Adaptive thermal management system and method for magnetic levitation motor under high voltage working condition

Abstract

The present invention discloses an adaptive thermal management system and method for high-voltage working conditions of a magnetic levitation motor, which belongs to the field of magnetic levitation motors. The management method includes: obtaining input voltage information and first speed information, determining second speed information based on the voltage information, and determining target speed information based on the fluctuation deviation between the first speed information and the second speed information, and controlling the operation of the magnetic levitation bearing accordingly to improve its stability. The motor also includes an adaptive thermal management module, which is connected to a temperature sensor and is used to collect temperature data in real time and dynamically adjust the heat dissipation intensity and method. The module predicts the temperature change trend based on the thermal power and temperature-heat dissipation correlation model, and starts or adjusts the heat dissipation measures in advance to maintain the temperature within a safe range. The present invention improves the operating stability of the magnetic levitation bearing, realizes real-time monitoring and dynamic management of the motor temperature, effectively controls the temperature rise, enhances safety and reliability, reduces downtime, extends service life, and improves energy efficiency.

Description

Technical Field

[0001] The present invention relates to the field of magnetic levitation motors, and in particular to a high-voltage working condition adaptive thermal management system and method for magnetic levitation motors. Background Art

[0002] Electric motors play a vital role as power sources in modern industrial and high-tech applications. Technological advancements are driving increasing performance requirements for electric motors, particularly in applications requiring high precision and stability, such as magnetic bearings. These bearings utilize magnetic forces to suspend the rotor in mid-air, eliminating the friction and wear associated with traditional bearings and improving mechanical efficiency and lifespan. However, ensuring stable operation of magnetic bearings places higher demands on speed control and thermal management.

[0003] Traditional motor management methods often fail to meet the stability and efficiency requirements of magnetic bearings under various operating conditions. For example, when a motor operates under high load or at high speed, it generates significant heat. Improper heat dissipation can lead to overheating, impacting its performance and lifespan. Furthermore, the complex temperature variations of motors under different operating conditions require precise temperature monitoring and management strategies to ensure safe and stable operation. High-voltage operating conditions are common in practical applications, particularly where motors must operate at high voltages to meet specific process requirements, such as in high-performance industrial equipment, aerospace, and electric vehicles. "High-voltage" refers to operating magnetic bearings with input voltages exceeding the standard operating voltage range. Specifically, high-voltage operating conditions occur when the motor input voltage exceeds a certain percentage of its rated voltage, such as 110% or higher. For example, for a motor rated at 380V, high-voltage operating conditions refer to input voltages of 420V or higher. High-voltage operating conditions present additional challenges, such as increased motor heating, stricter insulation requirements, and higher speed control accuracy.

[0004] Existing thermal management methods typically employ fixed heat dissipation strategies and fail to dynamically adjust based on the motor's real-time operating conditions and temperature fluctuations. This results in energy waste and low heat dissipation efficiency. Patent publication number CN119496338B discloses a magnetic levitation motor with a composite heat dissipation method and proposes a motor design with this composite heat dissipation mechanism. This design includes a heat dissipation assembly consisting of a housing, a rear end cover, a front end cover, a motor shaft, two magnetic bearings, a fluid reservoir, a fluid return box, a rotor, and an S-shaped flow channel. Heat dissipation is achieved by constructing two independent flow paths: the first path consists of a fluid reservoir, a fluid infusion tube, a cooling pipe, a heat sink, a cooling pipe, a fluid infusion tube, and a fluid return box; the second path consists of a second fluid inlet channel, an S-shaped flow channel, a first fluid inlet channel, and a fluid return box. These two paths work together to effectively dissipate heat from the stator, rotor, and motor shaft. As can be seen, existing technologies achieve heat dissipation by combining liquid cooling and air cooling, but these are limited to structural improvements and have little contribution to the control level. Therefore, it is particularly important to develop an adaptive thermal management method that can monitor the motor temperature in real time and dynamically adjust the heat dissipation measures according to the motor working status.

[0005] This paper proposes an adaptive thermal management method and system for magnetic levitation motors operating under high-voltage conditions, aiming to address the aforementioned issues. By monitoring the motor's speed and temperature in real time, this method dynamically adjusts the motor's heat dissipation intensity and method to ensure optimal performance and stability under various operating conditions. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for adaptive thermal management of magnetic levitation motors under high-voltage working conditions. The method dynamically adjusts the heat dissipation intensity and method of the motor by real-time monitoring of the motor's speed and temperature information to ensure that the motor can maintain optimal performance and stability under various working conditions.

[0007] The object of the present invention is achieved through the following technical solutions:

[0008] A method for adaptive thermal management of a magnetic levitation motor under high-voltage operating conditions, wherein the magnetic levitation motor includes a magnetic bearing, a speed sensor, and a temperature sensor. The method includes:

[0009] Acquire input voltage information and obtain first speed information of the magnetic bearing through the speed sensor, and simultaneously obtain initial temperature information of the motor through the temperature sensor;

[0010] determining second speed information of the magnetic bearing according to the voltage information, where the second speed information includes first sub-information and second sub-information;

[0011] determining target speed information according to the first speed information and the fluctuation deviation of the second speed information;

[0012] Controlling the operation of the magnetic bearing according to the target speed information to improve the operating stability of the magnetic bearing;

[0013] The magnetic levitation motor further includes an adaptive thermal management module, which is connected to the temperature sensor. The temperature sensor is used to collect temperature data of the motor in real time and input the data into the adaptive thermal management module. The adaptive thermal management module dynamically adjusts the heat dissipation intensity and heat dissipation mode of the motor according to a preset temperature rise control strategy.

[0014] During the operation of the motor, the adaptive thermal management module calculates the thermal power of the motor in real time according to the speed, load and temperature changes of the motor;

[0015] According to the thermal power and temperature-heat dissipation correlation model, the temperature change trend of the motor is predicted, and heat dissipation measures are started or adjusted in advance to keep the motor temperature within a preset safe temperature range.

[0016] A method for adaptive thermal management of magnetic levitation motors under high-voltage operating conditions begins with data acquisition. The system acquires input voltage information, obtains the current speed of the magnetic bearing (first speed information) through a speed sensor, and collects the initial temperature of the motor through a temperature sensor. This information provides the basis for subsequent control and management. Next, based on the input voltage information, the system determines the target speed of the magnetic bearing (second speed information), which consists of first and second sub-information. Combining the fluctuation deviation of the first and second speed information, the system calculates the final target speed to ensure stable operation of the magnetic bearing.

[0017] The magnetic levitation motor is also equipped with an advanced adaptive thermal management module. This module, closely connected to the temperature sensor, accurately collects motor temperature data in real time. Based on this data, the module dynamically adjusts the motor's heat dissipation intensity and method according to a preset temperature rise control strategy to adapt to different operating conditions. During operation, the module also calculates the motor's thermal power in real time based on changes in speed, load, and temperature. This step is crucial for predicting motor temperature trends.

[0018] Finally, based on thermal power and temperature-heat dissipation correlation models, the system predicts the motor's temperature trends and proactively initiates or adjusts cooling measures accordingly. This maintains the motor's temperature within a preset safe range, ensuring stable operation and efficient performance under high-voltage conditions. Through this series of precise control and management steps, the adaptive thermal management method for magnetic levitation motors under high-voltage conditions not only improves the motor's operational stability but also effectively extends its service life, significantly enhancing industrial production and energy utilization.

[0019] As a preferred method, the first sub-information is a speed estimation value calculated based on a voltage-speed estimation model, and the second sub-information is a speed correction value calculated based on a motor parameter identification algorithm combined with the voltage information. The motor parameter identification algorithm uses the real-time operating data of the motor under high-voltage conditions to update the motor parameters to improve the speed calculation accuracy. Specifically, the motor's voltage, current, and speed are used through the least squares method or recursive algorithm to update the motor's winding resistance, stator inductance, and rotor time constant in real time.

[0020] As a preferred method, the target speed information is determined based on the fluctuation deviation of the first speed information and the second speed information, specifically including: calculating the difference between the first speed information and the second speed information and the fluctuation amplitude thereof, and when the fluctuation amplitude exceeds a preset fluctuation threshold, triggering the speed information fusion mechanism, performing weighted fusion on the first speed information and the second speed information according to a preset weight factor, and obtaining the fused speed information as the target speed information, and the weight factor is dynamically adjusted according to the load characteristics of the motor, specifically: when the motor is lightly loaded, increasing the weight of the first speed information; when the motor is heavily loaded, increasing the weight of the second speed information.

[0021] As a preferred method, the temperature rise control strategy includes a composite control mode that combines feedforward control based on the motor operating status and closed-loop control based on real-time temperature feedback. Specifically, the heating condition of the motor is predicted according to its speed and load, and the heat dissipation intensity is adjusted in advance as feedforward control; at the same time, the motor temperature is monitored in real time, and the heat dissipation intensity is adjusted according to the temperature deviation feedback as closed-loop control.

[0022] As a preferred embodiment, the calculation formula of the thermal power is:

[0023] ,in, represents thermal power, represents the motor current, represents the winding resistance, represents the magnetic field frequency, represents the magnetic flux density, represents the hysteresis loss coefficient, represents the eddy current loss coefficient, Indicates the mass of the stator core.

[0024] As a preferred embodiment, the temperature-heat dissipation correlation model is established based on the thermophysical parameters of the motor material and the thermal resistance and heat capacity of the heat dissipation path, and is used to characterize the quantitative relationship between the motor temperature and the required heat dissipation capacity. The specific form is:

[0025] ,in, Indicates the heat dissipation requirements, Indicates the motor temperature, Indicates the ambient temperature, Indicates thermal resistance.

[0026] As a preferred embodiment, during the operation of the motor, the operation status of the motor is also monitored in real time and fault diagnosis is performed. When a fault is detected, protective measures are taken in time to prevent the fault from expanding.

[0027] A high-voltage operating condition adaptive thermal management system for a magnetic levitation motor, the magnetic levitation motor comprising a magnetic bearing, a speed sensor, and a temperature sensor, the management system comprising:

[0028] an acquisition module, configured to acquire input voltage information and first speed information of the magnetic bearing through the speed sensor, and acquire initial temperature information of the motor through the temperature sensor;

[0029] a first determining module, configured to determine second speed information of the magnetic bearing according to the voltage information, where the second speed information includes first sub-information and second sub-information;

[0030] a second determining module, configured to determine target speed information according to the first speed information and the fluctuation deviation of the second speed information;

[0031] a control module, configured to control the operation of the magnetic bearing according to the target speed information, so as to improve the operation stability of the magnetic bearing;

[0032] An adaptive thermal management module, connected to the temperature sensor, for collecting the temperature data of the motor in real time and dynamically adjusting the heat dissipation intensity and heat dissipation mode of the motor according to a preset temperature rise control strategy;

[0033] Thermal power calculation unit, used to calculate the thermal power of the motor in real time according to the motor speed, load and temperature changes during the motor operation;

[0034] The temperature prediction unit is used to predict the temperature change trend of the motor according to the thermal power and temperature-heat dissipation correlation model, and start or adjust the heat dissipation measures in advance to keep the motor temperature within a preset safe temperature range.

[0035] As a preferred embodiment, a weight factor adjustment module is further included, which is used to dynamically adjust the weight factor according to the load characteristics of the motor. When the motor is lightly loaded, the weight of the first speed information is increased; when the motor is heavily loaded, the weight of the second speed information is increased.

[0036] As a preferred embodiment, the system also includes a fault diagnosis module for real-time monitoring of the motor's operating status and fault diagnosis. When a fault is detected, protective measures are taken in a timely manner, such as reducing the motor speed, reducing the load or shutting down the motor, to prevent the fault from expanding.

[0037] The present invention has at least the following beneficial effects:

[0038] By monitoring and adjusting speed information, the present invention improves the operational stability of the magnetic bearing and ensures that the motor maintains optimal performance under different operating conditions. At the same time, it enables real-time monitoring and dynamic management of the motor temperature. The adaptive thermal management module dynamically adjusts the heat dissipation intensity and method based on the motor's real-time operating status and temperature changes, effectively controlling the motor temperature and preventing overheating. These measures enhance the safety and reliability of the motor, reduce failures and downtime caused by overheating, and extend the motor's service life. Furthermore, by precisely controlling heat dissipation measures, unnecessary energy waste is avoided, improving the motor's overall energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To reveal the technical details of the embodiments of the present invention, the following is a brief introduction to the drawings involved in the embodiments. It should be emphasized that these drawings only illustrate several embodiments of the present invention and should not be considered as defining the scope of the invention. Those skilled in the art can deduce other relevant drawings based on these drawings without engaging in creative work.

[0040] Figure 1 Schematic diagram of the process of determining target speed information in an embodiment;

[0041] Figure 2 Schematic diagram of the thermal balance determination process of the adaptive thermal management of the magnetic levitation motor under high-voltage conditions in an embodiment. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0043] In the following, embodiments of the present disclosure are described in detail with the aid of accompanying drawings. However, please be aware that the present disclosure is not limited to the specific forms shown herein. Rather, it should be understood to encompass various variations, equivalents, and / or alternatives to the embodiments of the present disclosure. In describing the drawings, the same reference numerals will be used to indicate similar components.

[0044] In the various embodiments of the present disclosure, expressions such as "first," "second," "the first," or "the second" are intended to modify different components, rather than to indicate order and / or importance, and do not limit the corresponding components. For example, a first user device and a second user device each represent different user devices, although they both fall within the scope of user devices. Similarly, a first component can be named a second component, and a second component can be named a first component, which does not change their essential attributes within the scope of the present disclosure.

[0045] In this disclosure, terms are used to illustrate specific embodiments and do not constitute limitations of this disclosure. In this context, the use of the singular also encompasses the plural, unless the text clearly indicates otherwise. In the process of explanation, it should be understood that terms such as "including" or "having" are intended to indicate the presence of a feature, quantity, step, operation, structural component, part, or combination thereof, and do not preclude the possibility or addition of one or more other features, quantities, steps, operations, structural components, parts, or combinations thereof.

[0046] It should be understood that while the following description provides extensive specific details intended to facilitate a comprehensive understanding of the example embodiments, those skilled in the art will appreciate that the example embodiments can be implemented without these specific details. For example, systems may be presented in block diagram form to avoid excessive detail that would obscure the clarity of the examples. In other cases, unnecessary details regarding well-known processes, structures, and techniques may be omitted to maintain clarity of the examples.

[0047] A method for adaptive thermal management of a magnetic levitation motor under high-voltage operating conditions, wherein the magnetic levitation motor includes a magnetic bearing, a speed sensor, and a temperature sensor. The method includes:

[0048] Acquire input voltage information and obtain first speed information of the magnetic bearing through the speed sensor, and simultaneously obtain initial temperature information of the motor through the temperature sensor;

[0049] determining second speed information of the magnetic bearing according to the voltage information, where the second speed information includes first sub-information and second sub-information;

[0050] Determine the target speed information based on the fluctuation deviation of the first speed information and the second speed information (see Figure 1 );

[0051] Controlling the operation of the magnetic bearing according to the target speed information to improve the operating stability of the magnetic bearing;

[0052] The magnetic levitation motor further includes an adaptive thermal management module, which is connected to the temperature sensor. The temperature sensor is used to collect temperature data of the motor in real time and input the data into the adaptive thermal management module. The adaptive thermal management module dynamically adjusts the heat dissipation intensity and heat dissipation mode of the motor according to a preset temperature rise control strategy.

[0053] During the operation of the motor, the adaptive thermal management module calculates the thermal power of the motor in real time according to the speed, load and temperature changes of the motor;

[0054] According to the thermal power and temperature-heat dissipation correlation model, the temperature change trend of the motor is predicted, and heat dissipation measures are started or adjusted in advance to keep the motor temperature within a preset safe temperature range.

[0055] In a preferred embodiment, the first sub-information is a speed estimate calculated based on a voltage-speed prediction model, and the second sub-information is a speed correction value calculated based on the voltage information using a motor parameter identification algorithm. The voltage-speed prediction model is a mapping relationship established based on the motor's basic electrical characteristics and a large amount of experimental data. When the current voltage information is input, the model can output a preliminary speed estimate, namely the first sub-information. This is a "lookup table" established using the motor's speed performance at different voltages. By searching this "lookup table," the corresponding speed estimate can be quickly obtained. The second sub-information is calculated in real time using a motor parameter identification algorithm, combining real-time collected voltage information and other motor operating parameters (such as current and temperature). This algorithm can dynamically adjust and correct key motor parameters (such as winding resistance, stator inductance, rotor time constant, etc.) based on the actual operating conditions of the motor under high-voltage conditions, thereby more accurately calculating the current speed correction value, namely the second sub-information.

[0056] After obtaining the first and second sub-information, the two sub-information are fused according to a preset fusion algorithm. This fusion algorithm comprehensively considers factors such as the credibility and accuracy of the two information and the current operating status of the motor, performing a weighted calculation on them to ultimately obtain a more accurate and reliable second speed information. During the motor startup phase, the credibility of the first sub-information may be relatively low, in which case the second sub-information will be given a greater weight; while during the stable operation phase of the motor, the credibility of the first sub-information is higher, and the weight of the first sub-information will be appropriately increased. In this way, it can be ensured that the second speed information can truly and accurately reflect the actual speed of the motor.

[0057] In one embodiment, at a certain operating moment of the magnetic levitation motor, the first sub-information (speed estimate) obtained by the voltage-speed estimation model is 1000 rpm, and the second sub-information (speed correction) calculated by the motor parameter identification algorithm combined with the voltage information is 900 rpm. A simple linear weighted fusion algorithm is used to fuse these two pieces of information, with the weight coefficients being and , and satisfies The weight coefficient is determined based on the following: During the motor startup phase, since the motor parameters are not stable yet, the credibility of the first sub-information is low, so a greater weight is given to the second sub-information, for example , ;In the stable operation stage of the motor, the credibility of the first sub-information is higher, and the first sub-information is given a greater weight, such as , If the current motor is in a stable operation stage, the weight coefficient is , , then the fused second speed information is calculated as follows:

[0058] .

[0059] The motor parameter identification algorithm uses the real-time operating data of the motor under high-voltage conditions to update the motor parameters to improve the speed calculation accuracy. Specifically, the motor voltage, current, and speed are used to update the motor winding resistance, stator inductance, and rotor time constant parameters in real time through the least squares method or recursive algorithm.

[0060] Under high-voltage conditions, accurate speed calculation is crucial for stable operation and precise control of magnetic levitation motors. Motor parameters vary with operating conditions, making traditional fixed-parameter models incapable of accurately reflecting actual conditions. Therefore, a motor parameter identification algorithm is employed to update motor parameters using real-time operating data, improving speed calculation accuracy.

[0061] The mathematical model of the motor is the basis for parameter identification. The form given in this embodiment is as follows:

[0062] ,in: , is the motor at time of Axis and The shaft voltage V, , The motor is in time of Axis and Shaft current A, is the motor winding resistance Ω, is the motor stator inductance H, is the motor back EMF constant V·s / rad, is the motor at time Angular velocity rad / s, and speed The relationship is , is the motor torque constant N·m / A, is the motor rotor inertia kg·m 2 , is the motor viscous friction coefficient N·m·s / rad, is the time s.

[0063] The above model integrates the electrical characteristics (such as voltage, current, resistance, inductance) and mechanical characteristics (such as torque, moment of inertia, and friction coefficient) of the motor, and can fully describe the dynamic behavior of the motor.

[0064] The recursive least squares method is used to update the motor parameters in real time. Its basic principle is to estimate the model parameters by minimizing the prediction error.

[0065] ①Define parameter vector: combine the motor parameters to be identified into a vector ,For example:

[0066]

[0067] ②Construct measurement matrix and measurement vector: Construct measurement matrix according to the motor mathematical model and the measurement vector , used to associate the motor’s input and output data with the parameter vector.

[0068] ③ Recursive update formula: Use recursive formula, such as gain matrix , covariance matrix The update formula adjusts the parameter estimates in real time. The gain matrix determines the impact of new data on the parameter estimates, and the covariance matrix reflects the uncertainty of the parameter estimates.

[0069]

[0070] in, is the identity matrix, Forgetting Factor , used to give new data a higher weight.

[0071] ④ Parameter estimation update: Combine the new measurement data and gain matrix to correct the parameter estimation value:

[0072] Make the parameter estimates gradually approach the true values.

[0073] The basic implementation process of this embodiment is as follows:

[0074] Implementation Process

[0075] 1. Real-time Operation Data Acquisition: During the operation of the magnetic levitation motor, high-precision voltage, current, and speed sensors are used to collect the motor's three-phase voltage, three-phase current, and speed signals in real time. These sensors operate at a high sampling frequency (e.g., 10kHz) to ensure that the motor's performance is captured under different operating conditions. The collected analog signals are then preprocessed, including filtering, noise removal, and digital conversion, to improve data quality.

[0076] 2. Data preprocessing: Filter the collected signals using digital low-pass filters or median filters to remove high-frequency noise and spike noise, improving data accuracy. Simultaneously, the analog signals are converted to digital signals for subsequent digital signal processing and calculations.

[0077] 3. Initialize parameters and matrices: Set the initial estimated values ​​of the motor parameters and initialize the covariance matrix , usually a larger value is taken to indicate the uncertainty of the initial estimate and to determine the forgetting factor , which is usually close to 1 and is used to balance the weights of new and old data.

[0078] 4. Parameter identification iteration: As the motor runs, the collected data at each time point is processed in turn. According to the above recursive least squares formula, the gain matrix is ​​calculated , update the parameter estimates and covariance matrix This process is repeated continuously, allowing the parameter estimates to gradually approach the actual values. During motor operation, new data is continuously used for iterative calculations, dynamically adjusting the parameter estimates to adapt to the dynamic changes of the motor under high-voltage conditions.

[0079] 5. Result Verification and Correction: Apply the updated parameters to the speed calculation model and compare the calculated speed with the actual measured speed. If the error is still within the allowable range, retain the updated parameters. If the error is outside the allowable range, correct and optimize the model or algorithm, such as adjusting the model structure, improving the filtering method, or changing the algorithm's initial conditions, to improve the accuracy and reliability of parameter identification.

[0080] Through the above process, the motor parameter identification algorithm can make full use of the real-time operating data of the motor under high-voltage conditions, update the key parameters of the motor in real time, improve the accuracy of speed calculation, and ensure stable operation and precise control of the motor.

[0081] In a preferred embodiment, the target speed information is determined based on the fluctuation deviation of the first speed information and the second speed information, specifically including: calculating the difference between the first speed information and the second speed information and their fluctuation amplitude; when the fluctuation amplitude exceeds a preset fluctuation threshold, triggering the speed information fusion mechanism, performing weighted fusion on the first speed information and the second speed information according to a preset weight factor, and obtaining the fused speed information as the target speed information; the weight factor is dynamically adjusted according to the load characteristics of the motor, specifically: when the motor is lightly loaded, increasing the weight of the first speed information; when the motor is heavily loaded, increasing the weight of the second speed information.

[0082] In the magnetic levitation motor management system, in order to ensure stable operation of the motor, it is necessary to integrate the first speed information (directly measured) and the second speed information (calculated from the voltage information) to obtain accurate and reliable target speed information.

[0083] The accuracy of motor speed information can be affected by load variations during operation (e.g., light loads transporting small batches of materials, heavy loads transporting large batches of equipment). First speed information is more accurate under light loads but fluctuates significantly under heavy loads. Second speed information is more accurate under heavy loads but is more susceptible to deviation under light loads due to voltage fluctuations.

[0084] Speed ​​information fusion process: In the process of magnetic levitation motor speed information fusion, in order to obtain more accurate and reliable target speed information, a dynamic weight adjustment mechanism based on the motor load characteristics is adopted to perform weighted fusion of the first speed information and the second speed information.

[0085] Target speed information ( ) is calculated using the following formula:

[0086] ,in: Indicates the first speed information, unit: rpm, Indicates the second speed information, unit: rpm, and are weight factors of the first speed information and the second speed information, and satisfy .

[0087] Under light load conditions, since the first speed information is relatively stable and accurate, it is given a larger weight factor ( ), and the weight factor of the second speed information is reduced accordingly When the first speed information is 1000 rpm and the second speed information is 950 rpm, the target speed information is calculated as:

[0088]

[0089] On the contrary, in the case of heavy load, the reliability of the second speed information is higher, so its weight factor is increased ( ), while reducing the weight factor of the first speed information When the first speed information is 1100 rpm and the second speed information is 1050 rpm, the target speed information is calculated as:

[0090]

[0091] Through this weighted fusion method of dynamically adjusting the weight factors, the accuracy and reliability of the target speed information can be effectively improved, ensuring the stable operation and precise control of the motor under different load conditions.

[0092] In a preferred embodiment, the temperature rise control strategy includes a composite control mode that combines feedforward control based on the motor operating status and closed-loop control based on real-time temperature feedback. Specifically, the heating condition of the motor is predicted according to its speed and load, and the heat dissipation intensity is adjusted in advance as feedforward control; at the same time, the motor temperature is monitored in real time, and the heat dissipation intensity is adjusted according to the temperature deviation feedback as closed-loop control.

[0093] The following is the specific process of the temperature rise control strategy:

[0094] Feedforward control:

[0095] 1. Data collection and analysis: Real-time data collection of motor speed and load information is used to analyze the motor's operating conditions and predict motor heating. For example, motor heating increases under high speed and high load conditions.

[0096] 2. Heat prediction: Based on the historical data or empirical formula of speed, load and motor heat, a heat prediction model is established. The formula used is: Heat = a × speed² + b × load + c, where a, b, and c are coefficients determined based on experiments or historical data, and the unit of a is , the unit of b is , c is in watts ( ).

[0097] 3. Adjust cooling intensity in advance: Based on the heat prediction results, increase the cooling intensity in advance before the heat increases. For example, if it is predicted that the motor will enter a high-load condition, increase the cooling fan speed or increase the coolant flow in advance.

[0098] Closed-loop control: 1. Real-time temperature monitoring: During the entire motor operation process, the temperature of the key parts of the motor (such as stator windings, permanent magnets, etc.) is monitored in real time through temperature sensors installed inside or on the surface of the motor (such as thermocouples, infrared thermometers, etc.).

[0099] 2. Temperature deviation calculation: Compare the real-time monitored temperature with the set temperature target value and calculate the temperature deviation, that is, temperature deviation = actual temperature - target temperature.

[0100] 3. Feedback-based cooling intensity adjustment: Dynamically adjust the cooling intensity based on the magnitude and direction of temperature deviation. For example, if the actual temperature is higher than the target temperature, increase the cooling intensity; otherwise, decrease it. Simple proportional control or PID control algorithms can be used to ensure precise temperature regulation. Using the deviation between the setpoint and actual temperature as input, the cooling intensity gain is adjusted to stabilize the actual temperature near the setpoint.

[0101] Compound control mode:

[0102] 1. Combined feedforward and closed-loop control: Feedforward and closed-loop control operate simultaneously, complementing each other and working together to improve the cooling system. Feedforward control makes advance adjustments based on predictions, while closed-loop control makes fine adjustments based on actual temperature feedback, achieving more accurate and timely cooling control.

[0103] 2. Control coordination and optimization: During implementation, feedforward control and closed-loop control are coordinated, and control parameters are optimized to avoid interference or conflict between them. For example, the weights of feedforward and closed-loop control in heat dissipation intensity adjustment are appropriately allocated to ensure system stability and responsiveness. At a certain moment, based on the motor speed and load, the feedforward control predicts a heat dissipation intensity of 50%, while the closed-loop control calculates a heat dissipation intensity adjustment of 70% based on current temperature feedback. , meaning the cooling intensity is now 64%. At this point, the 30% (0.3) weight of the feedforward control applies: Feedforward control pre-adjusts the cooling intensity based on the speed and load, providing a baseline cooling capability that allows the cooling system to proactively respond to changes in motor heat generation and reduce lag. The 70% weight of the closed-loop control applies: Closed-loop control fine-tunes based on real-time temperature feedback to ensure the actual temperature remains stable near the set point. Due to the higher weight of closed-loop control, the system is more sensitive to temperature fluctuations and can adjust the cooling intensity promptly to maintain temperature stability.

[0104] 3. Real-time update and adjustment: Based on changes in the motor's operating status and real-time temperature feedback, the parameters of the heating prediction model and PID controller are dynamically updated to adapt to different operating conditions and environmental conditions, ensuring the continued effectiveness of the temperature rise control strategy.

[0105] In a preferred embodiment, the calculation formula of the thermal power is:

[0106] ,in, Indicates thermal power W, Indicates the motor current A, represents the winding resistance Ω, Indicates the magnetic field frequency Hz, s -1 , represents the magnetic flux density T, Represents the hysteresis loss coefficient , which is Watt-second-Tesla⁻ᵝ-kilogram⁻¹, represents the eddy current loss coefficient , Indicates the mass of the stator core in kg, Represents the hysteresis loss index (usually 1.5-2.5).

[0107] In a preferred embodiment, the temperature-heat dissipation correlation model is established based on the thermophysical properties of the motor material and the thermal resistance and heat capacity of the heat dissipation path, and is used to characterize the quantitative relationship between the motor temperature and the required heat dissipation capacity. The specific form is:

[0108] ,in, Indicates the heat dissipation requirements, Indicates the motor temperature, Indicates the ambient temperature, Represents thermal resistance, which can be measured experimentally.

[0109] To maintain the motor temperature at 80°C, the motor requires 110 watts of heat dissipation at an ambient temperature of 25°C. This means that the cooling system must be able to provide at least 110 watts of heat dissipation to prevent the motor from overheating.

[0110] ,in, is the real-time cooling requirement. Under steady-state conditions:

[0111] ,in, is the heat dissipation capacity required to maintain the target temperature (such as 80°C), is the target temperature.

[0112] Thermal equilibrium conditions:

[0113] ,when When the motor temperature is stable; when The motor temperature is stabilized at the set target value (such as 80℃). The ultimate goal: to adjust the heat dissipation capacity to , achieving temperature stability.

[0114] This embodiment obtains real-time heat dissipation requirements through temperature sensors , calculated by operating parameters (current, load, etc.) (heat generated when the motor is running), determine the thermal balance (see Figure 2 ).like , heat accumulates, the temperature will rise, and heat dissipation needs to be enhanced. , the heat dissipates too quickly, the temperature will drop, and the heat dissipation can be reduced. Finally, by adjusting the heat dissipation capacity (such as fan speed, coolant flow), near , and matches ( ). It should be noted that It is the steady-state target value set by the system, which is used to guide the adjustment direction of the heat dissipation capacity. , the current heat dissipation capacity is insufficient and needs to be enhanced; when , the current heat dissipation capacity is too strong and the heat dissipation can be reduced. It is the source of heat generation, reflects the heat that the motor may generate currently and in the future, and is used to predict risks. It is the front-end heat dissipation requirement, which is determined by the real-time temperature and is the heat dissipation capacity that the system needs to dynamically match. It is a steady-state target that guides the long-term adjustment direction of heat dissipation capacity to ensure that the temperature is stable within a safe range.

[0115] In one embodiment, the thermal power formula is used in conjunction with a temperature-heat dissipation correlation model. Unable to sense the current temperature status, It is impossible to predict future heat changes. It cannot provide guidance on the specific heat dissipation capacity, may cause overheating due to lag, and may over-enhance heat dissipation; The failure to correlate the source of heat generation may lead to fluctuations due to over-reliance on the current state, or may lead to excessive or insufficient cooling due to lag. Therefore, combining the two, dynamic adjustment on demand can reduce energy consumption. For example, if the load Jump from 100W to 150W (heat will increase suddenly), Still at 110W (temperature has not changed or has no time to change), Enhance heat dissipation in advance (such as increasing the fan speed), and then the motor will experience a process of temperature rising first and then falling. Towards The motor temperature changes until equilibrium is restored. Compared to traditional methods, the motor temperature does not rise to the theoretical temperature that can be reached due to a sudden load increase. Instead, cooling or heat dissipation measures are implemented during the motor heating process, causing the motor temperature to drop rapidly and reach equilibrium. It should be noted that the temperature-heat dissipation correlation model can be used to infer temperature changes caused by load changes, etc. ,in, is the motor temperature, is the ambient temperature, is the thermal resistance.

[0116] The combination of the thermal power formula and the temperature-heat dissipation correlation model upgrades the thermal management system from "passive response" to "active prevention", ensuring real-time heat dissipation needs while achieving forward-looking control by predicting heat changes, ultimately achieving the optimal balance between safety, stability and energy efficiency.

[0117] In a preferred embodiment, self-learning and self-optimization are also included, and self-learning and self-optimization include:

[0118] Collect the motor's operating data under different working conditions, including speed, load, temperature, etc.;

[0119] Based on the collected data, a machine learning algorithm is used to train and update the temperature-heat dissipation correlation model and the relevant parameters in the thermal power calculation formula;

[0120] The updated model is regularly verified and optimized to ensure its accuracy and adaptability.

[0121] Magnetic levitation amorphous motors offer advantages such as high power density and transmission efficiency, but they also face challenges such as heat dissipation difficulties and significant temperature rise. To better predict and control motor temperature rise and improve operational efficiency and reliability, a self-learning and self-optimization mechanism was introduced. By collecting motor operating data and using machine learning algorithms, the temperature-heat dissipation correlation model and relevant parameters in the thermal power calculation formula are updated and optimized.

[0122] Install various sensors at key locations on the motor, including speed sensors, load sensors, and temperature sensors. For example, place temperature sensors at the motor's rotor, stator, and windings (the highest temperature across all sensors can be used for temperature calculations) to monitor temperature changes in these locations in real time. Install speed sensors and torque sensors at the motor's input and output terminals to measure the motor's actual speed and load. Build a high-speed, stable data acquisition system to transmit sensor data to a data processing center via wired or wireless communication.

[0123] Select appropriate machine learning algorithms, such as linear regression, support vector machines, and neural networks, to train and update the relevant parameters in the temperature-heat dissipation correlation model and the thermal power calculation formula. Taking neural networks as an example, construct a neural network model consisting of an input layer, hidden layers, and output layers. The input layer receives preprocessed data such as speed, load, and temperature, and the output layer outputs the updated model parameters.

[0124] The model is trained using training data, and the network weights and bias parameters are adjusted to ensure that the model output is as close as possible to the actual temperature-heat dissipation relationship and thermal power values. During the training process, cross-validation and other methods are used to evaluate and optimize the model's performance to avoid overfitting or underfitting.

[0125] Based on the training results, the relevant parameters in the temperature-heat dissipation correlation model and the thermal power calculation formula are updated. For example, the coefficients, thermal resistance, and other parameters in the thermal power calculation formula are updated to enable the model to more accurately reflect the heat dissipation characteristics and thermal power changes of the motor under different operating conditions.

[0126] Further optimize the updated model by adjusting its structure and parameters to improve its prediction accuracy and generalization ability. For example, increase the number of hidden nodes or layers in the neural network and use regularization and other techniques to constrain the model to improve its performance.

[0127] Establish a reasonable verification cycle and regularly verify the updated model. The verification cycle can be determined based on the motor's operating conditions and data collection, such as after a certain number of hours of operation or after a certain amount of new data has been collected. During the verification process, the model is tested using newly collected operating data, and the model's predicted results for temperature, thermal power, and other factors are compared and analyzed with the actual measured values. Based on the verification results, the model is further optimized and adjusted. If the model's prediction error is large or its adaptability is poor, recollect data, retrain the model, and update its parameters. If the error is within an acceptable range but there is still room for improvement, fine-tune the model's structure or parameters.

[0128] Through self-learning and self-optimization mechanisms, the model continuously updates and adjusts parameters based on the motor's actual operating data, enabling more accurate predictions of the motor's temperature rise under different operating conditions. For example, if the load suddenly increases during motor operation, the model can promptly adjust parameters based on the new data, accurately predicting the motor's temperature rise trend and final stable temperature, providing a more reliable basis for motor cooling design and operational control.

[0129] The updated temperature-heat dissipation correlation model enables more rational design of the motor's cooling system. For example, based on the model's predicted cooling requirements, the number of heat sinks can be increased or the cooling channel structure can be optimized in key motor locations to improve cooling efficiency. Furthermore, based on the model's predictions of cooling requirements under different operating conditions, more flexible cooling control strategies can be designed, such as reducing cooling fan speed to save energy under low loads and increasing speed to enhance cooling under high loads. Accurate temperature rise predictions and optimized cooling designs help maintain the motor within its optimal operating temperature range, reducing the risk of performance degradation and failure caused by excessively high or low temperatures.

[0130] In a preferred embodiment, before determining the target speed information, the method further includes: filtering the first speed information and the second speed information to remove noise interference and improve the accuracy of the speed information.

[0131] Before determining the target speed information, a key preprocessing step is to filter the first speed information and the second speed information. The main purpose of filtering is to remove noise interference in the speed information, because the original speed data is often affected by various factors, such as sensor accuracy limitations and environmental interference, which can introduce noise. This noise may cause the speed information to fluctuate and be distorted, affecting the subsequent accurate judgment of the target speed. By applying a suitable filtering algorithm, the speed data curve can be smoothed, making the speed information more stable and reliable, thereby laying a solid foundation for accurately determining the target speed information, effectively improving the accuracy of the entire speed control or monitoring system in grasping the speed information, and ensuring that the system can make reasonable decisions and responses based on accurate speed data.

[0132] When filtering the first speed information and the second speed information, it is necessary to select an appropriate filtering method based on the actual situation. Common filtering methods include low-pass filtering, Gaussian filtering, Kalman filtering, etc. Each method has its own characteristics and applicable scenarios. For example, low-pass filtering can effectively remove high-frequency noise and is suitable for scenarios where the speed changes relatively slowly; Kalman filtering performs well when processing speed signals with dynamic characteristics. By rationally selecting and applying filtering technology, the useful components in the speed information can be retained to the maximum extent, while eliminating noise interference, so that the quality of the first speed information and the second speed information can be significantly improved, thereby providing a strong guarantee for the accuracy of the subsequent determination of the target speed information, so that the entire system can operate more stably and efficiently in speed-related applications, avoiding problems such as failures or performance degradation caused by inaccurate speed information.

[0133] In a preferred embodiment, the heat dissipation measures include air cooling, water cooling or liquid cooling, and the adaptive thermal management module selects the most appropriate heat dissipation method for heat dissipation according to the heat dissipation requirements and the actual working conditions of the motor.

[0134] The adaptive thermal management module selects the appropriate cooling method based on the cooling requirements and the actual motor operating conditions. Real-time Monitoring and Requirement Assessment: Using temperature sensors and other devices, the module monitors the temperature of various motor components in real time. Based on the actual motor operating conditions (such as speed and load), it calculates the current cooling requirement. For example, during low loads or short periods of operation, the cooling requirement may be low; during high loads or long periods of operation, the cooling requirement increases significantly. Cooling Method Selection: The adaptive thermal management module automatically selects the most appropriate cooling method based on the cooling requirement and the motor's operating status. Air Cooling: When cooling requirements are low, air cooling is preferred. By increasing the cooling fan speed and air flow, it quickly dissipates heat generated by the motor. This method is low-cost and simple, making it suitable for scenarios with low cooling requirements. Water or Liquid Cooling: When cooling requirements are high, water or liquid cooling is switched to. Water or liquid cooling more efficiently transfers heat and quickly reduces motor temperature. This cooling method is particularly effective when the motor is operating under high load or when rapid cooling is required. Real-time adjustment and optimization: After the cooling method is selected, the adaptive thermal management module will monitor the cooling effect in real time and fine-tune cooling parameters (such as fan speed and coolant flow) according to temperature changes to ensure the cooling effect is optimal and the motor temperature is always maintained within the preset safe temperature range.

[0135] In a preferred embodiment, during the operation of the motor, the operating status of the motor is also monitored in real time and fault diagnosis is performed. When a fault is detected, protective measures are taken in a timely manner, such as reducing the motor speed, reducing the load or shutting down the motor, to prevent the fault from expanding.

[0136] Monitoring motor operating conditions and diagnosing faults are crucial to ensuring motor reliability and safety. Key parameters such as temperature, current, speed, and vibration reflect the motor's operating status. By setting threshold comparisons and performing trend analysis, the system analyzes monitoring data in real time and predicts potential faults. Comprehensive multi-parameter analysis further enhances fault diagnosis accuracy. For example, increasing current and decreasing speed may indicate a motor overload, while a rapid temperature rise or a gradual increase in current could signal an impending fault.

[0137] Once a fault is detected, the system takes appropriate protective measures based on the severity and type of the fault. These measures include reducing motor speed, reducing load, or, in severe cases, shutting down the machine to prevent equipment damage and safety incidents. Furthermore, the system incorporates feedback and alarm mechanisms, sending fault information to operators or a central management system and alerting relevant personnel through audible and visual alarms or monitoring software prompts to ensure a timely response. This real-time monitoring and fault diagnosis system significantly improves motor reliability and safety, reduces downtime and repair costs, and ensures continuous and efficient production processes.

[0138] A high-voltage operating condition adaptive thermal management system for a magnetic levitation motor, the magnetic levitation motor comprising a magnetic bearing, a speed sensor, and a temperature sensor, the management system comprising:

[0139] an acquisition module, configured to acquire input voltage information and first speed information of the magnetic bearing through the speed sensor, and acquire initial temperature information of the motor through the temperature sensor;

[0140] a first determining module, configured to determine second speed information of the magnetic bearing according to the voltage information, where the second speed information includes first sub-information and second sub-information;

[0141] a second determining module, configured to determine target speed information according to the first speed information and the fluctuation deviation of the second speed information;

[0142] a control module, configured to control the operation of the magnetic bearing according to the target speed information, so as to improve the operation stability of the magnetic bearing;

[0143] An adaptive thermal management module is connected to the temperature sensor, the temperature sensor is used to collect temperature data of the motor in real time, and the adaptive thermal management module dynamically adjusts the heat dissipation intensity and heat dissipation mode of the motor according to a preset temperature rise control strategy;

[0144] Thermal power calculation unit, used to calculate the thermal power of the motor in real time according to the motor speed, load and temperature changes during the motor operation;

[0145] The temperature prediction unit is used to predict the temperature change trend of the motor according to the thermal power and temperature-heat dissipation correlation model, and start or adjust the heat dissipation measures in advance to keep the motor temperature within a preset safe temperature range.

[0146] The adaptive thermal management system for high-voltage operation of magnetic levitation motors integrates multiple functional modules to achieve precise control of the motor. The motor includes a magnetic bearing, a speed sensor, and a temperature sensor. The management system includes an acquisition module that acquires the input voltage, the first speed of the magnetic bearing, and the initial temperature of the motor. A first determination module determines the second speed of the magnetic bearing based on the voltage information, while a second determination module determines the target speed based on the fluctuation deviation of the first and second speed information. The control module then controls the operation of the magnetic bearing to improve its stability.

[0147] The system also features adaptive thermal management. The adaptive thermal management module is connected to the temperature sensor to collect motor temperature data in real time and dynamically adjust the heat dissipation intensity and method based on the preset temperature rise control strategy. Simultaneously, the system's built-in thermal power calculation unit calculates the motor's thermal power in real time based on speed, load, and temperature changes during motor operation. The temperature prediction unit, based on the thermal power and temperature-heat dissipation correlation model, predicts the motor's temperature change trend and initiates or adjusts heat dissipation measures in advance to ensure that the motor temperature remains within the preset safety range, thereby fully guaranteeing the stable operation of the magnetic levitation motor under high-voltage conditions.

[0148] In a preferred embodiment, a motor parameter identification module is also included. The motor parameter identification module adopts the least squares method or recursive algorithm and uses the real-time operating data of the motor under high-voltage conditions to update the motor's winding resistance, stator inductance, rotor time constant and other parameters.

[0149] In a preferred embodiment, a weight factor adjustment module is further included, which is used to dynamically adjust the weight factor according to the load characteristics of the motor. When the motor is lightly loaded, the weight of the first speed information is increased; when the motor is heavily loaded, the weight of the second speed information is increased.

[0150] In the motor management system, the dynamic weighting factor adjustment module adjusts the weighting factors based on the motor load characteristics. When the motor operates under different loads, the primary speed information (such as directly measured speed) is more stable and reliable under light loads. Increasing its weight in these conditions can reduce interference caused by fluctuations in the secondary speed information (such as speed calculated from other factors such as voltage), making the speed calculation more accurate. Under heavy loads, the secondary speed information often better reflects the actual motor operating status. Increasing its weight corrects for deviations in the primary speed information, thereby improving the accuracy of speed calculation under various load conditions and providing a reliable basis for precise motor control.

[0151] By properly assigning weighting factors, the system can quickly respond to load changes. Under light loads, it relies on more stable speed measurements to avoid system oscillations caused by overreliance on the easily disturbed calculated speed. Under heavy loads, it fully utilizes the calculated speed information to prevent motor stalls or overloads caused by measurement lag. This dynamic adjustment mechanism effectively suppresses speed fluctuations, enhances motor operation stability, reduces mechanical stress and vibration caused by sudden speed changes, and extends motor life while ensuring production continuity and product quality.

[0152] Motor loads vary widely across different application scenarios. Dynamically adjusting weighting factors enables the management system to automatically adapt to a wide range of operating conditions, from light to heavy, without requiring frequent manual intervention to adjust control parameters. Whether experiencing sudden load increases or decreases, the system quickly switches to the optimal speed information fusion strategy, ensuring the motor always operates within an efficient and stable range. This significantly improves the versatility and flexibility of the motor management system, making it easier to apply to a wide range of industrial equipment and complex process flows.

[0153] In a preferred embodiment, the adaptive thermal management module includes a feedforward control unit and a closed-loop control unit. The feedforward control unit predicts the heating condition of the motor according to the speed and load of the motor and adjusts the heat dissipation intensity in advance; the closed-loop control unit monitors the motor temperature in real time and adjusts the heat dissipation intensity according to temperature deviation feedback.

[0154] In a preferred embodiment, the system also includes a fault diagnosis module for real-time monitoring of the motor's operating status and fault diagnosis. When a fault is detected, protective measures are taken promptly, such as reducing motor speed, reducing load, or shutting down the motor, to prevent the fault from escalating. Real-time monitoring of the motor's operating status allows for the timely detection of potential faults and the implementation of protective measures, thereby reducing the probability of faults and ensuring stable motor operation.

[0155] The present invention uses feedforward control to proactively adjust cooling measures based on changes in motor speed and load during motor operation. Closed-loop control monitors motor temperature in real time and makes fine adjustments based on temperature deviations. Feedforward control provides basic cooling adjustments, while closed-loop control provides fine-tuning. Furthermore, the present invention calculates the motor's thermal power in real time. Based on a thermal power and temperature-heat dissipation correlation model, it predicts temperature changes and proactively initiates or adjusts cooling measures. Feedback from both feedforward and closed-loop control allows for dynamic adjustment of cooling measures. For example, if feedforward control predicts an increase in load, cooling intensity is proactively increased; if closed-loop control detects a temperature deviation, cooling measures are further adjusted based on the deviation. The selection and intensity of cooling measures are optimized based on thermal power and the temperature-heat dissipation correlation model. For example, when thermal power is high, water or liquid cooling is preferred; when thermal power is low, air cooling is used. A self-learning and self-optimization mechanism updates the thermal power calculation formula and the parameters of the temperature-heat dissipation correlation model in real time, ensuring model accuracy and adaptability.

[0156] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as covering the preferred embodiments and all changes and modifications that fall within the scope of the invention. The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for adaptive thermal management of a magnetic levitation motor under high-voltage conditions, characterized in that: The magnetic levitation motor includes a magnetic levitation bearing, a speed sensor, and a temperature sensor. The management method includes: Acquire input voltage information and obtain first speed information of the magnetic bearing through the speed sensor, and simultaneously obtain initial temperature information of the motor through the temperature sensor; Determining second speed information of the magnetic bearing based on the voltage information, the second speed information including first sub-information and second sub-information; the first sub-information is a speed estimation value calculated based on a voltage-speed estimation model, and the second sub-information is a speed correction value calculated based on a motor parameter identification algorithm combined with the voltage information; , and are weight coefficients respectively; determining target speed information based on a fluctuation deviation between the first speed information and the second speed information; calculating a difference between the first speed information and the second speed information and a fluctuation amplitude thereof; and triggering a speed information fusion mechanism when the fluctuation amplitude exceeds a preset fluctuation threshold to weightedly fuse the first speed information and the second speed information according to a preset weight factor, obtaining fused speed information as target speed information; Controlling the operation of the magnetic bearing according to the target speed information to improve the operating stability of the magnetic bearing; The magnetic levitation motor further includes an adaptive thermal management module, which is connected to the temperature sensor. The temperature sensor is used to collect temperature data of the motor in real time and input the data into the adaptive thermal management module. The adaptive thermal management module dynamically adjusts the heat dissipation intensity and heat dissipation mode of the motor according to a preset temperature rise control strategy. During the operation of the motor, the adaptive thermal management module calculates the thermal power of the motor in real time according to the speed, load and temperature changes of the motor; According to the thermal power and temperature-heat dissipation correlation model, the temperature change trend of the motor is predicted, and heat dissipation measures are started or adjusted in advance to keep the motor temperature within a preset safe temperature range.

2. The method for adaptive thermal management of a magnetic levitation motor under high voltage conditions according to claim 1, characterized in that: The motor parameter identification algorithm uses the real-time operating data of the motor under high-voltage conditions to update the motor parameters to improve the speed calculation accuracy. Specifically, the motor voltage, current, and speed are used to update the motor winding resistance, stator inductance, and rotor time constant in real time through the least squares method or recursive algorithm.

3. The method for adaptive thermal management of a magnetic levitation motor under high voltage conditions according to claim 1, characterized in that: The weight factor is dynamically adjusted according to the load characteristics of the motor. Specifically, when the motor is lightly loaded, the weight of the first speed information is increased; when the motor is heavily loaded, the weight of the second speed information is increased.

4. The method for adaptive thermal management of a magnetic levitation motor under high voltage conditions according to claim 1, characterized in that: The temperature rise control strategy includes a composite control mode that combines feedforward control based on the motor's operating status and closed-loop control based on real-time temperature feedback. Specifically, the heat generation condition of the motor is predicted according to its speed and load, and the heat dissipation intensity is adjusted in advance as feedforward control; at the same time, the motor temperature is monitored in real time, and the heat dissipation intensity is adjusted according to the temperature deviation feedback as closed-loop control.

5. The method for adaptive thermal management of a magnetic levitation motor under high voltage conditions according to claim 1, characterized in that: The calculation formula of the thermal power is: ;in, represents thermal power, represents the motor current, represents the winding resistance, represents the magnetic field frequency, represents the magnetic flux density, represents the hysteresis loss coefficient, represents the eddy current loss coefficient, Indicates the mass of the stator core, Represents the hysteresis loss index.

6. The method for adaptive thermal management of a magnetic levitation motor under high voltage conditions according to claim 1, characterized in that: The temperature-heat dissipation correlation model is established based on the thermophysical parameters of the motor material and the thermal resistance and heat capacity of the heat dissipation path. It is used to characterize the quantitative relationship between the motor temperature and the required heat dissipation capacity. The specific form is: ;in, Indicates the heat dissipation requirements, Indicates the motor temperature, Indicates the ambient temperature, Indicates thermal resistance.

7. The method for adaptive thermal management of a magnetic levitation motor under high voltage conditions according to claim 1, characterized in that: During the operation of the motor, the operating status of the motor is also monitored in real time and fault diagnosis is performed. When a fault is detected, protective measures are taken in time to prevent the fault from expanding.

8. A magnetic levitation motor high voltage working condition adaptive thermal management system, characterized in that: The method for adaptive thermal management of a magnetic levitation motor under high-voltage working conditions according to claim 1 is adopted. The magnetic levitation motor includes a magnetic bearing, a speed sensor, and a temperature sensor. The management system includes: an acquisition module, configured to acquire input voltage information and first speed information of the magnetic bearing through the speed sensor, and acquire initial temperature information of the motor through the temperature sensor; a first determining module, configured to determine second speed information of the magnetic bearing according to the voltage information, where the second speed information includes first sub-information and second sub-information; a second determining module, configured to determine target speed information according to the first speed information and the fluctuation deviation of the second speed information; a control module, configured to control the operation of the magnetic bearing according to the target speed information, so as to improve the operation stability of the magnetic bearing; An adaptive thermal management module is connected to the temperature sensor, the temperature sensor is used to collect temperature data of the motor in real time, and the adaptive thermal management module dynamically adjusts the heat dissipation intensity and heat dissipation mode of the motor according to a preset temperature rise control strategy; Thermal power calculation unit, used to calculate the thermal power of the motor in real time according to the motor speed, load and temperature changes during the motor operation; The temperature prediction unit is used to predict the temperature change trend of the motor according to the thermal power and temperature-heat dissipation correlation model, and start or adjust the heat dissipation measures in advance to keep the motor temperature within a preset safe temperature range.

9. The magnetic levitation motor high voltage working condition adaptive thermal management system according to claim 8, characterized in that: It also includes a weight factor adjustment module, which is used to dynamically adjust the weight factor according to the load characteristics of the motor. When the motor is lightly loaded, the weight of the first speed information is increased; when the motor is heavily loaded, the weight of the second speed information is increased.

10. The magnetic levitation motor high voltage working condition adaptive thermal management system according to claim 8, characterized in that: The system also includes a fault diagnosis module for real-time monitoring of the motor's operating status and fault diagnosis. When a fault is detected, protective measures are taken in a timely manner to prevent the fault from expanding.

Images