operation, necessitating large-scale testing at various power levels, which hinders research and development efficiency. Furthermore, predicting the motor's temperature after a certain power level for a specific duration during actual operation can assist pilots in making safe driving decisions. To meet the aforementioned needs for capturing and predicting motor temperature during both the research and development and operational phases, it is essential to develop accurate and rapid methods for predicting motor temperature rise.

[0003] Current methods for predicting motor temperature rise fall into two categories: physical modeling and statistical and intelligent algorithm analysis of historical data to derive a predictive model. Physical modeling requires detailed geometric parameters and material properties of internal motor components, necessitates corrections, and suffers from low computational accuracy. Statistical and intelligent algorithm-based methods, on the other hand, suffer from overfitting, high computational demands for training, and large data requirements. Summary of the Invention

[0004] This application provides a method and apparatus for predicting the winding temperature of an air-cooled motor, used for predicting the winding temperature of air-cooled motors in scenarios such as electric aircraft and electric vehicles.

[0005] This invention provides a method for predicting the winding temperature of an air-cooled motor, the method comprising: The time series data is divided into multiple stable speed segments based on the motor speed, and the average power of each stable speed segment is calculated; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points; The motor winding temperature for each stable speed segment is modeled using an exponential approach model, and the undetermined parameters of the exponential approach model are solved. The undetermined parameters include steady-state temperature, time constant, and temperature change amplitude. Based on the convective heat transfer formula, the heat dissipation coefficient for each stable speed range is calculated using the motor heat loss, ambient temperature, and the steady-state temperature. The steady-state temperature, time constant, and heat dissipation coefficient under different power levels are fitted using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level. The steady-state temperature of the target power is corrected based on the new ambient temperature, and the motor winding temperature under the target power is predicted by the corrected steady-state temperature using an exponential approach model.

[0006] In one optional embodiment, the step of dividing the time series data according to the motor speed to obtain multiple stable speed segments includes: Calculate the smoothed rotational speed value at each time point in the time series data; Calculate the relative rate of change of adjacent rotational speeds at each time point based on the smoothed rotational speed value; The time series data is divided into multiple stable speed segments by the relative change rate of adjacent speeds. The relative change rate of adjacent speeds in each stable speed segment is less than a set speed fluctuation tolerance threshold, and the duration of each stable speed segment is greater than the minimum duration.

[0007] In an optional embodiment, calculating the smoothed rotational speed value at each time point in the time series data includes: The time series data is divided into multiple time windows using a sliding window method with a step size of 1 and a length of 2H+1. Sort the 2H+1 motor speeds within each time window, and take the motor speed in the middle of the sorted sequence as the smoothed speed value at the corresponding time point.

[0008] In an optional embodiment, the step of modeling the motor winding temperature for each stable speed range using an exponentially approaching model and solving for the undetermined parameters of the exponentially approaching model includes: Calculate the average rate of change of motor winding temperature at a predetermined number of time points within each speed stabilization segment, and determine the exponential approach model based on the average rate of change. The exponential approach model can be a negative exponential growth model or a positive exponential decay model. Nonlinear least squares fitting is used to solve for the undetermined parameters of the exponentially approaching model by iterative optimization to minimize the objective function.

[0009] In an optional embodiment, the step of calculating the heat dissipation coefficient for each stable speed range based on the convective heat transfer formula, using the motor heat loss, ambient temperature, and the steady-state temperature, includes: The heat loss power of each stable speed range is calculated by the average power and motor efficiency of each stable speed range. The heat dissipation coefficient for each stable speed range is calculated based on the heat loss power, steady-state temperature, and ambient temperature during the stable speed range.

[0010] In an optional embodiment, the step of fitting the steady-state temperature, time constant, and heat dissipation coefficient under different power levels using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level includes: The corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function are determined by the number of stable speed segments. The polynomial coefficients corresponding to the steady-state temperature fitting function, the time constant fitting function, and the heat dissipation factor fitting function are solved by the least squares method. The polynomial coefficients and the given power are input into the corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function to obtain the steady-state temperature, time constant, and heat dissipation factor under the given power.

[0011] In an optional embodiment, the step of correcting the steady-state temperature of the target power based on the new ambient temperature, and predicting the motor winding temperature at the target power using an exponentially approaching model based on the corrected steady-state temperature of the target power, includes: Based on the duration of the target power, a correction strategy is determined by selecting the steady-state temperature of the target power, and the steady-state temperature of the target power is corrected according to the correction strategy. The steady-state temperature, initial temperature, time constant, and initial time of the modified target power are input into the exponential approach model to calculate the motor winding temperature under the target power.

[0012] This invention provides a device for predicting the winding temperature of an air-cooled motor, the device comprising: The segmentation module is used to divide the time series data into multiple stable speed segments based on the motor speed, and calculate the average power of each stable speed segment; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points respectively; The calculation module is used to model the motor winding temperature for each stable speed segment using an exponential approach model, and solve for the undetermined parameters of the exponential approach model, including steady-state temperature, time constant, and temperature change amplitude. The calculation module is also used to calculate the heat dissipation coefficient for each stable speed range based on the convective heat transfer formula, using the motor heat loss, ambient temperature and the steady-state temperature. The acquisition module is used to fit the steady-state temperature, time constant, and heat dissipation coefficient under different power using a polynomial fitting function, and to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power. The prediction module is used to correct the steady-state temperature of the target power based on the new ambient temperature, and to predict the motor winding temperature at the target power using an exponential approach model based on the corrected steady-state temperature of the target power.

[0013] A computer program product comprising a computer program that, when executed by a processor, implements the above-described method for predicting the winding temperature of an air-cooled motor.

[0014] This invention provides a method and apparatus for predicting the winding temperature of an air-cooled motor. First, time-series data is divided into multiple stable speed segments based on motor speed, and the average power of each stable speed segment is calculated. The time-series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points. An exponential approach model is used to model the motor winding temperature for each stable speed segment, and the undetermined parameters of the exponential approach model are solved. These undetermined parameters include steady-state temperature, time constant, and temperature change amplitude. Then, based on the convective heat transfer formula, the heat dissipation coefficient for each stable speed segment is calculated using motor heat loss, ambient temperature, and the steady-state temperature. A polynomial fitting function is used to fit the steady-state temperature, time constant, and heat dissipation coefficient under different power levels to obtain the steady-state temperature, time constant, and heat dissipation coefficient for a given power. Finally, the steady-state temperature for the target power is corrected based on the new ambient temperature, and the exponential approach model is used to predict the motor winding temperature at the target power using the corrected steady-state temperature. This application enables rapid and accurate prediction of motor winding temperature for each given power at various ambient temperatures. Attached Figure Description

[0015] Figure 1 A flowchart of a method for predicting the winding temperature of an air-cooled motor provided in this application; Figure 2 An example diagram illustrating the comparison between predicted and measured values ​​provided in this application; Figure 3 A schematic diagram of the structure of a wind-cooled motor winding temperature prediction device provided in this application; Figure 4 A schematic diagram of a computer device provided in this application. Detailed Implementation

[0016] To better understand the above technical solutions, the technical solutions of the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0017] Please see Figure 1 This invention provides a method for predicting the winding temperature of an air-cooled motor, which specifically includes steps S101-S105: Step S101: Divide the time series data into multiple speed stability segments according to the motor speed, and calculate the average power of each speed stability segment.

[0018] To establish an accurate temperature model, the process must be performed under conditions where the motor power is essentially constant. This step automatically segments the continuous data based on speed stability. It ensures that the motor power is essentially constant for each segment of winding temperature and calculates the average power for each stable speed segment. The time-series data includes multiple time points corresponding to the motor winding temperature, motor speed, and motor power. It should be noted that this embodiment uses an anti-aliasing filter to downsample the data to obtain the time-series data, thereby improving data processing efficiency.

[0019] Specifically, during the testing process, signals such as motor winding temperature, motor speed, motor power, current, and voltage are all measured at high frequencies. (For example, 400 Hz) is synchronously acquired to form the raw time series data. In the data preprocessing stage, the raw high-frequency sampled data is downsampled based on an anti-aliasing filter to improve subsequent computational efficiency and avoid spectral aliasing. Let the original sampling frequency be... The target sampling frequency is (Preferably 1Hz), then the Nyquist cutoff frequency is:

[0020] in, Let be the cutoff frequency of the anti-aliasing filter, which, according to the Nyquist sampling theorem, is set to half the target sampling frequency. Design a finite-length unit impulse response (FIR) low-pass filter whose transfer function is expressed as:

[0021] Where M is the filter order (preferably 50). Let k be the coefficient of the filter. This represents the unit delay over k sampling periods. Performing zero-phase bidirectional filtering on the original signal x[n] eliminates the phase distortion introduced by the filtering:

[0022] This represents a discrete-time series, which includes all the original data channels that require downsampling. The original signals are: motor winding temperature T, motor speed ω, and motor power P.

[0023] Finally, in the target time grid Resampling is achieved per second using linear interpolation:

[0024] In this embodiment, linear interpolation is performed on a given target (resampled) time point. Find two adjacent known points on the time axis of the original signal. and (satisfy Then, based on the signal values ​​at these two points... and Calculate Signal estimate at time . This represents the last time point (in seconds) of the original time series in the experimental data, and indicates the total experimental duration after rounding. and These are two consecutive, known discrete time points on the original signal's time axis, used to calculate any target time point. The interpolated signal value.

[0025] In one optional embodiment provided in this application, the step of dividing the time series data according to the motor speed to obtain multiple stable speed segments includes: Step S1011: Calculate the smoothed rotational speed value at each time point in the time series data.

[0026] In this embodiment, calculating the smoothed rotational speed value at each time point in the time series data includes: dividing the time series data into multiple time windows using a sliding window method with a step size of 1 and a length of 2H+1; sorting the 2H+1 motor rotational speeds within each time window; and taking the motor rotational speed in the middle of the sorted sequence as the smoothed rotational speed value at the corresponding time point.

[0027] Specifically, firstly, regarding the motor speed Median filtering is used for smoothing to suppress minor fluctuations:

[0028] in, The half-width of the median filter window corresponds to a window length of 2H+1 sampling points; n represents the nth time point in the sampled data points arranged in chronological order.

[0029] Then, taking the current time n as the center, H sampling points are taken forward and backward to form a local data window of length 2H+1. Next, the median of the data window is calculated by sorting the 2H+1 rotational speed values ​​within the window and taking the middle value of the sorted sequence. This median value is then used as the smoothed rotational speed value at the current time n. Move the window one point (n+1) to the right and repeat the above process to obtain the smoothed result of the entire sequence.

[0030] Step S1012: Calculate the relative rate of change of adjacent rotational speeds at each time point based on the smoothed rotational speed value.

[0031] In this embodiment, the relative rate of change of adjacent rotational speeds is calculated using the following formula:

[0032] Step S1013: Divide the time series data into multiple stable speed segments by the relative change rate of adjacent speeds.

[0033] Wherein, the relative rate of change of adjacent speeds within the speed stabilization segment is less than the set speed fluctuation tolerance threshold, and the duration of the speed stabilization segment is greater than the minimum duration.

[0034] In this embodiment, a speed fluctuation tolerance threshold is set. (Preferably 2%), when A stable state is determined at this point. For each identified stable speed range, its duration is calculated. :

[0035] and The start and end times of each identified k-th stable speed segment are determined. Strain the results to find those that meet the criteria. The interval between these intervals is taken as the final stable speed segment, and is denoted as the k-th stable speed segment. Minimum duration constraint (preferably 20s).

[0036] The final formula for calculating the average power within the k-th speed stabilization segment is as follows:

[0037] in, Let be the average power of the kth speed stabilization segment, where k is used to identify the kth speed stabilization segment identified from the time series data; , These are the start and end indices of the segment, respectively (Note: s represents start, e represents end, and e and s will also appear later). This represents the number of data points within the k-th speed stabilization segment. Let be the motor power at the i-th sampling point.

[0038] Step S102: Model the motor winding temperature for each stable speed range using the exponential approach model, and solve for the undetermined parameters of the exponential approach model.

[0039] In this embodiment, the motor winding temperature for each stable speed range k is... Its thermal dynamics process follows a first-order thermal balance equation, the solution of which is an exponentially approaching form. The temperature of each section of the motor winding is modeled using the exponentially approaching model evolved from the first-order thermal balance equation, and the undetermined parameters of the exponentially approaching model are solved, namely steady-state temperature, time constant, and temperature change amplitude.

[0040] In this embodiment, the process of modeling the motor winding temperature for each stable speed range using an exponentially approaching model and solving for the undetermined parameters of the exponentially approaching model includes: Step S1021: Calculate the average rate of change of motor winding temperature at the preset number of time points within each speed stabilization segment, and determine the exponential approach model based on the average rate of change.

[0041] Specifically, according to the first law of thermodynamics, the temperature change of the motor windings follows a first-order thermal equilibrium equation:

[0042] in: The winding heat capacity is (J / ℃). The temperature is the motor winding temperature (°C). The power loss due to heat loss of the motor (W). Thermal resistance (°C / W). The ambient temperature is (°C).

[0043] By solving this differential equation, an exponentially approaching model of the motor winding temperature change over time is obtained. First, the average rate of change of the motor winding temperature in the initial part of the calculation segment (such as the first 20 points) is calculated. If it is positive, it indicates a heating trend; otherwise, it indicates a cooling trend.

[0044] For the temperature rise phase, a negative exponential growth model is adopted:

[0045] This refers to the starting time point of the k-th segment of stable rotational speed. t is a specific time point in the downsampled time series data from the experiment. It describes the relative time within a segment, that is, the time shifted back from the start time of a certain segment (which must be within a certain stable power segment).

[0046] For the temperature decrease phase, a positive exponential decay model is used:

[0047] To solve for the steady-state temperature, the magnitude of the temperature change, and the time constant, we first assume initial values. The initial value of the steady-state temperature is:

[0048] The initial value of the temperature change amplitude is:

[0049] The initial value of the time constant is:

[0050] Step S1022: Using nonlinear least squares fitting, the objective function is minimized through iterative optimization to solve for the undetermined parameters of the exponentially approaching model.

[0051] Specifically, nonlinear least squares fitting, such as the Levenberg-Marquardt optimization algorithm, is employed to minimize the objective function through iterative optimization (e.g., using built-in Matlab functions). The goal is to use optimization to enable the model to predict the winding temperature value. Approaching the measured winding temperature value Solve for the parameter vector , These are the steady-state temperature, temperature change amplitude, and time constant corresponding to the k-th speed stabilization segment:

[0052] in, For the k-th speed stabilization segment at time... The measured motor winding temperature; For the k-th speed stabilization segment with parameters as At time The model predicts the motor winding temperature. Refers to the time point within each stable speed range (not greater than) ).

[0053] For example, if the downsampled time series data contains 100 data points, divided into 4 segments, with 25 data points in each stable speed segment, then the start time of the third stable speed segment (k=3) is 76 seconds, and the third data point in each segment can be used... express.

[0054] Step S103: Based on the convective heat transfer formula, calculate the heat dissipation coefficient for each stable speed range using the motor heat loss, ambient temperature, and the steady-state temperature.

[0055] Among them, the heat dissipation coefficient is a key parameter used to comprehensively reflect the motor's cooling capacity.

[0056] In this embodiment, the step of calculating the heat dissipation coefficient for each stable speed range based on the convective heat transfer formula, using the motor heat loss, ambient temperature, and the steady-state temperature, includes: Step S1031: Calculate the heat loss power of the corresponding stable speed segment based on the average power and motor efficiency of each stable speed segment.

[0057] Specifically, the formula for calculating heat loss power is:

[0058] in, This represents the heat loss power during the k-th stable speed range. The average power during the k-th speed stabilization period. The motor efficiency can be measured in a certain way or estimated by empirical values.

[0059] Step S1032: Calculate the heat dissipation coefficient for each stable speed range based on the heat loss power, steady-state temperature, and ambient temperature during the stable speed range.

[0060] The heat dissipation coefficient for each stable speed range is calculated based on Newton's law of cooling and the principle of thermal equilibrium. This coefficient characterizes the heat power (W / K) required for a unit temperature rise in the motor windings, comprehensively reflecting the heat dissipation conditions. In air-cooled motors, heat dissipation conditions are determined by the airflow velocity and temperature on the motor surface. For example, for air-cooled motors in electric aircraft, the higher the motor power, the faster the fan or propeller speed, and the greater the airflow velocity on the motor surface, the greater the heat dissipation coefficient.

[0061] Specifically, the heat dissipation coefficient for each stable speed range is calculated using the following formula:

[0062] in, The heat dissipation coefficient during the kth stable speed range The ambient temperature (°C) corresponding to the kth stable speed range.

[0063] Step S104: Use a polynomial fitting function to fit the steady-state temperature, time constant and heat dissipation coefficient under different power to obtain the steady-state temperature, time constant and heat dissipation coefficient under a given power.

[0064] The exponentially approaching model parameters and heat dissipation coefficients under different power levels were fitted using a polynomial fitting method to obtain the original exponentially approaching model parameters and original heat dissipation coefficients for each power range under the new load spectrum.

[0065] In this embodiment, the step of fitting the steady-state temperature, time constant, and heat dissipation coefficient under different power levels using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level includes: Step S1041: Determine the corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function by dividing the number of stable speed segments.

[0066] Specifically, the heat dissipation factor fitting function is:

[0067] in, This is the heat dissipation coefficient corresponding to the motor power P, where P is the motor power (unit: kW). The coefficients are polynomial coefficients, i = 0, 1, ..., 5, obtained by least squares fitting. A 5th-order polynomial is used when there are enough stable speed ranges (K ≥ 3), a linear polynomial is used when there are only 2 points, and no calculation is performed when there is only one point; the fitting function is equal to a constant.

[0068] Specifically, the time constant fitting function as follows:

[0069] in, Let P be the time constant corresponding to the motor power. The coefficients are polynomial coefficients, i = 0, 1, ..., 3, obtained by least squares fitting. A third-order polynomial is used when there are enough stable power segments (K ≥ 3); a linear polynomial is used when there are only two points. No calculation is performed when there is only one point, and the fitting function is equal to a constant.

[0070] Specifically, the fitting function for steady-state temperature as follows:

[0071] in, The steady-state temperature corresponding to the motor power P The polynomial coefficients, i = 0, 1, ..., 3, are obtained through least squares fitting. A third-order polynomial is used when there are a sufficient number of stable power segments (K ≥ 3); a linear polynomial is used when there are only two points. No calculation is performed when there is only one point, and the fitting function is equal to a constant.

[0072] Step S1042: Solve the polynomial coefficients corresponding to the steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function respectively using the least squares method.

[0073] Specifically, in this embodiment, the least squares method can be used to solve for the polynomial coefficients. For example, in the heat dissipation factor fitting function, if K ≥ 3, then... Substitute the contents of the second half of the parentheses in the formula below, and then calculate to obtain the polynomial coefficients. :

[0074] Where n is the order of the polynomial (n=3 in this example), the polynomial coefficients d and e are solved similarly; the argmin operator means to solve the polynomial coefficient vector c that makes the sum of squared fitting errors reach the global minimum by using the least squares method.

[0075] Step S1043: Input the polynomial coefficients and the given power into the corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function to obtain the steady-state temperature, time constant, and heat dissipation factor under the given power.

[0076] In this embodiment, after obtaining the polynomial fitting coefficients using the least squares method, these coefficients are substituted into the corresponding fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient at a given power. Here, the given power refers to a specific power value, which can be a dataset containing multiple set power values. , Represents the l-th given power. No more than all The maximum value is not less than all The minimum value.

[0077] Step S105: Correct the steady-state temperature of the target power based on the new ambient temperature, and use the exponential approach model to predict the motor winding temperature under the target power based on the corrected steady-state temperature of the target power.

[0078] The target power is either a given new motor load spectrum or the power of the currently operating motor.

[0079] In this embodiment, the step of correcting the steady-state temperature of the target power based on the new ambient temperature, and predicting the motor winding temperature at the target power using an exponentially approaching model based on the corrected steady-state temperature of the target power, includes: Step S1051: Select the steady-state temperature of the target power according to the duration of the target power, determine the correction strategy, and correct the steady-state temperature of the target power according to the correction strategy.

[0080] According to the new ambient temperature ( T amb,new,k ) for steady-state temperature ( T ss,original,k (This needs to be corrected.)

[0081] First, the target power is obtained based on the fitting function. The corresponding steady-state temperatures Time constant and heat dissipation coefficient (These three parameters are all obtained by giving power) (Substitute the three fitting functions from step S1041 to obtain). Then, based on the duration of the target power... Select steady-state temperature Correction strategies.

[0082] when hour:

[0083] when hour:

[0084]

[0085] in, Let L be the duration of the l-th operating condition (the target power corresponding to the l-th operating condition). Let l be the time constant under the l-th operating condition. The steady-state temperature is used to correct the obtained target power.

[0086] This represents the current ambient temperature corresponding to the l-th operating condition. Let l be the steady-state temperature under the l-th operating condition. The ambient temperature during the motor test under the l-th operating condition is denoted as . This is the motor heat loss power under the l-th operating condition. Let be the heat dissipation coefficient under the l-th operating condition. For the target power, For motor efficiency.

[0087] Step S1052: Input the steady-state temperature, starting temperature, time constant, and starting time of the corrected target power into the exponential approach model to calculate the motor winding temperature under the target power.

[0088] In this embodiment, an exponential approach model is used to calculate the continuous change in motor winding temperature under the new load spectrum. When the corrected steady-state temperature is greater than the initial operating temperature, a temperature rise model is selected.

[0089] When the corrected steady-state temperature is lower than the operating condition initiation temperature, the temperature descent model is selected:

[0090] For each operating condition, the starting temperature (the final temperature of the previous operating condition) is used. When calculating the first operating condition, this parameter is the starting temperature of the new motor windings. ), This refers to the starting time point under the l-th operating condition.

[0091] This invention provides a method for predicting the winding temperature of an air-cooled motor. First, the time-series data is divided into multiple stable speed segments based on the motor speed, and the average power of each stable speed segment is calculated. The time-series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points. An exponential approach model is used to model the motor winding temperature for each stable speed segment, and the undetermined parameters of the exponential approach model are solved. These undetermined parameters include steady-state temperature, time constant, and temperature change amplitude. Then, based on the convective heat transfer formula, the heat dissipation coefficient for each stable speed segment is calculated using the motor heat loss, ambient temperature, and the steady-state temperature. A polynomial fitting function is used to fit the steady-state temperature, time constant, and heat dissipation coefficient under different power levels to obtain the steady-state temperature, time constant, and heat dissipation coefficient for a given power. Finally, the steady-state temperature for the target power is corrected based on the new ambient temperature, and the exponential approach model is used to predict the motor winding temperature at the target power using the corrected steady-state temperature. This application enables rapid and accurate prediction of the motor winding temperature for each given power at various ambient temperatures.

[0092] Based on experimental data, the technical feasibility of the air-cooled motor winding temperature prediction method provided in this embodiment was verified. In a certain motor load spectrum test, the ambient temperature was 27.2℃, and the initial motor temperature was 35℃. Based on this method, the motor winding temperature in this load spectrum test was predicted. Solid lines represent predicted values, and dashed lines represent measured values. Figure 2 As shown in Table 1, the predicted and measured values ​​agree well. Three key temperature points (1-3) were selected for temperature comparison. The maximum relative deviation between the predicted and measured values ​​at these key temperature points is only 1.3%, which is suitable for engineering applications.

[0093] Table 1 Key temperature points Measured value / ℃ Predicted value / ℃ Relative deviation / % 1 160.5 160.7 0.1 2 105.9 106.7 0.8 3 56 56.7 1.3 This embodiment provides a method for predicting the winding temperature of an air-cooled motor. Based on the first-order differential equation of thermodynamics, a piecewise exponential approach model is constructed to predict the continuous dynamic changes in motor winding temperature, achieving prediction of winding temperature throughout both transient and steady-state processes. Based on the convective heat transfer formula, a heat dissipation coefficient is proposed to characterize the heat dissipation conditions of the air-cooled motor. When predicting the motor's temperature performance under new operating conditions, the heat dissipation coefficient is used to introduce changes in heat dissipation conditions under the new operating conditions, achieving a more realistic and accurate temperature prediction. A temperature prediction database is formed based on experimental data, and polynomial fitting is used to fit the temperature prediction model parameters, enabling prediction of motor winding temperature under any combination of operating conditions and any running time, achieving a prediction deviation of less than 1.3%.

[0094] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0095] In one embodiment, a device for predicting the winding temperature of an air-cooled motor is provided, which corresponds one-to-one with the air-cooled motor winding temperature prediction method described in the above embodiments. For example... Figure 3 As shown, the functional modules of the air-cooled motor winding temperature prediction device are described in detail below: The segmentation module 31 is used to divide the time series data into multiple speed stability segments according to the motor speed, and calculate the average power of each speed stability segment; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points respectively; Calculation module 32 is used to model the motor winding temperature for each stable speed segment using an exponential approach model, and solve for the undetermined parameters of the exponential approach model, including steady-state temperature, time constant, and temperature change amplitude. The calculation module 32 is also used to calculate the heat dissipation coefficient for each stable speed range based on the convective heat transfer formula, using the motor heat loss, ambient temperature and the steady-state temperature. The acquisition module 33 is used to fit the steady-state temperature, time constant and heat dissipation coefficient under different power using a polynomial fitting function, and to obtain the steady-state temperature, time constant and heat dissipation coefficient under a given power. The prediction module 34 is used to correct the steady-state temperature of the target power based on the new ambient temperature, and to predict the motor winding temperature under the target power using an exponential approach model based on the corrected steady-state temperature of the target power.

[0096] In an optional embodiment, the partitioning module 31 is specifically used for: Calculate the smoothed rotational speed value at each time point in the time series data; Calculate the relative rate of change of adjacent rotational speeds at each time point based on the smoothed rotational speed value; The time series data is divided into multiple stable speed segments by the relative change rate of adjacent speeds. The relative change rate of adjacent speeds in each stable speed segment is less than a set speed fluctuation tolerance threshold, and the duration of each stable speed segment is greater than the minimum duration.

[0097] In an optional embodiment, the partitioning module 31 is specifically used for: The time series data is divided into multiple time windows using a sliding window method with a step size of 1 and a length of 2H+1. Sort the 2H+1 motor speeds within each time window, and take the motor speed in the middle of the sorted sequence as the smoothed speed value at the corresponding time point.

[0098] In an optional embodiment, the calculation module 32 is specifically used for: Calculate the average rate of change of motor winding temperature at a predetermined number of time points within each speed stabilization segment, and determine the exponential approach model based on the average rate of change. The exponential approach model can be a negative exponential growth model or a positive exponential decay model. Nonlinear least squares fitting is used to solve for the undetermined parameters of the exponentially approaching model by iterative optimization to minimize the objective function.

[0099] In an optional embodiment, the calculation module 32 is specifically used for: The heat loss power of each stable speed range is calculated by the average power and motor efficiency of each stable speed range. The heat dissipation coefficient for each stable speed range is calculated based on the heat loss power, steady-state temperature, and ambient temperature during the stable speed range.

[0100] In an optional embodiment, the acquisition module 33 is specifically used for: The corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function are determined by the number of stable speed segments. The polynomial coefficients corresponding to the steady-state temperature fitting function, the time constant fitting function, and the heat dissipation factor fitting function are solved by the least squares method. The polynomial coefficients and the given power are input into the corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function to obtain the steady-state temperature, time constant, and heat dissipation factor under the given power.

[0101] In an optional embodiment, the prediction module 34 is specifically used for: Based on the duration of the target power, a correction strategy is determined by selecting the steady-state temperature of the target power, and the steady-state temperature of the target power is corrected according to the correction strategy. The steady-state temperature, initial temperature, time constant, and initial time of the modified target power are input into the exponential approach model to calculate the motor winding temperature under the target power.

[0102] Specific limitations regarding the air-cooled motor winding temperature prediction device can be found in the limitations of the air-cooled motor winding temperature prediction method described above, and will not be repeated here. Each module in the aforementioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the processor in the computer device, or stored in software in the computer device's memory, so that the processor can call and execute the corresponding operations of each module.

[0103] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for predicting the winding temperature of an air-cooled motor.

[0104] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: The time series data is divided into multiple stable speed segments based on the motor speed, and the average power of each stable speed segment is calculated; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points; The motor winding temperature for each stable speed segment is modeled using an exponential approach model, and the undetermined parameters of the exponential approach model are solved. The undetermined parameters include steady-state temperature, time constant, and temperature change amplitude. Based on the convective heat transfer formula, the heat dissipation coefficient for each stable speed range is calculated using the motor heat loss, ambient temperature, and the steady-state temperature. The steady-state temperature, time constant, and heat dissipation coefficient under different power levels are fitted using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level. The steady-state temperature of the target power is corrected based on the new ambient temperature, and the motor winding temperature under the target power is predicted by the corrected steady-state temperature using an exponential approach model.

[0105] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: The time series data is divided into multiple stable speed segments based on the motor speed, and the average power of each stable speed segment is calculated; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points; The motor winding temperature for each stable speed segment is modeled using an exponential approach model, and the undetermined parameters of the exponential approach model are solved. The undetermined parameters include steady-state temperature, time constant, and temperature change amplitude. Based on the convective heat transfer formula, the heat dissipation coefficient for each stable speed range is calculated using the motor heat loss, ambient temperature, and the steady-state temperature. The steady-state temperature, time constant, and heat dissipation coefficient under different power levels are fitted using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level. The steady-state temperature of the target power is corrected based on the new ambient temperature, and the motor winding temperature under the target power is predicted by the corrected steady-state temperature using an exponential approach model.

[0106] In one embodiment, a computer program product is provided, the computer program product comprising a computer program that is executed by a processor to perform the following steps: The time series data is divided into multiple stable speed segments based on the motor speed, and the average power of each stable speed segment is calculated; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points; The motor winding temperature for each stable speed segment is modeled using an exponential approach model, and the undetermined parameters of the exponential approach model are solved. The undetermined parameters include steady-state temperature, time constant, and temperature change amplitude. Based on the convective heat transfer formula, the heat dissipation coefficient for each stable speed range is calculated using the motor heat loss, ambient temperature, and the steady-state temperature. The steady-state temperature, time constant, and heat dissipation coefficient under different power levels are fitted using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level. The steady-state temperature of the target power is corrected based on the new ambient temperature, and the motor winding temperature under the target power is predicted by the corrected steady-state temperature using an exponential approach model.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0109] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for predicting the winding temperature of an air-cooled motor, characterized in that, The method includes: The time series data is divided into multiple stable speed segments based on the motor speed, and the average power of each stable speed segment is calculated; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points respectively; The motor winding temperature for each stable speed range is modeled using an exponential approach model, and the undetermined parameters of the exponential approach model are solved. The undetermined parameters include steady-state temperature, time constant, and temperature change amplitude. Based on the convective heat transfer formula, the heat dissipation coefficient for each stable speed range is calculated using the motor heat loss, ambient temperature, and the steady-state temperature. The steady-state temperature, time constant, and heat dissipation coefficient under different power levels are fitted using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level. The steady-state temperature of the target power is corrected based on the new ambient temperature, and the motor winding temperature under the target power is predicted by the corrected steady-state temperature using an exponential approach model.

2. The method according to claim 1, characterized in that, The process of dividing the time series data according to the motor speed yields multiple stable speed segments, including: Calculate the smoothed rotational speed value at each time point in the time series data; Calculate the relative rate of change of adjacent rotational speeds at each time point based on the smoothed rotational speed value; The time series data is divided into multiple stable speed segments by the relative change rate of adjacent speeds. The relative change rate of adjacent speeds in each stable speed segment is less than a set speed fluctuation tolerance threshold, and the duration of each stable speed segment is greater than the minimum duration.

3. The method according to claim 2, characterized in that, The calculation of the smoothed rotational speed value at each time point in the time series data includes: The time series data is divided into multiple time windows using a sliding window method with a step size of 1 and a length of 2H+1. Sort the 2H+1 motor speeds within each time window, and take the motor speed in the middle of the sorted sequence as the smoothed speed value at the corresponding time point.

4. The method according to any one of claims 1-3, characterized in that, The process of modeling the motor winding temperature for each stable speed range using an exponentially approaching model and solving for the undetermined parameters of the exponentially approaching model includes: Calculate the average rate of change of motor winding temperature at a predetermined number of time points within each speed stabilization segment, and determine the exponential approach model based on the average rate of change. The exponential approach model can be a negative exponential growth model or a positive exponential decay model. Nonlinear least squares fitting is used to solve for the undetermined parameters of the exponentially approaching model by iterative optimization to minimize the objective function.

5. The method according to claim 4, characterized in that, The calculation of the heat dissipation coefficient for each stable speed range based on the convective heat transfer formula, using motor heat loss, ambient temperature, and the steady-state temperature, includes: The heat loss power of each stable speed range is calculated by the average power and motor efficiency of each stable speed range. The heat dissipation coefficient for each stable speed range is calculated based on the heat loss power, steady-state temperature, and ambient temperature during the stable speed range.

6. The method according to claim 5, characterized in that, The step of fitting the steady-state temperature, time constant, and heat dissipation coefficient under different power levels using a polynomial fitting function to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power level includes: The corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function are determined by the number of stable speed segments. The polynomial coefficients corresponding to the steady-state temperature fitting function, the time constant fitting function, and the heat dissipation factor fitting function are solved by the least squares method. The polynomial coefficients and the given power are input into the corresponding steady-state temperature fitting function, time constant fitting function, and heat dissipation factor fitting function to obtain the steady-state temperature, time constant, and heat dissipation factor under the given power.

7. The method according to claim 6, characterized in that, The step of correcting the steady-state temperature of the target power based on the new ambient temperature, and using an exponential approach model to predict the motor winding temperature at the target power using the corrected steady-state temperature, includes: Based on the duration of the target power, a correction strategy is determined by selecting the steady-state temperature of the target power, and the steady-state temperature of the target power is corrected according to the correction strategy. The steady-state temperature, initial temperature, time constant, and initial time of the modified target power are input into the exponential approach model to calculate the motor winding temperature under the target power.

8. A device for predicting the winding temperature of an air-cooled motor, characterized in that, The device includes: The segmentation module is used to divide the time series data into multiple stable speed segments based on the motor speed, and calculate the average power of each stable speed segment; the time series data includes the motor winding temperature, motor speed, and motor power corresponding to multiple time points respectively; The calculation module is used to model the motor winding temperature for each stable speed segment using an exponential approach model, and solve for the undetermined parameters of the exponential approach model, including steady-state temperature, time constant, and temperature change amplitude. The calculation module is also used to calculate the heat dissipation coefficient for each stable speed range based on the convective heat transfer formula, using the motor heat loss, ambient temperature and the steady-state temperature. The acquisition module is used to fit the steady-state temperature, time constant, and heat dissipation coefficient under different power using a polynomial fitting function, and to obtain the steady-state temperature, time constant, and heat dissipation coefficient under a given power. The prediction module is used to correct the steady-state temperature of the target power based on the new ambient temperature, and to predict the motor winding temperature at the target power using an exponential approach model based on the corrected steady-state temperature of the target power.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the wind-cooled motor winding temperature prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the wind-cooled motor winding temperature prediction method as described in any one of claims 1 to 7.

Images