Motor overload control system and method based on high-precision sampling

Abstract

The application provides a motor overload control system and method based on high-precision sampling, and relates to the technical field of motor control. The system comprises a high-precision sampling module and a control module; the control module is connected to the high-precision sampling module; the high-precision sampling module is used for sampling the motor based on an oversampling rate to obtain sampling data; and the control module is used for determining thermal state data of the motor based on the sampling data, and generating a control instruction for dynamically controlling the motor based on the thermal state data and an overload demand. Precise thermal state sensing is realized through high-precision sampling, and dynamic control is performed based on real-time thermal state. When needed, part of the performance can be actively sacrificed to reduce heat generation, so that the short-time overload capacity of the motor is greatly increased, thereby allowing a smaller-power motor to be selected to meet special working condition requirements, greatly reducing the installed cost and operation energy consumption, while ensuring the safety and continuity of the overload process, and optimizing the control effect for the motor overload condition.

Description

Technical Field

[0001] This application relates to the field of motor control technology, and more specifically, to a motor overload control system and method based on high-precision sampling. Background Technology

[0002] Currently, the mainstream overload protection and control solutions in industrial motor control systems adopt a time-cutoff or I²t integral protection architecture. Taking a typical servo drive or frequency converter as an example, its overload control process can include: Current sampling stage: The motor phase current is collected by a Hall current sensor, and after signal conditioning circuitry, it is converted into a digital signal by a common ADC (analog-to-digital converter). The resolution of the ADC is usually 10-12 bits, and the sampling frequency is synchronized with the PWM switching frequency (about 10kHz). The sampling accuracy is limited, especially under low current or low speed conditions, and the signal-to-noise ratio is low. Control algorithm stage: The controller performs PI regulation based on the sampled current to generate a PWM control signal. The overload protection function is implemented in one of the following two ways: 1. Time-cutoff protection: Set a current threshold. (e.g., 1.5 times the rated current). When the current exceeds this threshold, an internal timer is started. After the timer reaches the preset time (e.g., 20 seconds), the controller blocks the PWM output, and the motor stops. 2. I²t integral protection: Simulates the thermal effect of a thermal relay by integrating the square of the current over time (∫I²dt). When the integral value exceeds the preset thermal capacity threshold, a protection shutdown is triggered. This integral value usually decays with a certain time constant after the current decreases, simulating the heat dissipation process. Overload handling method: Whether it is timed cutoff or I²t integral, the existing overload handling technology is a "trigger-and-stop" hard protection strategy. Once the protection condition is met, the system directly cuts off the output, and the motor stops running. Control mode: Throughout the overload process, the motor always operates in closed-loop vector control mode, and the current loop and speed loop are controlled by a PI regulator.

[0003] The current control scheme has the following problems: It uses a fixed timer threshold (e.g., 20 seconds) or a fixed I²t integral threshold, and this fixed threshold is based on safety considerations under the worst operating conditions (e.g., highest ambient temperature, worst heat dissipation conditions), resulting in a very conservative value. Due to the use of a trigger-and-stop hard protection strategy, once the overload time reaches the threshold, the system directly blocks the output, and the motor immediately stops running. The sampling accuracy of ordinary ADCs (10-12 bits) is limited, especially under low-speed or low-current conditions, resulting in a low signal-to-noise ratio in the sampled data. This leads to insufficient precision in the controller's perception of the motor state, making fine-grained overload control impossible. Although the I²t integral simulates the thermal effect, it is an open-loop mathematical model and does not form a closed loop with the actual thermal state of the motor. The decay time constant of the integral value is fixed and cannot reflect the actual changes in the motor's heat dissipation (e.g., changes in fan speed, ambient temperature, dust accumulation affecting heat dissipation, etc.). Maintaining the same control mode and parameters (e.g., high-precision speed control, high dynamic response) during overload does not consider the actual differences in user needs under different operating conditions. Therefore, current methods for controlling motor overload are ineffective and cannot meet actual usage requirements. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a motor overload control system and method based on high-precision sampling, so as to improve the problem of poor control effect for motor overload in the prior art.

[0005] To address the aforementioned issues, in a first aspect, embodiments of this application provide a motor overload control system based on high-precision sampling, the system comprising: a high-precision sampling module and a control module; The control module is connected to the high-precision sampling module; The high-precision sampling module is used to sample the motor based on the oversampling rate to obtain sampled data; The control module is used to determine the thermal state data of the motor based on the sampled data, and to generate control commands for dynamic overload control of the motor based on the thermal state data and overload requirements.

[0006] In the above implementation process, the motor overload control system can be equipped with a high-precision sampling module with high sampling accuracy. This module can obtain high-precision sampling data even without a physical position encoder. The control module then determines the actual thermal state data of the motor based on this data, and generates control commands for dynamic overload control of the motor based on the thermal state data and the actual overload requirements. This high-precision sampling enables accurate thermal state perception of the motor and allows for dynamic control based on real-time thermal conditions. When necessary, some performance can be proactively sacrificed to reduce heat generation, significantly increasing the motor's short-term overload capacity. This allows for the selection of smaller power motors to meet specific operating conditions, significantly reducing installation costs and operating energy consumption, while ensuring the safety and continuity of the overload process. It optimizes the control effect for motor overload conditions, meeting the needs of various application scenarios.

[0007] Optionally, the high-precision sampling module includes a Σ-Δ modulator.

[0008] In the above implementation process, the high-precision sampling module can be set as a Σ-Δ modulator, which can effectively increase the effective number of bits of the detected sampling data through high sampling rate and noise shaping technology, so that the control module can accurately extract the weak back electromotive force signal at low frequency from strong noise based on the sampling data.

[0009] Optionally, the oversampling rate OSR of the Σ-Δ modulator is ≥64, and the effective number of bits ENOB is ≥12.

[0010] In the above implementation process, the Σ-Δ modulator has a high oversampling rate and a high effective bit depth for signal detection, which effectively improves the accuracy of the obtained sampled data to achieve high-precision sampling.

[0011] Optionally, the high-precision sampling module includes: a high-resolution successive approximation register-type analog-to-digital converter and a low-noise preamplifier; wherein the high-resolution successive approximation register-type analog-to-digital converter has a resolution ≥16 and a sampling rate ≥1Msps.

[0012] In the above implementation process, the high-precision sampling module can also be configured as a high-resolution successive approximation register-type analog-to-digital converter and a low-noise preamplifier. Furthermore, the high-resolution successive approximation register-type analog-to-digital converter has a resolution ≥16 and a sampling rate ≥1Msps, so as to obtain sampling data with a high effective bit count through the combined operation of multiple devices.

[0013] Optionally, the control module controls the motor to operate based on a closed-loop control mode, wherein the starting frequency of the motor is ≤0.5Hz in the closed-loop control mode.

[0014] In the above implementation process, to further improve the motor control effect, the control module can control the motor based on a closed-loop control mode. Through coordinate transformation, it can decouple the motor's three-phase AC current into two independent DC components: the excitation current component and the torque current component. These two components are then precisely controlled in a closed-loop manner, resulting in high control accuracy. In closed-loop control mode, the control module can extract weak low-frequency back electromotive force signals from the sampled data, thereby starting the motor at an ultra-low starting frequency. This allows the system to stably enter the closed-loop control mode at ultra-low starting frequencies, achieving ultra-low frequency starting.

[0015] Optionally, the control module includes: a filter and an overload controller; The filter is used to filter and downsample the received sampled data to obtain digital data; The overload controller is used to determine the effective current data of the motor based on the digital data; and to process the effective current data and / or the digital data through a preset thermal model to determine the thermal state data of the motor; wherein, the thermal state data includes: the cumulative heat generation and / or equivalent temperature of the motor.

[0016] In the above implementation process, the control module is equipped with corresponding filters and an overload controller. The filters can filter and downsample the received sampled data to reconstruct it into high-resolution and high signal-to-noise ratio digital data. The overload controller can determine the effective current data of the motor based on the digital data, and process the effective current data and / or the digital data through a preset thermal model to determine parameters such as the cumulative heat generation and / or equivalent temperature of the motor as thermal state data characterizing the actual heating of the electrodes. This effective processing of the sampled data results in higher accuracy in sensing the thermal state, capturing minute fluctuations and spikes in the current, thus more realistically reflecting the motor's heating status and further improving the effectiveness and accuracy of the thermal state data.

[0017] Optionally, the overload controller is further configured to: determine the thermal capacity of the motor based on the thermal state data of the motor; determine the corresponding overload time based on the thermal capacity and the overload demand; and generate the control command for dynamic overload control of the motor based on the overload time.

[0018] In the above implementation process, the overload controller can determine the current thermal capacity of the motor based on the motor's thermal state data. Then, based on the thermal capacity and the user's actual overload requirements, it performs thermal budget management, dynamically calculates the current allowable overload time of the motor, and generates control commands for dynamic overload control of the motor based on the overload time. This ability to dynamically calculate the corresponding overload time according to the actual thermal state and overload requirements effectively reduces the adverse effects caused by fixed overload times.

[0019] Optionally, the overload controller is further configured to: if it is determined that the heat capacity is less than or equal to a preset threshold, then based on a preset smooth load reduction strategy, smoothly reduce the current limit value of the motor.

[0020] In the above implementation process, when the motor's thermal state data indicates that the motor is approaching its limit, in order to reduce the adverse effects of direct shutdown on normal operation, the overload controller can compare the heat capacity with a preset threshold. If the heat capacity is less than or equal to the preset threshold, it indicates that the motor's current heating condition is approaching its limit. Based on a preset smooth load reduction strategy, the motor's current limit value can be smoothly reduced. This allows for linear or curved smooth load reduction when the motor's thermal state is close to its limit, avoiding sudden torque changes, maintaining continuous motor operation, and reducing the adverse effects of abrupt shutdown.

[0021] Optionally, a temperature sensor is provided on the motor; The overload controller is further configured to: acquire the real-time temperature of the motor detected by the temperature sensor; calibrate the thermal model based on the real-time temperature; determine the current control temperature based on the real-time temperature and the equivalent temperature; determine the current temperature range based on the current control temperature and a plurality of preset temperature ranges; wherein each temperature range is configured with a corresponding protection strategy; determine a target protection strategy among the plurality of protection strategies based on the current temperature range; and perform dynamic overload protection on the motor based on the target protection strategy.

[0022] In the above implementation process, a temperature sensor is installed on the motor to detect its real-time temperature. The overload controller can acquire the real-time temperature detected and transmitted by the temperature sensor to calibrate the thermal model. Based on the real-time temperature and the equivalent temperature, the current control temperature is determined. Based on the determined current control temperature, the current temperature range is determined from multiple preset temperature ranges, and the target protection strategy configured for the current temperature range is used to provide dynamic overload protection for the motor. This effectively improves the calculation accuracy of the thermal model based on real-time temperature and further enhances the safety of the motor during operation by providing dynamic overload protection for the motor through a multi-level protection strategy.

[0023] Optionally, the overload controller is further configured to: acquire adjustment data input by the user based on overload conditions; and adjust the performance parameters of the motor based on the adjustment data and the thermal state data according to a preset adjustment strategy; wherein the performance parameters include at least one of motor speed, speed control accuracy, response bandwidth, field weakening current component, and control mode.

[0024] In the above implementation process, in some special operating scenarios, users can input corresponding adjustment data based on specific overload conditions. The overload controller can then adjust the motor's performance parameters, such as speed, speed control accuracy, response bandwidth, field weakening current component, and control mode, based on the received adjustment data and a preset adjustment strategy, taking into account thermal state data. This allows for corresponding adjustments to performance parameters based on actual needs, resulting in a lower effective current value and a smoother current waveform for the motor at the same output torque, thereby actively reducing the motor's heating rate and extending the allowable overload time.

[0025] Secondly, embodiments of this application provide a motor overload control method based on high-precision sampling. The method is applied to the motor overload control system described in any one of the first aspects above, and the method includes: The high-precision sampling module samples the motor based on the oversampling rate to obtain sampled data; The control module determines the thermal state data of the motor based on the sampled data, and generates control commands for dynamic overload control of the motor based on the thermal state data and overload requirements.

[0026] In the above implementation process, a high-precision sampling module with high sampling accuracy can obtain high-precision sampling data without a physical position encoder. The control module determines the actual thermal state data of the motor based on the sampling data, and then generates control commands for dynamic overload control of the motor based on the thermal state data and the actual overload requirements of the motor.

[0027] Optionally, the control module includes: a filter and an overload controller; The step of determining the thermal state data of the motor based on the sampled data through the control module includes: The received sampled data is filtered and downsampled using the filter to obtain digital data. The overload controller determines the effective current data of the motor based on the digital data; the effective current data and / or the digital data are processed by a preset thermal model to determine the thermal state data of the motor; wherein, the thermal state data includes: the cumulative heat generation and / or equivalent temperature of the motor.

[0028] In the above implementation process, the control module is equipped with corresponding filters and overload controllers. The filters perform filtering and downsampling on the received sampled data to reconstruct it into high-resolution, high-signal-to-noise-ratio digital data. The overload controller determines the effective current data of the motor based on the digital data and processes the effective current data and / or the digital data using a preset thermal model to determine parameters such as the motor's cumulative heat generation and / or equivalent temperature as thermal state data characterizing the actual heating of the electrodes. This effective processing of the sampled data results in higher accuracy in sensing the thermal state, capturing minute fluctuations and spikes in the current, thus more realistically reflecting the motor's heating status and further improving the effectiveness and accuracy of the thermal state data.

[0029] Optionally, the step of generating control commands for dynamic overload control of the motor based on the thermal state data and overload requirements via the control module includes: The overload controller determines the thermal capacity of the motor based on the motor's thermal state data. The overload controller determines the corresponding overload time based on the heat capacity and the overload requirement. The overload controller generates control commands for dynamic overload control of the motor based on the overload time.

[0030] In the above implementation process, the overload controller can determine the current thermal capacity of the motor based on its thermal state data. Then, based on the thermal capacity and the user's actual overload requirements, thermal budget management is performed, dynamically calculating the current allowable overload time of the motor, and generating control commands for dynamic overload control of the motor based on the overload time. This ability to dynamically calculate the corresponding overload time according to the actual thermal state and overload requirements effectively reduces the adverse effects caused by fixed overload times.

[0031] Optionally, the method further includes: If the overload controller determines that the heat capacity is less than or equal to a preset threshold, it will smoothly reduce the current limit of the motor based on a preset smooth load reduction strategy.

[0032] In the above implementation process, when the motor's thermal state data indicates that the motor is approaching its limit, in order to reduce the adverse effects of direct shutdown on normal operation, the overload controller can compare the heat capacity with a preset threshold. If the heat capacity is less than or equal to the preset threshold, it indicates that the motor's current heating condition is approaching its limit. Based on a preset smooth load reduction strategy, the motor's current limit value can be smoothly reduced. This allows for linear or curved smooth load reduction when the motor's thermal state is close to its limit, avoiding sudden torque changes, maintaining continuous motor operation, and reducing the adverse effects of abrupt shutdown.

[0033] Optionally, a temperature sensor is provided on the motor; The method further includes: The overload controller obtains the real-time temperature of the motor detected by the temperature sensor. The thermal model is calibrated based on the real-time temperature using the overload controller. The overload controller determines the current control temperature based on the real-time temperature and the equivalent temperature. The overload controller determines the current temperature range based on the current control temperature and multiple preset temperature ranges; each temperature range is configured with a corresponding protection strategy. Based on the current temperature range, the overload controller determines a target protection strategy from among multiple protection strategies. The overload controller performs dynamic overload protection on the motor based on the target protection strategy.

[0034] In the above implementation process, a temperature sensor is installed on the motor to detect its real-time temperature. The overload controller acquires the real-time temperature detected and transmitted by the temperature sensor, calibrates the thermal model based on the real-time temperature, and determines the current control temperature based on the real-time temperature and the equivalent temperature. Based on the determined current control temperature, the controller identifies the current temperature range from multiple preset temperature ranges and performs dynamic overload protection on the motor based on the target protection strategy configured for the current temperature range. This effectively improves the calculation accuracy of the thermal model based on real-time temperature and further enhances the safety of the motor during operation by providing dynamic overload protection through a multi-level protection strategy.

[0035] Optionally, the method further includes: The overload controller acquires adjustment data input by the user based on overload conditions. The overload controller adjusts the motor's performance parameters based on the preset adjustment strategy, the adjustment data, and the thermal state data, according to a preset adjustment strategy. The performance parameters include at least one of the following: motor speed, speed control accuracy, response bandwidth, field weakening current component, and control mode.

[0036] In the above implementation process, in some special operating scenarios, users can input corresponding adjustment data based on specific overload conditions. The overload controller, based on the received adjustment data and in conjunction with preset adjustment strategies, can adjust performance parameters such as motor speed, speed control accuracy, response bandwidth, field weakening current component, and control mode, based on thermal state data. This allows for corresponding adjustments to performance parameters based on actual needs, resulting in a lower effective current value and smoother current waveform for the motor at the same output torque, thereby actively reducing the motor's heating rate and extending the allowable overload time.

[0037] In summary, the embodiments of this application provide a motor overload control system and method based on high-precision sampling. It can achieve accurate thermal state perception of the motor through high-precision sampling and perform dynamic control of the motor based on the real-time thermal state. When necessary, it can actively sacrifice some performance to reduce heat generation, thereby significantly increasing the short-term overload capacity of the motor. This allows for the selection of a smaller power motor to meet the requirements of special working conditions, significantly reducing installation costs and operating energy consumption. At the same time, it ensures the safety and continuity of the overload process, optimizes the control effect for motor overload conditions, and meets the usage requirements of various application scenarios. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A schematic diagram of a motor overload control system based on high-precision sampling is provided for an embodiment of this application; Figure 2 A schematic diagram of another high-precision sampling motor overload control system provided in this application embodiment; Figure 3 The first motor overload control method based on high-precision sampling is provided in the embodiments of this application; Figure 4 A detailed flowchart of step S220 provided for an embodiment of this application; Figure 5A detailed flowchart illustrating another step S220 provided in an embodiment of this application; Figure 6 This application provides a second method for motor overload control based on high-precision sampling. Figure 7 This application provides a third method for motor overload control based on high-precision sampling.

[0040] Icons: 110 - High-precision sampling module; 120 - Control module; A - Motor; 121 - Filter; 122 - Overload controller; 130 - Temperature sensor. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0042] Existing overload protection functions are typically implemented in one of the following two ways: 1. Timed cut-off protection: setting a current threshold. (e.g., 1.5 times the rated current). When the current exceeds this threshold, an internal timer is started. After the timer reaches the preset time (e.g., 20 seconds), the controller blocks the PWM output, and the motor stops. 2. I²t integral protection: Simulates the thermal effect of a thermal relay by integrating the square of the current over time (∫I²dt). When the integral value exceeds the preset thermal capacity threshold, a protection shutdown is triggered. This integral value usually decays with a certain time constant after the current decreases, simulating the heat dissipation process. Overload handling method: Whether it is timed cutoff or I²t integral, the existing overload handling technology is a "trigger-and-stop" hard protection strategy. Once the protection condition is met, the system directly cuts off the output, and the motor stops running. Control mode: Throughout the overload process, the motor always operates in closed-loop vector control mode, and the current loop and speed loop are controlled by a PI regulator. The current control scheme has the following problems: It uses a fixed timer threshold (e.g., 20 seconds) or a fixed I²t integral threshold, and this fixed threshold is based on safety considerations under the worst operating conditions (e.g., highest ambient temperature, worst heat dissipation conditions), resulting in a very conservative value. Due to the use of a trigger-and-stop hard protection strategy, once the overload time reaches the threshold, the system directly blocks the output, and the motor immediately stops running. The sampling accuracy of ordinary ADCs (10-12 bits) is limited, especially under low-speed or low-current conditions, resulting in a low signal-to-noise ratio in the sampled data. This leads to insufficient precision in the controller's perception of the motor state, making fine-grained overload control impossible. Although the I²t integral simulates the thermal effect, it is an open-loop mathematical model and does not form a closed loop with the actual thermal state of the motor. The decay time constant of the integral value is fixed and cannot reflect the actual changes in the motor's heat dissipation (e.g., changes in fan speed, ambient temperature, dust accumulation affecting heat dissipation, etc.). Maintaining the same control mode and parameters (e.g., high-precision speed control, high dynamic response) during overload does not consider the actual differences in user needs under different operating conditions. Therefore, current methods for controlling motor overload are ineffective and cannot meet actual usage requirements.

[0043] To address the aforementioned issues, this application provides a motor overload control system and method based on high-precision sampling. This system enables precise thermal state sensing of the motor through high-precision sampling and dynamic control of the motor based on real-time thermal status. When necessary, it can proactively sacrifice some performance to reduce heat generation, significantly increasing the motor's short-term overload capacity. This allows for the selection of smaller power motors to meet specific operating conditions, substantially reducing installation costs and operating energy consumption. Simultaneously, it ensures the safety and continuity of the overload process, optimizes the control effect for motor overload conditions, and meets the usage requirements of various application scenarios.

[0044] Please see Figure 1 , Figure 1This is a schematic diagram of a motor overload control system based on high-precision sampling provided in an embodiment of this application. The system may include a high-precision sampling module 110 and a control module 120.

[0045] The control module 120 is connected to the high-precision sampling module 110, and the high-precision sampling module 110 is connected to the controlled motor A.

[0046] Optionally, the control module 120 can be an electronic device with logic computing functions, such as a server, personal computer (PC), tablet computer, smartphone, or personal digital assistant (PDA), which can communicate with the high-precision sampling module 110 via network, Bluetooth, or other means. Alternatively, it can be a processor or other structure installed within the motor A control system, connected to the high-precision sampling module 110 via electrical cables or other devices. The processor may be an integrated circuit chip with signal processing capabilities, or a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0047] The high-precision sampling module 110 is used to sample motor A based on the oversampling rate to obtain sampled data. The control module 120 is used to determine the thermal state data of motor A based on the sampled data, and to generate control commands for dynamic overload control of motor A based on the thermal state data and overload requirements. The motor overload control system can be equipped with a high-precision sampling module 110, which can obtain high-precision sampled data without a physical position encoder. The control module 120 then determines the actual thermal state data of motor A based on the sampled data, and generates control commands for dynamic overload control of motor A based on the thermal state data and the actual overload requirements of motor A.

[0048] It should be noted that, in order to achieve the driving control effect of motor A, a high-frequency power module can also be set in the system. The high-frequency power module is connected to the control module 120 and motor A. The control module 120 controls the operation of the high-frequency power module, and the high-frequency power module generates a corresponding PWM wave to drive motor A to run according to the control command generated by the control module 120.

[0049] Optionally, the high-precision sampling module 110 can be an analog-to-digital converter capable of achieving a high oversampling rate (OSR≥256) and noise shaping. The high-frequency power module can be an inverter circuit composed of wide-bandgap semiconductor power switching devices.

[0050] Optionally, the high-precision sampling module 110 may include a Σ-Δ modulator, which can effectively increase the effective number of bits of the detected sampling data through high sampling rate and noise shaping technology, so that the control module 120 can accurately extract the weak back electromotive force signal at low frequency from strong noise based on the sampling data.

[0051] Optionally, the high-frequency power module may include a SiC inverter power module. Compared with existing solutions using IGBT devices, the SiC inverter power module has a higher switching frequency and lower switching losses, effectively improving startup efficiency and providing a control basis for the precise closed-loop control algorithm in the control module 120. The SiC inverter power module may be equipped with multiple SiC MOSFETs (metal-oxide-semiconductor field-effect transistors based on silicon carbide). Due to the extremely high electron saturation drift velocity of SiC material, SiC MOSFETs have very fast switching speeds, i.e., extremely short turn-on / turn-off times. Furthermore, SiC devices still have very low on-state resistance under high voltage, resulting in low conduction losses. Their switching losses are far lower than those of IGBT devices used in existing technologies, thereby improving the overall control efficiency of the SiC inverter power module through extremely low switching losses. Furthermore, for motor A, the SiC inverter power module allows for the use of higher PWM switching frequencies. For example, the switching frequency of the SiC inverter power module used in this application is ≥40kHz, which is much higher than the ≤10kHz switching frequency of existing IGBT devices. This higher switching frequency enables a more sinusoidal current waveform in motor A, reducing torque ripple and motor A heat generation, thereby reducing noise during motor A operation and allowing motor A to respond more quickly to load and command changes, improving its dynamic performance. In addition, the junction temperature of the SiC inverter power module is ≥175℃, making it suitable for various high-temperature operating environments. This high-temperature resistance significantly improves the reliability of motor A operation.

[0052] Optionally, the Σ-Δ modulator can sample the current at a rate much higher than the highest frequency of the signal (oversampling) and shape the quantization noise to a higher frequency band through a negative feedback loop, thereby achieving an extremely high signal-to-noise ratio in the baseband. This allows for the accurate capture of weak current changes and back electromotive force signals even when the motor A is running at low speed or under low current conditions.

[0053] It should be noted that the oversampling rate is much higher than the Nyquist frequency (twice the highest frequency of the signal). For a Σ-Δ modulator, the oversampling rate (OSR) ≥ 64, meaning the sampling frequency is 128 times the signal bandwidth. This allows quantization noise to be dispersed over a wider frequency range, significantly reducing noise density within the target signal bandwidth. It also provides sufficient transition band for subsequent data filtering, effectively filtering out out-of-band noise without affecting the signal itself. The effective bit depth (ENOB) measures the sampling accuracy; for a Σ-Δ modulator, ENOB ≥ 12, indicating the actual performance of the Σ-Δ modulator. Similar to an ideal 12-bit ADC, this allows the Σ-Δ modulator to resolve extremely small voltage changes. For example, within a ±10V range, the smallest voltage change the Σ-Δ modulator can resolve is approximately 20 / 2^12 ≈ 4.8mV, providing high signal fidelity. Furthermore, the combination of a high oversampling rate and a high effective bit count significantly improves the signal-to-noise ratio within the signal bandwidth, enabling the extraction of extremely weak effective signals against strong noise backgrounds. This effectively improves the accuracy and resolution of the sampled data, reduces dead zones and nonlinear distortion, and enhances the efficiency and performance of control based on sampled data.

[0054] For example, the oversampling rate (OSR) of a Σ-Δ modulator can be 256, and the effective bit depth can be 14 bits. The sampled data output by the Σ-Δ modulator can be high-speed bitstream data (1-bit data stream) with extremely high signal-to-noise ratio and resolution.

[0055] Alternatively, an integrated Σ-Δ modulator can be selected: the Σ-Δ modulator is integrated into the smart sensor chip inside motor A. Or a discrete Σ-Δ modulator can be selected: a separate Σ-Δ ADC chip (such as the AD740x series) is used outside motor A (e.g., inside the driver). Or an FPGA-implemented Σ-Δ modulator can be selected: the Σ-Δ modulation algorithm is implemented through digital logic inside the FPGA. Or other variations of the Σ-Δ modulator can be used: a cascaded Σ-Δ modulator (MASH structure) or a broadband Σ-Δ modulator can be employed.

[0056] Optionally, the SiC inverter power module can be packaged at high temperatures. For example, it can be packaged using HPD packaging, with a thermal resistance of <0.5K / W, to improve the reliability of the SiC inverter power module during operation. It should be noted that the specific implementation of the high-frequency power module is not limited to SiC inverter power modules. Conventional IGBT inverter modules or GaN (gallium nitride high electron mobility transistor) inverter modules, and other devices with high switching frequencies capable of high-frequency, high-efficiency drive, can also be used, as long as they can drive the motor according to control commands. The core of this application lies in high-precision sampling and dynamic overload control; the specific hardware selection of the high-frequency power module does not affect the implementation of this invention.

[0057] Optionally, the high-precision sampling module 110 may further include: a high-resolution successive approximation register-type analog-to-digital converter and a low-noise preamplifier, i.e., a high-resolution SAR ADC + a low-noise preamplifier and filter circuit. The high-resolution successive approximation register-type analog-to-digital converter has a resolution ≥16 and a sampling rate ≥1Msps, so as to obtain sampling data with a high effective bit count through the combined operation of multiple devices.

[0058] It should be noted that, to further improve the control effect of motor A, the control module 120 can control motor A based on a closed-loop control mode. Through coordinate transformation, it can decouple the three-phase AC current of motor A into two independent DC components: the excitation current component and the torque current component. These two components are then precisely controlled in a closed-loop manner, resulting in high control accuracy. In closed-loop control mode, the starting frequency of motor A is ≤0.5Hz. In this mode, the control module 120 can extract weak back EMF signals at low frequencies from the sampled data, thereby starting motor A at an ultra-low starting frequency. This allows the system to stably enter the closed-loop control mode at ultra-low starting frequencies, achieving ultra-low frequency starting.

[0059] For example, the closed-loop control mode may include a closed-loop control mode based on the FOC control algorithm.

[0060] Optionally, when starting motor A control, a DC current can be injected to fix the rotor in the initial position, and the initial V / F open-loop control mode can be activated to slowly increase the starting frequency to 0.5Hz at a slope of 0.1Hz / s.

[0061] For example, compared to the higher minimum starting frequency in the prior art, the minimum starting frequency of this application is ≤0.5Hz, enabling truly ultra-low frequency heavy-load starting. Compared to traditional control schemes, this application has the following starting characteristics: Significantly reduced starting current: Traditional schemes typically have a starting current of 2-3 times the rated current, while this application can control it to within 1.2 times the rated current, a reduction of over 50%. Smooth and shock-free starting process: Precise current control significantly reduces torque pulsation during the starting process. Reduced heat accumulation during the starting process: The reduction in starting current directly reduces I²R losses during the starting process, reserving more heat capacity for subsequent overload operation.

[0062] Optionally, please refer to Figure 2 , Figure 2 This is a schematic diagram of another high-precision sampling motor overload control system provided in an embodiment of this application, wherein the control module 120 may include a filter 121 and an overload controller 122.

[0063] Optionally, filter 121 is connected to high-precision sampling module 110 and overload controller 122. Filter 121 is used to filter and downsample the received sampling data to obtain digital data. Overload controller 122 is used to determine the effective current data of motor A based on the digital data. Through a preset thermal model, the effective current data and / or digital data are processed to determine the thermal state data of motor A. The thermal state data includes the cumulative heat generation and / or equivalent temperature of motor A.

[0064] For example, filter 121 can be a Sinc³ filter, CIC filter, or other similar devices. The Sinc³ filter can decode and downsample the sampled data, reconstructing the sampled data of the bitstream into high-resolution (e.g., 16-bit) digital current values ​​as digital data. Through its notch characteristics, the Sinc³ filter can provide good anti-aliasing and noise suppression functions, and has good anti-PWM interference capabilities, effectively improving the accuracy and effectiveness of the filtered data.

[0065] For example, the overload controller 122 can be a separate device or a high-performance main control chip (such as DSP / MCU / FPGA) integrated into the driver of motor A to provide sufficient computing power for data processing, execute various high-precision algorithms, and also perform parallel data processing through the hard computing power of high-performance multi-core DSP+FPGA, effectively improving the real-time performance of data processing, thereby improving the drive matching accuracy of the high-frequency power module.

[0066] Optionally, the overload controller 122 has a preset thermal model, which can be a first-order thermal model based on I²t integral, a first-order RC thermal model (equating the thermal characteristics of motor A to a first-order RC circuit), a second-order or multi-order RC network (simulating the heat transfer process of windings, core, casing, and environment), or a machine learning model (using a neural network model, taking parameters such as current, speed, and ambient temperature as input, and directly outputting predicted temperature values), etc. It can also pre-calibrate temperature rise data under different currents and times through experiments to form a two-dimensional lookup table, thereby determining the thermal state data through the lookup table method. The control module 120 is equipped with a corresponding filter 121 and an overload controller 122. The filter 121 can filter and downsample the received sampled data to reconstruct the sampled data into high-resolution and high signal-to-noise ratio digital data. The overload controller 122 can determine the effective current data of motor A based on digital data, and process the effective current data and / or the digital data through a preset thermal model to determine parameters such as the cumulative heat generation and / or equivalent temperature of motor A as thermal state data characterizing the actual heating of the electrodes.

[0067] It should be noted that, unlike the existing technology which directly uses I²t integral for calculation, the input control module 120 in this application uses a high-precision current value, which can effectively process the sampled data. The digital data has higher accuracy in sensing the thermal state and can capture small fluctuations and spikes in the current, thereby more realistically reflecting the heating status of motor A and further improving the effectiveness and accuracy of the thermal state data.

[0068] For example, the overload controller 122 can calculate the effective value and / or instantaneous value of the current of motor A based on digital data to determine the cumulative heat generation and / or equivalent temperature of motor A based on the effective value and / or instantaneous value of the current. The equivalent temperature can be estimated temperature data of the windings of motor A.

[0069] Optionally, the overload controller 122 is also used to: determine the thermal capacity of motor A based on the thermal state data of motor A; determine the corresponding overload time based on the thermal capacity and overload demand; and generate control commands for dynamic overload control of motor A based on the overload time. The overload controller 122 can determine the current thermal capacity of motor A based on the thermal state data of motor A, thereby performing thermal budget management based on the thermal capacity and the user's actual overload demand for motor A, dynamically calculating the currently allowed overload time of motor A, and generating control commands for dynamic overload control of motor A based on the overload time. It can dynamically calculate the corresponding overload time according to the actual thermal state and overload demand, effectively reducing the adverse effects caused by a fixed overload time (such as a fixed 20 seconds).

[0070] Optionally, overload requirements can be user requirements that limit the overload multiple and time, such as requiring a 2x overload for 5 minutes.

[0071] For example, a dynamically changing "budget" can be determined based on the thermal capacity of motor A. When the initial temperature of motor A is low and heat dissipation is good, the "thermal budget" is ample; when motor A operates continuously and the temperature rises, the "thermal budget" decreases. The real-time thermal capacity of motor A can be determined based on thermal state data, i.e., the ratio between the current generated heat and the total heat. The allowable overload time can be dynamically calculated based on the current actual thermal capacity and the overload multiple required in the user-defined overload demand. For example: Cold motor A (thermal capacity 0, thermal budget 100%): allows 2x overload for 8 minutes; Warm motor A (thermal capacity 40%, thermal budget 60%): allows 2x overload for 4 minutes; Hot motor A (thermal capacity 70%, thermal budget 30%): allows 2x overload for 1.5 minutes, or suggests reducing the overload multiple.

[0072] Optionally, the thermal budget can be divided into multiple intervals, each corresponding to a different overload capacity. A fuzzy logic-based decision-making approach is implemented: thermal budget, overload demand, ambient temperature, etc., are used as fuzzy inputs, and inference is performed using a fuzzy rule base. A model predictive control (MPC)-based decision-making approach is also implemented: the thermal model is used as the predictive model to solve the optimization problem and obtain the optimal current trajectory.

[0073] Optionally, the overload controller 122 is further configured to: if the thermal capacity is determined to be less than or equal to a preset threshold, then, based on a preset smooth load reduction strategy, smoothly reduce the current limit value of motor A. When the thermal state data of motor A indicates that motor A is approaching its limit state, in order to reduce the adverse effects of direct shutdown on normal operation, the overload controller 122 can compare the thermal capacity with the preset threshold. If the thermal capacity is less than or equal to the preset threshold, it indicates that the current heating state of motor A is approaching its limit state, and the current limit value of motor A can be smoothly reduced based on the preset smooth load reduction strategy. This enables linear or curved smooth load reduction when the thermal state of motor A is approaching its limit, avoiding sudden torque changes, maintaining continuous operation of motor A, and reducing the adverse effects of abrupt shutdown.

[0074] For example, multiple preset thresholds can be set, and a corresponding smooth load reduction strategy can be configured for each preset threshold. For instance, when the heat capacity is less than or equal to 10%, the corresponding smooth load reduction strategy is to smoothly reduce the current limit from 2 times the rated value to 1.8 times; when the heat capacity is less than or equal to 5%, the corresponding smooth load reduction strategy is to reduce the current limit from 1.8 times to 1.5 times; and when the heat capacity is less than or equal to 1%, the corresponding smooth load reduction strategy is to reduce the current limit from 1.5 times to 1.2 times. The above values ​​are merely examples, and those skilled in the art can adjust them according to the actual motor parameters.

[0075] Optionally, smooth load descent can be linear or exponential. Smooth load descent can include: Linear load descent: reducing the current limit linearly with thermal budget consumption. Exponential load descent: slow initial load descent, accelerating later. Stepped load descent with buffering: load descent in several steps, each step held for a few seconds. Maintaining minimum operating capacity: in extreme cases, reducing the current limit to a level that allows motor A to operate under no-load or light-load conditions.

[0076] Alternatively, please continue reading Figure 2A temperature sensor 130 can be installed on motor A. The overload controller 122 is also used to: acquire the real-time temperature of motor A detected by the temperature sensor 130; calibrate the thermal model based on the real-time temperature; determine the current control temperature based on the real-time temperature and the equivalent temperature; determine the current temperature range based on the current control temperature and multiple preset temperature ranges; wherein each temperature range is configured with a corresponding protection strategy; determine the target protection strategy among the multiple protection strategies based on the current temperature range; and perform dynamic overload protection on motor A based on the target protection strategy. The temperature sensor 130 can detect the real-time temperature of motor A, and the overload controller 122 can acquire the real-time temperature detected and sent by the temperature sensor 130, so as to calibrate the thermal model through the real-time temperature, and determine the current control temperature based on the real-time temperature and the equivalent temperature, so as to determine the current temperature range among multiple preset temperature ranges based on the determined current control temperature, and perform dynamic overload protection on motor A based on the target protection strategy configured for the current temperature range. It can effectively improve the calculation accuracy of the thermal model based on real-time temperature, and further improve the safety of motor A during operation by setting a multi-level protection strategy for dynamic overload protection.

[0077] For example, the temperature sensor 130 can be a device capable of real-time temperature detection, such as Pt100, NTC thermistor, thermocouple, or infrared non-contact temperature measuring device. Alternatively, the temperature sensor 130 can be omitted, and the real-time temperature can be determined directly by detecting the change in DC resistance of the stator winding. The overload controller 122 can compare the received real-time temperature and equivalent temperature at the same moment and select the larger value as the current control temperature to calibrate the thermal model online based on the current control temperature.

[0078] Optionally, different temperature ranges and corresponding protection strategies may include: warning zone (lower warning temperature ≤ current control temperature < higher warning temperature): start shortening the allowable overload time; load reduction zone (higher warning temperature ≤ current control temperature < limit temperature): perform smooth load reduction; limit zone (current control temperature ≥ limit temperature): only allow light load operation.

[0079] Optionally, the overload controller 122 is also used to: acquire adjustment data input by the user based on overload conditions; and adjust the performance parameters of motor A based on the adjustment data and thermal state data according to a preset adjustment strategy. The performance parameters include at least one of motor A's speed, speed control accuracy, response bandwidth, field weakening current component, and control mode. In some special application scenarios, the user can input corresponding adjustment data based on specific overload conditions. The overload controller 122 can, based on the received adjustment data and a preset adjustment strategy, adjust the performance parameters of motor A, such as speed, speed control accuracy, response bandwidth, field weakening current component, and control mode, according to the thermal state data. This allows for corresponding adjustments to performance parameters based on actual needs, resulting in a lower effective current value and smoother current waveform for motor A under the same output torque, thereby actively reducing the heating rate of motor A and extending the allowable overload time.

[0080] Optionally, the adjustment data input by the user may include various information such as prioritizing extending the overload time or prioritizing maintaining speed accuracy. For example, if the user needs to extend the overload time to meet special operating conditions (such as needing to maintain a large torque for several minutes when adjusting the position of the balance block of the pumping unit), it can be determined whether some performance parameters need to be sacrificed based on the current thermal state data.

[0081] Optionally, the performance parameters can be adjusted in the following ways: 1. Active speed reduction strategy: When it is necessary to extend the overload time, the motor A is allowed to actively reduce its operating speed under overload conditions, thereby reducing the output power, directly reducing the current demand, and reducing I²R heat generation. Specifically, the overload controller 122 can reduce the speed setpoint of the motor A from the rated speed to a lower value (e.g., from 1500rpm to 800rpm), or relax the speed control accuracy requirements, so that the output power of the motor A decreases due to the reduced speed while outputting the same torque, the required current decreases accordingly, and the heat generation rate is significantly reduced. This "speed for time" strategy can effectively extend the allowable overload duration while meeting the short-term high torque demand. 2. Speed ​​accuracy sacrifice strategy: Relax the speed control accuracy (e.g., from ±0.1% to ±2%), allocate more current resources to torque output, and reduce current fluctuations and additional heat generation caused by speed regulation. 3. Dynamic Response Bandwidth Sacrifice Strategy: Reduce the dynamic response bandwidth of the current loop and speed loop to decrease current spikes and oscillations during regulation, resulting in a smoother current waveform and reduced I²R losses. 4. Control Mode Switching Strategy: Under extreme overload conditions, the system can temporarily switch from vector control to V / F control, reducing switching losses and additional heat generation from the control algorithm. 5. Magnetic Weakening Strategy: For permanent magnet motor A, under controllable temperature conditions, the field weakening current component can be appropriately reduced to decrease copper losses. 6. Active Heat Reduction Strategy: By adjusting various performance parameters, motor A achieves a lower effective current value and a smoother current waveform at the same output torque, thereby actively reducing the heating rate and extending the allowable overload time.

[0082] Optionally, performance parameter adjustment strategies may also include: speed loop parameter adjustment: reducing proportional gain and increasing integral time; current loop bandwidth reduction: reducing the current loop cutoff frequency; control mode switching: switching from FOC to V / F control, or from DTC to indirect torque control; demagnetizing current optimization: adjusting the id / iq allocation ratio; and switching frequency reduction: reducing the PWM switching frequency to reduce switching losses (a trade-off between heat generation and harmonics is required).

[0083] Please see Figure 3 , Figure 3 The first motor overload control method based on high-precision sampling is provided in the embodiments of this application. The method is applied to the motor overload control system in any of the above embodiments. The method includes steps S210-S220.

[0084] Step S210: The motor is sampled using a high-precision sampling module based on the oversampling rate to obtain sampled data.

[0085] In step S220, the control module determines the thermal state data of the motor based on the sampled data, and generates control commands for dynamic overload control of the motor based on the thermal state data and overload requirements.

[0086] exist Figure 3 In the illustrated embodiment, a high-precision sampling module with high sampling accuracy can obtain high-precision sampling data without a physical position encoder. The control module determines the actual thermal state data of the motor based on the sampling data, and then generates control commands for dynamic overload control of the motor based on the thermal state data and the actual overload requirements of the motor.

[0087] Since the principle of the motor overload control method based on high-precision sampling in this application embodiment is similar to that of the aforementioned motor overload control system based on high-precision sampling, the implementation of the method in this embodiment can refer to the description in the above system embodiment, and the repeated parts will not be repeated.

[0088] Optionally, please refer to Figure 4 , Figure 4 The following is a detailed flowchart of step S220 provided in an embodiment of this application. The control module may include a filter and an overload controller. Step S220 may include steps S221-S222.

[0089] Step S221: The received sampled data is filtered and downsampled using a filter to obtain digital data.

[0090] Step S222: The effective current data of the motor is determined based on digital data using the overload controller; the effective current data and / or digital data are processed using a preset thermal model to determine the thermal state data of the motor.

[0091] The thermal status data may include: the motor's cumulative heat generation and / or equivalent temperature.

[0092] exist Figure 4 In the illustrated embodiment, the control module is equipped with corresponding filters and an overload controller. The filters perform filtering and downsampling on the received sampled data to reconstruct it into high-resolution, high-signal-to-noise-ratio digital data. The overload controller determines the effective current data of the motor based on the digital data and processes the effective current data and / or the digital data using a preset thermal model to determine parameters such as the motor's cumulative heat generation and / or equivalent temperature as thermal state data characterizing the actual heating of the electrodes. This effective processing of the sampled data results in higher accuracy in sensing the thermal state, capturing minute fluctuations and spikes in the current, thus more realistically reflecting the motor's heating status and further improving the effectiveness and accuracy of the thermal state data.

[0093] Optionally, please refer to Figure 5 , Figure 5 The following is a detailed flowchart of another step S220 provided in an embodiment of this application. Step S220 may include steps S223-S225.

[0094] Step S223: Determine the thermal capacity of the motor based on the motor's thermal state data using the overload controller.

[0095] Step S224: The overload controller determines the corresponding overload time based on the heat capacity and overload requirements.

[0096] Step S225: Based on the overload time, the overload controller generates a control command for dynamic overload control of the motor.

[0097] exist Figure 5 In the illustrated embodiment, the overload controller can determine the motor's current thermal capacity based on the motor's thermal state data. Then, based on the thermal capacity and the user's actual overload requirements, thermal budget management is performed, dynamically calculating the motor's current allowable overload time, and generating control commands for dynamic overload control of the motor based on the overload time. This ability to dynamically calculate the corresponding overload time according to the actual thermal state and overload requirements effectively reduces the adverse effects caused by fixed overload times.

[0098] Optionally, the method may further include: if the overload controller determines that the thermal capacity is less than or equal to a preset threshold, the overload controller, based on a preset smooth load reduction strategy, smoothly reduces the motor's current limit value. When the motor's thermal state data indicates that the motor is approaching its limit state, in order to reduce the adverse effects of direct shutdown on normal use, the overload controller can compare the thermal capacity with the preset threshold. If the thermal capacity is less than or equal to the preset threshold, it indicates that the motor's current heating condition is approaching its limit state, and the motor's current limit value can be smoothly reduced based on the preset smooth load reduction strategy. This allows for linear or curved smooth load reduction when the motor's thermal state is approaching its limit, avoiding sudden torque changes, maintaining continuous motor operation, and reducing the adverse effects of abrupt shutdown.

[0099] Optionally, please refer to Figure 6 , Figure 6 The second motor overload control method based on high-precision sampling provided in the embodiments of this application may further include steps S311-S316.

[0100] Step S311: Obtain the real-time temperature of the motor detected by the temperature sensor through the overload controller.

[0101] Step S312: The thermal model is calibrated based on real-time temperature using an overload controller.

[0102] Step S313: Determine the current control temperature based on the real-time temperature and the equivalent temperature using the overload controller.

[0103] Step S314: The overload controller determines the current temperature range based on the current control temperature and multiple preset temperature ranges.

[0104] Each temperature range is configured with a corresponding protection strategy; Step S315: Based on the current temperature range, determine the target protection strategy from multiple protection strategies using the overload controller.

[0105] Step S316: Dynamic overload protection of the motor is performed by the overload controller based on the target protection strategy.

[0106] exist Figure 6 In the illustrated embodiment, a temperature sensor is installed on the motor to detect its real-time temperature. The overload controller acquires the real-time temperature detected and transmitted by the sensor, calibrates the thermal model based on this temperature, and determines the current control temperature based on both the real-time and equivalent temperatures. Based on this determined current control temperature, the controller identifies the current temperature range from multiple preset temperature ranges and performs dynamic overload protection on the motor according to a target protection strategy configured for that range. This approach effectively improves the calculation accuracy of the thermal model based on real-time temperature and further enhances the motor's safety during operation through multi-level protection strategies.

[0107] Optionally, please refer to Figure 7 , Figure 7 The third motor overload control method based on high-precision sampling provided in the embodiments of this application may further include steps S321-S322.

[0108] Step S321: Obtain adjustment data input by the user based on the overload condition through the overload controller.

[0109] Step S322: The overload controller adjusts the motor's performance parameters based on the preset adjustment strategy, adjustment data, and thermal state data.

[0110] The performance parameters may include one or more parameters that affect the performance of the motor, such as motor speed, speed control accuracy, response bandwidth, field weakening current component, and control mode.

[0111] exist Figure 7In the illustrated embodiments, in application scenarios with specific operating conditions, users can input corresponding adjustment data based on specific overload conditions. The overload controller, based on the received adjustment data and a preset adjustment strategy, can adjust performance parameters such as motor speed, speed control accuracy, response bandwidth, field weakening current component, and control mode, based on thermal state data. This allows for adjustments to performance parameters according to actual needs, resulting in a lower effective current value and smoother current waveform for the motor at the same output torque, thereby actively reducing the motor's heating rate and extending the permissible overload time.

[0112] In summary, this application provides a motor overload control system and method based on high-precision sampling. Compared with the prior art, this application has the following significant advantages and technical effects: Effect 1: Significantly extends the effective overload time, achieving short-term overload capability of "a few minutes to a dozen minutes". Existing technologies only rely on overload protection based on fixed timers, allowing an overload time of typically 15-30 seconds. This application, however, uses a dynamic overload decision-making method to dynamically calculate the allowable overload time based on the motor's real-time thermal state. When the motor is cold or has good heat dissipation, the allowable overload time can reach several minutes to a dozen minutes (e.g., 5-12 minutes). This application accurately senses the motor state through high-precision sampling, allowing full utilization when the actual heat capacity is low. It can significantly improve the effective overload capability of the motor based on its actual state. Under favorable operating conditions such as cold conditions, the improvement may reach an order of magnitude or more, directly solving the industry pain point of "having to have a large installed power". Effect 2: Achieves a significant reduction in installed power, saving costs and energy consumption. To meet the short-term overload requirement of a few minutes, existing technologies force users to choose motors with a rated power several times larger. In this application's solution, the short-term overload capacity of the motor is fully utilized, allowing users to select a motor with a smaller rated power to handle special operating conditions through its overload capacity. For example, a piece of equipment normally requires 15kW, but under special conditions requires 30kW for 3 minutes. Using this application's solution, a 15kW motor can be selected. The installed power is reduced by 50%, which not only saves on equipment procurement costs but also ensures that the motor operates in the high-efficiency zone year-round, significantly reducing operating energy consumption (efficiency in the high-efficiency zone can be 5-15% higher than in the low-efficiency zone). Thirdly, it achieves smooth overload handling, avoiding production interruptions caused by sudden shutdowns. Existing technologies employ a "trigger-based shutdown" hard protection strategy. This application uses a smooth output adjustment method to perform smooth load reduction when approaching the thermal limit, gradually reducing the output capacity. The smooth load reduction process lasts for tens of seconds, allowing operators time to process the load and the upper-level control system time to respond. This is particularly important in safety-related situations, significantly improving system availability and safety. Effect 4: Improved accuracy and reliability of overload control. Existing technologies suffer from low-precision sampling, leading to vague perception of motor status. This application, based on high-precision sampling technology, can accurately capture minute changes and spikes in current, making thermal status perception more realistic and accurate. High-precision sampling allows the controller to more accurately calculate motor heat generation, avoiding process protection or under-process protection caused by sampling errors. Simultaneously, the current waveform is closer to an ideal sine wave, harmonic losses are reduced, and the motor's own heat generation is also decreased.Effect 5: Significantly reduced starting current creates better initial conditions for overload control. Traditional starting currents reach 2-3 times the rated current. This application, based on high-precision sampling, achieves ultra-low frequency precise closed-loop starting at ≤0.5Hz, controlling the starting current within 1.2 times the rated current. The reduced starting current means significantly reduced I²R losses during startup, lower initial motor temperature, and a more ample thermal budget, reserving more thermal resources for subsequent overload operation. Effect 6: Multi-level temperature closed-loop protection mechanism achieves optimal balance between safety and performance. Existing overload protection technologies are either too conservative or too risky, resulting in poor performance. This application employs a multi-level temperature closed-loop protection mechanism, with thermal models and measured temperatures mutually verifying and redundant, significantly improving system reliability. The multi-threshold graded protection strategy provides users with sufficient warning and buffer time when approaching limits, allowing them to proactively take measures rather than passively accepting sudden shutdowns. Effect 7: Synergistic effect of high-precision sampling technology and temperature closed-loop control. In existing technologies, high-precision sampling and thermal protection are often separate, while this application organically integrates high-precision sampling and temperature closed-loop protection to form a closed-loop control system. High-precision sampling makes thermal model estimation more accurate, and a more accurate thermal model allows the threshold setting of temperature closed-loop control to be closer to the physical limit. The presence of temperature sensors provides a safety guarantee for this limit control. This synergistic effect cannot be achieved by a single technical means. Effect 8: By actively sacrificing performance, overload time is further extended, achieving "on-demand allocation" of motor resources. In existing technologies, overload protection is singular and passive, while this application introduces a dynamic performance adjustment method. When the user needs to extend the overload time, some non-core performance parameters (such as speed accuracy and dynamic response) are actively sacrificed based on actual adjustment data in exchange for longer overload time and lower heat generation. For example, in the balancing operation of an oil pump, what the user needs most is a large torque for several minutes to adjust the crank position, at which point speed accuracy is irrelevant. This application proactively reduces speed to decrease motor heat generation, automatically lowers the speed loop gain, allocates more current resources to torque output, and reduces regulation losses, extending overload time from the original 2 minutes to 5 minutes. Taking elevator emergency rescue as an example: when people are trapped in an elevator, continuous torque output is needed to overcome unbalanced loads. This application proactively reduces dynamic response, allowing the motor to operate for a longer period with lower heat generation, ensuring the rescue is completed. Actual test data shows that under double overload conditions, by proactively sacrificing speed accuracy (allowing ±2% fluctuation), the allowable overload time can be extended by 30-50% compared to the original dynamic control, and the motor temperature rise rate is reduced by 15-20%. Effect Nine: By proactively reducing speed, "trading speed for time" is achieved, further extending overload capacity. Motor heat generation mainly comes from copper losses (I²R) generated by current. With a constant load torque T, the output power P = T × ω is proportional to the speed ω. Proactively reducing the speed ω reduces the output power P, thereby reducing the required current I and directly reducing heat generation.Assume a permanent magnet synchronous motor has a rated speed of 1500 rpm, a rated torque of 100 Nm, and a rated power of 15 kW. Under special operating conditions, it needs to output twice the torque (200 Nm) for 5 minutes. If the rated speed is maintained at 1500 rpm, the output power P = 200 Nm × 157 rad / s ≈ 31.4 kW, the current is approximately 2.1 times the rated current, and the heat dissipation power I²R is approximately 4.4 times the rated heat dissipation power. If the speed is allowed to decrease to 800 rpm (ω = 83.7 rad / s), the output power P = 200 Nm × 83.7 rad / s ≈ 16.7 kW, only slightly higher than the rated power, the current is approximately 1.2 times the rated current, and the heat dissipation power I²R is only 1.44 times the rated heat dissipation power. This application reduces the heat dissipation power from 4.4 times to 1.44 times through active speed reduction, a reduction of 67%. Correspondingly, the allowable overload time can be extended from several minutes to tens of minutes, or even longer. In many special operating conditions (such as pumping unit stuck in well, elevator emergency rescue, and crane suspending heavy loads), the user's core requirement is to maintain torque output (such as overcoming loads and maintaining suspension), while there are no requirements for operating speed or even zero speed is permissible. In this case, the active speed reduction strategy can significantly extend the equipment's continuous operating time without sacrificing core functions, buying valuable time for fault handling and personnel rescue.

[0113] The technical solution provided in this application is applicable to various types of motors, including but not limited to AC asynchronous motors, permanent magnet synchronous motors, electrically excited synchronous motors, synchronous reluctance motors, switched reluctance motors, DC brushed motors, DC brushless motors, and stepper motors, and can be applied to various fields such as industrial automation, new energy, and transportation. Many industrial motor applications face a contradiction between "short-term overload requirements" and "long-term economical operation." This application can directly reduce the motor selection specifications, saving 20%-50% of installation costs, while simultaneously reducing operating energy consumption by 5%-15%. Taking replacing a 37kW motor with a 22kW motor as an example, the cost savings per motor are approximately 2000-3000 yuan, the cost savings per frequency converter are approximately 1000-2000 yuan, and the annual electricity cost savings are approximately 3000-5000 yuan.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed device can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of the device according to various embodiments of this application. In this regard, each block in the block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram, and combinations of block diagrams, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0115] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0116] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0118] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A motor overload control system based on high-precision sampling, characterized in that, The system includes: a high-precision sampling module and a control module; The control module is connected to the high-precision sampling module; The high-precision sampling module is used to sample the motor based on the oversampling rate to obtain sampled data; The control module is used to determine the thermal state data of the motor based on the sampled data, and to generate control commands for dynamic overload control of the motor based on the thermal state data and overload requirements.

2. The system according to claim 1, characterized in that, The high-precision sampling module includes a Σ-Δ modulator.

3. The system according to claim 2, characterized in that, in, The oversampling rate OSR of the Σ-Δ modulator is ≥64, and the effective number of bits ENOB is ≥12.

4. The system according to claim 1, characterized in that, The high-precision sampling module includes: a high-resolution successive approximation register-type analog-to-digital converter and a low-noise preamplifier; wherein the high-resolution successive approximation register-type analog-to-digital converter has a resolution ≥16 and a sampling rate ≥1Msps.

5. The system according to claim 1, characterized in that, in, The control module controls the motor to work based on a closed-loop control mode. In the closed-loop control mode, the starting frequency of the motor is ≤0.5Hz.

6. The system according to any one of claims 1-5, characterized in that, The control module includes: a filter and an overload controller; The filter is used to filter and downsample the received sampled data to obtain digital data; The overload controller is used to determine the effective current data of the motor based on the digital data; and to process the effective current data and / or the digital data through a preset thermal model to determine the thermal state data of the motor; wherein, the thermal state data includes: the cumulative heat generation and / or equivalent temperature of the motor.

7. The system according to claim 6, characterized in that, The overload controller is further configured to: determine the thermal capacity of the motor based on the thermal state data of the motor; and determine the corresponding overload time based on the thermal capacity and the overload requirement. Based on the overload time, a control command is generated to dynamically control the overload of the motor.

8. The system according to claim 7, characterized in that, The overload controller is further configured to: if it is determined that the heat capacity is less than or equal to a preset threshold, then, based on a preset smooth load reduction strategy, smoothly reduce the current limit value of the motor.

9. The system according to claim 6, characterized in that, A temperature sensor is installed on the motor; The overload controller is further configured to: acquire the real-time temperature of the motor detected by the temperature sensor; calibrate the thermal model based on the real-time temperature; and determine the current control temperature based on the real-time temperature and the equivalent temperature. Based on the current control temperature and multiple preset temperature ranges, a current temperature range is determined; each temperature range is configured with a corresponding protection strategy; based on the current temperature range, a target protection strategy is determined among the multiple protection strategies; dynamic overload protection is performed on the motor based on the target protection strategy.

10. The system according to claim 6, characterized in that, The overload controller is further configured to: acquire adjustment data input by the user based on overload conditions; and adjust the performance parameters of the motor based on the adjustment data and the thermal state data according to a preset adjustment strategy; wherein the performance parameters include at least one of the following: motor speed, speed control accuracy, response bandwidth, field weakening current component, and control mode.

11. A motor overload control method based on high-precision sampling, characterized in that, The method is applied to the motor overload control system according to any one of claims 1-10, and the method includes: The high-precision sampling module samples the motor based on the oversampling rate to obtain sampled data; The control module determines the thermal state data of the motor based on the sampled data, and generates control commands for dynamic overload control of the motor based on the thermal state data and overload requirements.

12. The method according to claim 11, characterized in that, The control module includes: a filter and an overload controller; The step of determining the thermal state data of the motor based on the sampled data through the control module includes: The received sampled data is filtered and downsampled using the filter to obtain digital data. The overload controller determines the effective current data of the motor based on the digital data; the effective current data and / or the digital data are processed by a preset thermal model to determine the thermal state data of the motor; wherein, the thermal state data includes: the cumulative heat generation and / or equivalent temperature of the motor.

13. The method according to claim 12, characterized in that, The control module generates control commands for dynamic overload control of the motor based on the thermal state data and overload requirements, including: The overload controller determines the thermal capacity of the motor based on the motor's thermal state data. The overload controller determines the corresponding overload time based on the heat capacity and the overload requirement. The overload controller generates control commands for dynamic overload control of the motor based on the overload time.

14. The method according to claim 13, characterized in that, The method further includes: If the overload controller determines that the heat capacity is less than or equal to a preset threshold, it will smoothly reduce the current limit of the motor based on a preset smooth load reduction strategy.

15. The method according to claim 12, characterized in that, A temperature sensor is installed on the motor; The method further includes: The overload controller obtains the real-time temperature of the motor detected by the temperature sensor. The thermal model is calibrated based on the real-time temperature using the overload controller. The overload controller determines the current control temperature based on the real-time temperature and the equivalent temperature. The overload controller determines the current temperature range based on the current control temperature and multiple preset temperature ranges; each temperature range is configured with a corresponding protection strategy. Based on the current temperature range, the overload controller determines a target protection strategy from among multiple protection strategies. The overload controller performs dynamic overload protection on the motor based on the target protection strategy.

16. The method according to claim 12, characterized in that, The method further includes: The overload controller acquires adjustment data input by the user based on overload conditions. The overload controller adjusts the motor's performance parameters based on the preset adjustment strategy, the adjustment data, and the thermal state data, according to a preset adjustment strategy. The performance parameters include at least one of the following: motor speed, speed control accuracy, response bandwidth, field weakening current component, and control mode.

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