A temperature monitoring method and system for a motor controller

Abstract

This application relates to the field of data processing technology and discloses a temperature monitoring method and system for a motor controller. The method includes: using a temperature sensor array to acquire a three-dimensional temperature data stream in real time, performing temperature analysis and hotspot detection, constructing an adaptive protection strategy, and dynamically adjusting PWM parameters to optimize motor operating safety and performance. This application addresses the technical issues of incomplete temperature monitoring point coverage and a lack of targeted and adaptive protection strategies. By integrating multi-point temperature acquisition and intelligent analysis and processing, the application improves the accuracy of motor controller temperature monitoring and the flexibility of protection strategies, ensuring that the motor is both safe and reliable while maintaining optimal performance under various operating conditions.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a temperature monitoring method and system for a motor controller. Background Art

[0002] In motor control system applications, temperature monitoring and overtemperature protection have always been key technologies for ensuring safe and reliable system operation. Traditional motor controller temperature protection solutions typically employ single-point temperature sensing. This involves placing one or a few temperature sensors in key areas of the controller, triggering overtemperature protection based on fixed thresholds. This approach is widely used in automotive motor controllers, such as water pump controllers and fan controllers in automotive thermal management systems. These systems typically employ a simple two-stage protection mechanism: output power is reduced when the temperature exceeds a warning threshold, and a safety shutdown is initiated when the temperature exceeds a critical threshold. With the rapid development of electric and hybrid vehicles, motor controllers face increasingly complex and variable operating environments and load conditions. Consequently, temperature monitoring technology has evolved into a complex system based on multi-point temperature sensing and dynamic threshold adjustment.

[0003] However, existing technical solutions still have significant shortcomings. First, single-point or a small number of multiple-point temperature monitoring cannot fully reflect the temperature distribution within the motor controller, and hidden hotspots are easily missed. Second, fixed-threshold protection strategies lack specificity for temperature characteristics under different operating conditions, often leading to over-protection or under-protection. Third, existing solutions often fail to consider the differences in temperature characteristics during different motor operating stages (such as startup, acceleration, steady state operation, and braking), resulting in a lack of refinement and differentiation in protection strategies. Finally, existing technologies fail to fully utilize historical temperature data for self-learning optimization, resulting in a lack of adaptability in protection strategies, making it impossible to dynamically adjust protection parameters based on seasonal changes, environmental conditions, and other factors. These shortcomings result in motor controllers being either overly conservative, impacting performance, or overly aggressive, threatening safety. Summary of the Invention

[0004] The present application provides a temperature monitoring method and system for a motor controller, which is used to solve the technical problems of incomplete coverage of temperature monitoring points and lack of specificity and adaptability of protection strategies. Through multi-point temperature acquisition and intelligent analysis and processing, the accuracy of motor controller temperature monitoring and the flexibility of protection strategies are improved, ensuring that the motor is safe and reliable and maintains optimal performance under different working conditions.

[0005] In the first aspect, the present application provides a temperature monitoring method for a motor controller, which comprises: performing multi-point temperature acquisition on the power MOSFET area, driver IC area and PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream containing a timestamp, spatial position and temperature value; filtering, denoising and outlier marking the data according to the three-dimensional temperature data stream to obtain a standardized temperature matrix and reliability mark; based on the standardized temperature matrix and reliability mark, analyzing the temperature distribution during motor startup, acceleration, stable operation and braking, and obtaining a temperature distribution related to the motor operation status. The method uses a two-dimensional temperature distribution field matrix associated with the state; based on the two-dimensional temperature distribution field matrix, the temperature peak points under different load conditions are detected and correlated, and a hot spot feature vector set containing the correlation between position, motor speed and load correlation is obtained; based on the hot spot feature vector set, combined with the power device temperature specifications and the current operating parameters of the motor, the adaptive protection thresholds corresponding to different operating stages are determined, and a protection execution instruction set containing hierarchical trigger conditions and corresponding control strategies is generated; based on the protection execution instruction set, the motor PWM duty cycle and switching frequency are controlled in real time, and the protection parameters are optimized and adjusted based on historical data on the relationship between operating conditions and temperature rise.

[0006] In a second aspect, the present application provides a temperature monitoring system for a motor controller, the temperature monitoring system for a motor controller comprising:

[0007] The acquisition module is used to collect multi-point temperatures of the power MOSFET area, driver IC area, and PWM control module area of the motor controller through a temperature sensor array, and obtain a three-dimensional temperature data stream containing timestamps, spatial positions, and temperature values;

[0008] a processing module, configured to perform filtering, noise reduction, and outlier marking on the three-dimensional temperature data stream to obtain a standardized temperature matrix and a reliability mark;

[0009] An analysis module is configured to analyze the temperature distribution during motor startup, acceleration, stable operation, and braking based on the standardized temperature matrix and the reliability mark, and obtain a two-dimensional temperature distribution field matrix associated with the motor operation state;

[0010] A correlation module is used to detect and perform correlation analysis on the temperature peak points under different load conditions based on the two-dimensional temperature distribution field matrix to obtain a hotspot feature vector set including the correlation between position, motor speed and load correlation;

[0011] A generation module is used to determine adaptive protection thresholds corresponding to different operating stages based on the hotspot feature vector set, combined with the power device temperature specifications and the current operating parameters of the motor, and generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies;

[0012] The control module is used to control the motor PWM duty cycle and switching frequency in real time according to the protection execution instruction set, and optimize and adjust the protection parameters through historical data on the relationship between operating conditions and temperature rise.

[0013] A third aspect of the present invention provides a computer device comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned temperature monitoring method for the motor controller.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the above-mentioned temperature monitoring method for a motor controller.

[0015] In the technical solution provided by the present application, a temperature sensor array is arranged in the power MOSFET area, the driver IC area and the PWM control module area to collect multi-point temperature, and a three-dimensional temperature data stream containing timestamps, spatial positions and temperature values is constructed, which effectively solves the problem of missed hotspot detection caused by traditional single-point temperature measurement, making temperature monitoring more comprehensive and accurate; filtering, noise reduction and outlier marking processing are performed based on the three-dimensional temperature data stream to obtain a standardized temperature matrix and reliability mark, which significantly improves the accuracy and reliability of temperature data and effectively identifies and marks sensor faults and temperature anomalies; targeted analysis is performed on the temperature distribution in different operating stages such as motor start-up, acceleration, stable operation and braking, and a temperature matrix associated with the motor operating status is generated. The two-dimensional temperature distribution field matrix is used to achieve a refined characterization of the temperature characteristics in different operating stages; the temperature peak points under different load conditions are detected and correlated, forming a hotspot feature vector set containing the correlation between position, motor speed and load, providing rich data support for the subsequent formulation of protection strategies; combining the power device temperature specifications and the current operating parameters of the motor, adaptive protection thresholds are determined for different operating stages, and hierarchical trigger conditions and protection execution instruction sets of corresponding control strategies are generated, achieving precise and differentiated protection strategies; through real-time regulation of the motor PWM duty cycle and switching frequency, and optimizing protection parameters based on historical data on the relationship between operating conditions and temperature rise, a closed-loop adaptive temperature protection mechanism is constructed. The present invention applies a variety of artificial intelligence algorithms in specific functions or application fields, such as the Z-score standardization method for outlier identification, the thermal diffusion model for temperature field reconstruction, and the correlation calculation method for hotspot association analysis. These algorithm features have made important contributions to the solution and achieved a technological leap from simple threshold triggering to intelligent analysis and decision-making. The application of artificial intelligence algorithms enables the motor controller to implement differentiated protection strategies according to the temperature characteristics of different working conditions and different operating stages, just like an experienced operator, which not only ensures system safety but also maximizes the performance of the motor. This intelligent feature is incomparable to traditional temperature monitoring methods. By combining artificial intelligence analysis with real-time control technology, the present invention effectively solves key technical problems such as motor controllers being prone to overheating, inaccurate protection strategies, and incomplete temperature monitoring. It is particularly suitable for application scenarios such as new energy vehicle thermal management systems that have high requirements for control accuracy and reliability. Compared with the existing technology, the present invention realizes an upgrade from passive temperature protection to active intelligent temperature management, greatly improving the thermal safety, reliability and performance of the motor controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a temperature monitoring method for a motor controller in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an embodiment of a temperature monitoring system for a motor controller in an embodiment of the present application;

[0019] Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide a temperature monitoring method and system for a motor controller. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for monitoring the temperature of a motor controller includes:

[0022] Step S101: Perform multi-point temperature acquisition on the power MOSFET area, driver IC area, and PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream containing timestamps, spatial positions, and temperature values;

[0023] Step S102: Filter and de-noise the data and mark outliers based on the three-dimensional temperature data stream to obtain a standardized temperature matrix and reliability marks;

[0024] Step S103: Analyze the temperature distribution during motor startup, acceleration, stable operation, and braking based on the standardized temperature matrix and the reliability mark to obtain a two-dimensional temperature distribution field matrix associated with the motor operating state;

[0025] Step S104: Detect and perform correlation analysis on the temperature peak points under different load conditions based on the two-dimensional temperature distribution field matrix to obtain a hotspot feature vector set including the correlation between position, motor speed, and load correlation;

[0026] Step S105: Determine adaptive protection thresholds corresponding to different operating stages based on the hotspot feature vector set, combined with power device temperature specifications and current motor operating parameters, and generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies;

[0027] Step S106: According to the protection execution instruction set, the motor PWM duty cycle and switching frequency are controlled in real time, and the protection parameters are optimized and adjusted based on the historical data of the relationship between the operating conditions and the temperature rise.

[0028] It is understandable that the execution subject of the present application can be a temperature monitoring system for a motor controller, or a terminal or a server, which is not limited here. The embodiment of the present application is described by taking a server as the execution subject as an example.

[0029] Specifically, the approach begins with multi-point temperature acquisition. High-precision NTC thermistor sensors are placed in the motor controller's power MOSFET area, digital temperature sensors are placed in the driver IC area, and temperature monitoring points are placed in the PWM control module area, forming a temperature sensor array. These sensors dynamically adjust their sampling frequency based on the motor's operating status, using a 1Hz sampling rate during normal operation and increasing to 10Hz during high load or startup. Data is aggregated and timestamped via a single bus or I²C bus. Each temperature data point is assigned a spatial location identifier, ultimately constructing a three-dimensional temperature data stream containing timestamps, spatial locations, and temperature values. After acquiring the three-dimensional temperature data stream, filtering and noise reduction and outlier identification are performed. First, a sliding window median filter is used to remove high-frequency noise. A 7-point window is used to process the sample sequence to effectively remove sudden interference. An exponentially weighted moving average (EWMA) is then applied to the initially filtered temperature data, with a weight coefficient of 0.2 to give higher weight to new data while preserving historical trends. The temperature series is then reorganized into a two-dimensional temperature data table based on the sensor's spatial distribution. The Z-score is calculated for each point, and points exceeding 3.0 are marked as potential outliers. By analyzing the correlation between potential outliers and spatially adjacent sensor data, we can distinguish between sensor failures and true temperature anomalies. For example, if the temperature at a point suddenly rises by 50°C while the surrounding sensors only rise by 2°C, this is considered a sensor failure. Finally, the stabilized temperature series is normalized and combined with sensor reliability weights to form a standardized temperature matrix and reliability signature.

[0030] To analyze temperature distribution under different operating conditions, the system obtains operating status signals from the motor controller and divides the motor's operating process into startup, acceleration, steady-state, and braking phases. The standardized temperature matrix is then grouped sequentially by operating phase. Reliability tags are assigned to each data point, and the weighted stage temperature data is mapped to the motor controller's physical layout. Non-measurement points are interpolated using heat conduction, ultimately generating a two-dimensional temperature distribution field matrix associated with the motor's operating state.

[0031] For hotspot analysis under different load conditions, the system extracts temperature distribution submatrices for low-load, medium-load, and high-load conditions from the two-dimensional temperature distribution field matrix. Local extreme value detection is performed on the temperature distribution under low-load conditions, temperature peak points are identified under medium-load conditions, and hotspot screening is performed under high-load conditions. The low-load hotspot temperature gradient index, medium-load hotspot spatial distribution characteristics, and high-load thermal mass index are calculated. The correlation between these hotspot locations and motor speed data is calculated. For example, if the temperature of a hotspot increases by 15°C when the motor speed increases from 1000 rpm to 2000 rpm, then the hotspot has a high correlation with the speed. Finally, the various indicators are integrated to form a hotspot feature vector set.

[0032] When determining the protection threshold, the maximum junction temperature specification value of the power MOSFET (usually 175°C) and the maximum operating temperature specification value of the driver IC (usually 150°C) are obtained as the basic protection upper limit. The weights are adjusted according to the hotspot location information, and a speed-temperature rise relationship matrix is constructed. Combined with the load correlation, a five-level progressive protection threshold is generated, corresponding to the set temperature warning trigger conditions, PWM frequency reduction trigger conditions, power limit trigger conditions, forced frequency reduction trigger conditions and safety shutdown trigger conditions.

[0033] Finally, when implementing protection control, the system regulates the motor PWM according to the trigger signals in the protection execution instruction set. When a temperature warning is triggered, the sampling rate is increased to high speed. When a PWM frequency reduction is triggered, the switching frequency is reduced to 80% of the original frequency according to the slope limit function (maximum change of 10% within 100ms). When a power limit is triggered, the duty cycle limit is lowered to 85%. When a forced frequency reduction is triggered, the power is further limited to 70%. The system records the data of each temperature protection action in the temperature event log, constructs a characteristic map of the operating condition and temperature rise, and periodically optimizes the protection threshold.

[0034] For example, a brushless automotive fan motor controller measured a temperature of 125°C and rapidly rising in the power MOSFET region. The system immediately and smoothly reduced the PWM switching frequency from 20kHz to 16kHz, slowing the temperature rise. Subsequently, when the temperature reached 140°C, the system limited the PWM duty cycle from 90% to 76.5%, and the temperature began to drop. Analysis of historical data revealed that this controller was more prone to overheating in summer when the ambient temperature exceeded 35°C. The system automatically lowered the summer protection threshold, providing early intervention and effectively preventing damage to the motor controller due to overheating.

[0035] In an embodiment of the present application, a temperature sensor array is arranged in the power MOSFET area, the driver IC area and the PWM control module area to collect multi-point temperature, and a three-dimensional temperature data stream containing timestamps, spatial positions and temperature values is constructed, which effectively solves the problem of missed hotspot detection caused by traditional single-point temperature measurement, making temperature monitoring more comprehensive and accurate; filtering, noise reduction and outlier marking processing are performed based on the three-dimensional temperature data stream to obtain a standardized temperature matrix and reliability mark, which significantly improves the accuracy and reliability of temperature data and effectively identifies and marks sensor faults and temperature anomalies; targeted analysis is performed on the temperature distribution in different operating stages such as motor start-up, acceleration, stable operation and braking, and a two-dimensional temperature matrix associated with the motor operating status is generated. The three-dimensional temperature distribution field matrix realizes the refined characterization of temperature characteristics in different operating stages; the temperature peak points under different load conditions are detected and correlated, forming a hotspot feature vector set containing position, motor speed correlation and load correlation, providing rich data support for the subsequent formulation of protection strategies; combining the power device temperature specifications and the current operating parameters of the motor, the adaptive protection threshold is determined for different operating stages, and the hierarchical trigger conditions and protection execution instruction sets of the corresponding control strategies are generated, realizing the precision and differentiation of the protection strategy; through real-time regulation of the motor PWM duty cycle and switching frequency, and optimizing the protection parameters based on historical data on the relationship between operating conditions and temperature rise, a closed-loop adaptive temperature protection mechanism is constructed. The present invention applies a variety of artificial intelligence algorithms in specific functions or application fields, such as the Z-score standardization method for outlier identification, the thermal diffusion model for temperature field reconstruction, and the correlation calculation method for hotspot association analysis. These algorithm features have made important contributions to the solution and achieved a technological leap from simple threshold triggering to intelligent analysis and decision-making. The application of artificial intelligence algorithms enables the motor controller to implement differentiated protection strategies according to the temperature characteristics of different working conditions and different operating stages, just like an experienced operator, which not only ensures system safety but also maximizes the performance of the motor. This intelligent feature is incomparable to traditional temperature monitoring methods. By combining artificial intelligence analysis with real-time control technology, the present invention effectively solves key technical problems such as motor controllers being prone to overheating, inaccurate protection strategies, and incomplete temperature monitoring. It is particularly suitable for application scenarios such as new energy vehicle thermal management systems that have high requirements for control accuracy and reliability. Compared with the existing technology, the present invention realizes an upgrade from passive temperature protection to active intelligent temperature management, greatly improving the thermal safety, reliability and performance of the motor controller.

[0036] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0037] (1) Arrange NTC thermistor sensors in the power MOSFET area of the motor controller to collect the surrounding temperature of the power device with high precision;

[0038] (2) Place a digital temperature sensor in the driver IC area of the motor controller to accurately measure the temperature of the control circuit;

[0039] (3) Arrange temperature monitoring points in the PWM control module area to track the temperature of the signal processing area in real time;

[0040] (4) Dynamically adjust the temperature sampling frequency according to the motor operating status, sampling at 1 Hz when the motor is operating normally and at 10 Hz during high load or startup phase;

[0041] (5) Summarize the collected temperature data and add timestamp information via a single bus or I²C bus;

[0042] (6) A spatial location identifier is attached to each temperature data point to construct a three-dimensional temperature data stream containing timestamp, spatial location and temperature value.

[0043] Specifically, when placing an NTC thermistor sensor in the power MOSFET area, select an NTC thermistor element with an accuracy of up to ±0.5°C and attach it directly to the back of the MOSFET device heat sink or the PCB copper foil area next to the power tube. Use thermally conductive silicone to ensure good heat conduction. The resistance value of the NTC thermistor decreases as the temperature increases, showing a nonlinear characteristic. Based on this, the resistance-temperature correspondence formula is constructed:

[0044]

[0045] in, is the resistance value at temperature T, is the nominal resistance value at 25°C, is the material constant, is the temperature value. By measuring the resistance value and substituting it into the formula, the accurate temperature value can be calculated.

[0046] When placing digital temperature sensors in the driver IC area, use integrated temperature sensors such as the DS18B20 or TMP102, which offer an accuracy of ±0.25°C. These digital temperature sensors have built-in temperature-to-digital conversion circuitry, directly outputting digital temperature values and avoiding interference during analog signal transmission. Driver ICs typically have a narrow operating temperature range, requiring precise monitoring. Digital temperature sensors are placed on the driver IC surface or on the PCB, no more than 2mm from the copper foil underneath the IC, ensuring accurate measurement.

[0047] Temperature monitoring points in the PWM control module area utilize thermocouples or thermistors combined with operational amplifiers, placed around the signal processing chip and along key signal traces. These temperature-sensitive areas are tracked in real time. Temperature changes in these areas are typically slow, but they can have a significant impact on system stability. A low-noise preamplifier circuit is used to preprocess the temperature signal and improve the signal-to-noise ratio.

[0048] Dynamic sampling frequency adjustment of the motor's operating status is based on the motor's status signal. The current motor operating status is determined by monitoring the motor's phase current, speed signal, and control instructions. When the motor is detected to be in a normal and stable operating state, the temperature change rate is low, and the sampling frequency is set to 1Hz, meaning all sensor data is collected once per second. When the motor is detected to be in a high-load state (phase current exceeds 80% of the rated value) or in the startup phase (the process from the moment of startup to reaching a stable speed), the temperature change rate increases significantly, and the sampling frequency is automatically increased to 10Hz to capture rapidly changing temperature information. Sampling frequency control is implemented through hardware timers and software counters. The motor controller's main processor dynamically adjusts the timer overflow time based on the operating status parameters.

[0049] Temperature data is aggregated via a single bus or I²C bus. Analog sensors such as NTC thermistors are first converted to digital signals using an ADC; digital temperature sensors directly output digital values. Single bus technology allows multiple devices to share a single data line, identifying them through timing, making it suitable for distributed sensors. The I²C bus communicates via the SCL clock line and the SDA data line, supporting multiple master and multiple slave devices and suitable for scenarios with high system integration. Aggregated temperature data is timestamped with millisecond-level information to accurately record the sampling moment. This timestamp is generated by the controller's internal real-time clock and is formatted as a 32-bit unsigned integer, representing the number of milliseconds since system startup.

[0050] To attach a spatial location identifier to each temperature data point, a two-dimensional coordinate system is established for the motor controller PCB, with the lower left corner as the origin (0,0), the X axis pointing rightward, and the Y axis pointing upward, in millimeters. The physical location of each sensor is uniquely identified by its (x,y) coordinates. For example, a power MOSFET is located at (15.3, 42.8) and a driver IC is located at (28.6, 35.2). Combining the timestamp t, the spatial coordinates (x,y), and the temperature value T, a four-tuple of the form (t, x, y, T) is constructed, forming a three-dimensional temperature data stream. Three-dimensional means that the data contains information in three dimensions: time, spatial X, and spatial Y.

[0051] For example, a brushless motor controller used in an automotive thermal management system consists of six power transistors in the power MOSFET area, each with an NTC sensor. Two digital temperature sensors are located in the driver IC area, and three temperature monitoring points are located in the PWM control module area. When the motor starts from a standstill, the sampling frequency immediately increases from 1Hz to 10Hz, recording the rapid temperature rise in the power zone during startup. The temperature data stream fragment at a certain moment is: (5328, 15.3, 42.8, 78.6), (5328, 22.4, 43.1, 76.2), (5328, 28.6, 35.2, 62.4), indicating that 5328 milliseconds after the system is started, the temperature of the power MOSFET at (15.3, 42.8) reaches 78.6°C, the temperature of the other power transistor at (22.4, 43.1) is 76.2°C, and the temperature of the driver IC at (28.6, 35.2) is 62.4°C.

[0052] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0053] (1) High-frequency noise is eliminated from the temperature data of each sampling point in the three-dimensional temperature data stream by sliding window median filtering to generate preliminary filtered temperature data;

[0054] (2) Applying exponentially weighted moving average processing to the preliminary filtered temperature data to form a stabilized temperature series;

[0055] (3) Reorganize the stabilized temperature sequence into a two-dimensional temperature data table according to the spatial distribution of the sensors, and calculate the Z-score value of each point based on the historical temperature mean;

[0056] (4) Screen the temperature mutation points according to the Z-score value, and mark them as potential abnormal points when the Z-score value exceeds the preset threshold;

[0057] (5) By analyzing the correlation between potential abnormal points and spatially adjacent sensor data, sensor failures are distinguished from real temperature anomalies, and sensor reliability weights are generated;

[0058] (6) The stabilized temperature sequence is standardized and combined with the sensor reliability weight to form a standardized temperature matrix and reliability mark.

[0059] Specifically, the processing of the three-dimensional temperature data stream first uses a sliding window median filter algorithm to eliminate high-frequency noise from the raw temperature data. Sliding window median filtering is a nonlinear signal processing technique. For each temperature sampling point, a window is formed by taking a total of z points (z is usually an odd number, such as 5, 7, or 9) before and after the sampling point. These data are sorted by size and the median value is taken as the filtered output for the current point. For example, when the window size is 7, for each temperature point, seven consecutive points in the time series are taken, sorted, and the fourth value is taken as the initial filtered temperature value. Median filtering effectively suppresses pulse interference and random noise while preserving edge information in the temperature data, avoiding the oversmoothing caused by mean filtering.

[0060] When the preliminary filtered temperature data is further processed, an exponentially weighted moving average (EWMA) algorithm is used to form a stabilized temperature series. EWMA processing gives more weight to recent data while retaining the influence of historical data. In motor controller temperature monitoring, a smoothing factor of 0.2-0.3 is typically used to quickly respond to actual temperature changes while suppressing residual noise. For the first data point, its preliminary filtered value is directly used as the EWMA starting value. This processing method is particularly suitable for motor controller temperature monitoring, because motor temperature has both a slowly changing underlying trend and rapid fluctuations caused by load changes.

[0061] The stabilized temperature series needs to be reorganized into a two-dimensional temperature data table based on the spatial distribution of the sensors to facilitate subsequent spatial correlation analysis. During the reorganization process, each sensor location is represented as a row in the table, the time point as a column, and the corresponding EWMA temperature value as the value. This two-dimensional table intuitively displays the temperature trend of each spatial point over time. When calculating the Z-score value based on the historical temperature mean, the temperature mean and standard deviation within a time window (for example, 1 minute) are taken for each sensor location, and then the Z-score of the current temperature is calculated. The Z-score represents the number of standard deviations of the current temperature from the historical mean and is a quantitative indicator of the degree of temperature anomaly.

[0062] Screening temperature mutation points based on the Z-score value is a key step in anomaly detection. When the absolute Z-score value of a point is greater than 3.0 (i.e., more than 3 standard deviations from the mean), the point is marked as a potential anomaly. The threshold of 3.0 is based on normal distribution theory. At this threshold, the probability of normal data occurring is less than 0.3%. Therefore, points exceeding this threshold have a high probability of being anomalies. The marking process generates a binary anomaly flag matrix, where a value of 1 indicates a potential anomaly and a value of 0 indicates normal data.

[0063] For marked potential anomalies, further differentiation between sensor failure and true temperature anomalies is required. This differentiation is achieved by analyzing the correlation between the potential anomaly and the data from spatially adjacent sensors. The Pearson correlation coefficient is calculated between the anomaly and its spatially adjacent points (e.g., points within a distance of less than 5 mm). The correlation coefficient is based on the recent temperature trends of these points. If the correlation coefficient is greater than 0.7, the temperature trends are consistent, and the anomaly is a true temperature anomaly. If the correlation coefficient is less than 0.3, the temperature trends are inconsistent, and the anomaly is likely a sensor failure. Based on the analysis results, a reliability weight is assigned to each sensor location: a weight of 1.0 when the sensor is functioning properly and a weight between 0 and 0.5 when a fault is suspected. The weight varies linearly with the correlation coefficient.

[0064] The stabilized temperature series is normalized and converted into standardized temperature values. Normalization maps the temperature values to a range of 0-1, facilitating comparison and subsequent processing across different temperature scales. The standardized temperature values are combined with the corresponding sensor reliability weights to form a standardized temperature matrix and reliability markers, which serve as the basis for subsequent temperature field reconstruction and hotspot analysis.

[0065] Take, for example, a practical example from an automotive motor controller: During operation, the temperature data at a point in the power MOSFET region experienced sudden interference. The original values were [70.2, 71.5, 69.8, 85.4, 72.3, 70.1, 71.8]°C. After filtering with a 7-point sliding window median filter, the median value was 71.5°C, effectively filtering out the abnormal value of 85.4°C. The filtered values at this point were processed using the EWMA algorithm over multiple consecutive moments, resulting in a stabilized temperature value of 72.3°C. The historical mean of this point was 68.9°C, with a standard deviation of 1.2°C, resulting in a Z-score of 2.83, close to but not exceeding the abnormal threshold of 3.0. Furthermore, the correlation coefficient between this point and the surrounding sensors was 0.92, indicating a highly consistent temperature trend and a sensor reliability weight of 1.0. After normalization, the temperature value at this point was 0.76 (based on a system minimum temperature of 25°C and a maximum temperature of 100°C).

[0066] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0067] (1) Obtain the motor operating status signal from the motor controller and divide the motor operating process into the starting phase, acceleration phase, stable operation phase and braking phase;

[0068] (2) The standardized temperature matrix is grouped in time sequence according to the motor operation stage to form a stage temperature data set;

[0069] (3) Assign trust weights to each data point in the stage temperature dataset based on the reliability label to generate weighted stage temperature data;

[0070] (4) Map the weighted stage temperature data to the physical layout of the motor controller according to the spatial position to construct the temperature spatial distribution map;

[0071] (5) Perform heat conduction interpolation on non-measurement point locations in the temperature spatial distribution map to fill in the temperature blank areas;

[0072] (6) The temperature spatial distribution map is temporally correlated with the motor operating status signal to generate a two-dimensional temperature distribution field matrix associated with the motor operating status.

[0073] Specifically, operating status signals are obtained from the motor controller. The motor controller outputs operating parameters, including phase current values, motor speed, control commands, and load feedback, to the temperature monitoring system via the CAN bus or a dedicated interface. Based on these parameters, the motor operation process is divided into four typical phases: the startup phase is characterized by a sudden increase in phase current and a rapid increase in speed from zero; the acceleration phase is characterized by high current values but below the startup peak and a continuous increase in speed; the stable operation phase is characterized by minimal fluctuations in current and speed within a set range; and the braking phase is characterized by a rapid decrease in speed, possibly accompanied by energy feedback or reverse current. These four phases are specifically based on the motor speed change rate and phase current values. For example, the startup phase is determined when the speed starts from zero and the current exceeds 150% of the rated value; the acceleration phase is determined when the speed change rate exceeds 200 rpm / s and the current exceeds 120% of the rated value; the stable operation phase is determined when the speed change rate is less than 50 rpm / s and the current is within the range of 80%-110% of the rated value; and the braking phase is determined when the speed decrease rate exceeds 300 rpm / s.

[0074] When the standardized temperature matrix is grouped in time series by the motor's operating stage, the temperature data is associated with the operating stage identifier through the timestamp. The specific operation is to establish a two-level index structure, with the first level being the operating stage type and the second level being the timestamp, and the corresponding value is the standardized temperature matrix at that moment. This grouping method facilitates the analysis of the temperature distribution characteristics and evolution patterns of different operating stages. After the grouping is completed, temperature data sets for four typical stages are formed: the startup stage temperature data set, the acceleration stage temperature data set, the stable operation stage temperature data set, and the braking stage temperature data set. Each data set contains the temperature distribution data for all sampling moments within that stage, retaining the timestamp information, so that subsequent analysis can focus on both spatial distribution characteristics and track temporal evolution trends.

[0075] When assigning trust weights to each data point in the stage temperature dataset based on the reliability tag, the sensor reliability weights generated in the previous step are directly applied to the corresponding temperature data points. This weighted processing reduces the impact of unreliable sensor data and improves the accuracy of temperature analysis. The resulting temperature data retains the original temporal and spatial index structure, but the values are more reliable and better reflect the actual temperature status of the motor controller.

[0076] Mapping the weighted temperature data from the phased process onto the motor controller's physical layout based on spatial location requires building a digital two-dimensional model of the motor controller. This model includes information such as the PCB's shape and outline, component distribution, copper foil area, and electrical connections. At each sampling moment, the temperature value of each sensor is placed at its corresponding physical coordinate location, forming a discrete distribution of temperature points. This mapping process links abstract temperature data with concrete physical structures, making temperature analysis more meaningful. Due to the limited number of sensors, the initial mapping result is a sparsely distributed set of temperature points, with temperature values only at the sensor locations.

[0077] Thermal conduction interpolation is performed at non-measurement points in the temperature spatial distribution map to fill in temperature gaps and obtain the temperature field distribution. This interpolation method is based on a physical model of heat conduction, taking into account PCB material properties, copper foil distribution, and device heating characteristics. Specifically, a discrete solution of the two-dimensional Laplace equation is employed. For any non-measurement point, its temperature is calculated as a weighted average of surrounding known temperature points, with the weight inversely proportional to the distance and taking into account the characteristics of the heat conduction path. For areas with thermal barriers (such as perforations and device boundaries), the weight calculation method is adjusted to minimize the impact of heat conduction between separated areas. Interpolation is performed iteratively, starting from known temperature points at the boundary and gradually moving inward until all blank areas are filled. This physics-based interpolation method better reflects actual heat conduction laws than simple mathematical interpolation, resulting in a more realistic temperature field.

[0078] The purpose of temporally correlating the spatial temperature distribution map with the motor's operating status signal is to analyze the temperature distribution characteristics under different operating states. The complete temperature field distribution at each moment is marked with the corresponding timestamp and operating status identifier, forming a temperature data structure encompassing three dimensions: space, time, and state. The resulting two-dimensional temperature distribution field matrix is a multidimensional dataset, with each cell containing position coordinates, temperature values, timestamps, and operating status identifiers. This data structure facilitates targeted analysis, such as extracting the temperature rise rate during startup, the temperature distribution pattern during stable operation, and the changes in hotspot locations during different operating stages.

[0079] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0080] (1) Extracting the low-load working condition temperature distribution submatrix, the medium-load working condition temperature distribution submatrix, and the high-load working condition temperature distribution submatrix from the two-dimensional temperature distribution field matrix;

[0081] (2) Perform local extreme value detection on the temperature distribution submatrix under low load conditions, identify the temperature peak point location and temperature value under low load conditions, and calculate the low load hot spot temperature gradient index;

[0082] (3) Identify the temperature peak points of the temperature distribution submatrix under medium load conditions, extract the coordinates and temperature values of the high temperature points under medium load conditions, and analyze the spatial distribution characteristics of medium load hot spots;

[0083] (4) Screen the hot spots of the temperature distribution submatrix under high load conditions, locate the boundaries and center points of the high load hot spot area, and calculate the high load thermal quality index;

[0084] (5) Calculate the correlation between the positions of low-load hot spots, medium-load hot spots, and high-load hot spots and the motor speed data to generate the motor speed correlation index for each hot spot;

[0085] (6) Integrate the low-load hotspot temperature gradient index, the medium-load hotspot spatial distribution characteristics, the high-load thermal mass index, and the correlation index between the hotspot position and the motor speed to form a hotspot feature vector set that includes the position, motor speed correlation, and load correlation.

[0086] Specifically, data classification is performed according to the motor load state. Extracting temperature distribution sub-matrices for different load conditions from the two-dimensional temperature distribution field matrix is achieved based on the motor load parameters. The motor load state is usually defined by the ratio of phase current to rated current or the ratio of output power to rated power. The low-load condition is defined as a load ratio in the range of 0-40%, the medium-load condition is defined as a load ratio in the range of 40-70%, and the high-load condition is defined as a load ratio in the range of 70-100%. For each load condition, the temperature data of the corresponding time period is extracted from the original two-dimensional temperature distribution field matrix to form three sub-matrices: the low-load condition temperature distribution sub-matrix (TL), the medium-load condition temperature distribution sub-matrix (TM), and the high-load condition temperature distribution sub-matrix (TH). Each sub-matrix retains the spatial distribution information of the original matrix, but only contains temperature data under a specific load state.

[0087] When detecting local extreme values in the temperature distribution submatrix under low-load conditions, a sliding window search algorithm is used. A search window (usually a 5×5 or 7×7 grid) is set in two-dimensional space. The window slides across the entire temperature distribution submatrix, and with each step, the temperature difference between the center point of the window and the surrounding points is calculated. When the temperature of the center point is higher than that of all other points in the window and the temperature difference exceeds a preset threshold (usually 2°C), the point is marked as the temperature peak point under low-load conditions. The position coordinates and temperature value of each peak point are recorded to form a low-load hotspot set. For each low-load hotspot, its temperature gradient index is calculated, expressed as:

[0088]

[0089] in, Represents the low-load hotspot temperature gradient index, is the number of neighbor points around the hotspot, is the hot spot temperature, is the temperature of the kth neighbor point, is the distance from the hotspot to the kth neighbor point. A larger value indicates a steeper temperature change around the hotspot and a higher possibility of heat diffusion being hindered.

[0090] A similar approach is used to identify temperature peaks in the temperature distribution submatrix under medium-load conditions, but the window size is typically expanded to a 9×9 or 11×11 grid, and the threshold is increased to 3-4°C to accommodate the more diffuse temperature distribution under medium-load conditions. The location coordinates and temperature values of the identified medium-load hotspots are recorded to form a medium-load hotspot set. Analysis of the spatial distribution characteristics of medium-load hotspots includes calculating hotspot density, hotspot spacing, and hotspot clustering. Hotspot density represents the number of hotspots per unit area; hotspot spacing calculates the minimum distance between any two hotspots; and hotspot clustering, calculated using spatial autocorrelation methods, reflects the degree of clustering or dispersion of the hotspot distribution. These characteristics help understand the overall pattern of temperature distribution under medium-load conditions.

[0091] Hotspot screening for the temperature distribution submatrix under high-load conditions requires more stringent screening criteria. First, an absolute temperature threshold is set (usually 80% of the maximum allowable operating temperature of the equipment). Any point exceeding this threshold is considered a potential high-load hotspot. Then, a region growing algorithm is used to determine the hotspot area. Starting from each potential hotspot, adjacent points with similar temperatures are gradually included in the same area until the boundary temperature of the area is lower than a given value (usually the center point temperature minus 10°C). This results in a hotspot area with a continuous boundary, rather than a discrete point. Record the boundary contour coordinate set and center point position of each hotspot area. For each high-load hotspot area, calculate its thermal mass index, which is defined as:

[0092]

[0093] in, Indicates the high load thermal mass index, is the area of the hotspot region, is the temperature at the center of the region, is the average temperature of the entire motor controller, is the time rate of change of the hotspot temperature. It takes into account the size of the hotspot, the degree of temperature exceedance and the rate of temperature rise, and is an important indicator for evaluating the severity of the hotspot.

[0094] The correlation between the locations of low-load hotspots, medium-load hotspots, and high-load hotspots and the motor speed data is calculated to quantify the degree of correlation between the formation of hotspots and the motor operating parameters. For any hotspot (x, y), its motor speed correlation index is calculated as:

[0095]

[0096] in, Indicates the rotation speed correlation index of the hot spot at position (x,y), is the temperature of the location at the i-th sampling moment, is the average temperature at that location, is the motor speed at the i-th sampling moment, is the average speed, is the number of samples. This formula essentially calculates the absolute value of the Pearson correlation coefficient between temperature and speed. The result ranges from 0 to 1. The closer the value is to 1, the stronger the correlation between the hotspot temperature and the motor speed.

[0097] By integrating various indicators, a feature vector is constructed for each hotspot. This feature vector contains the following elements: the hotspot's location coordinates (x, y), the load condition category (low / medium / high), the temperature value, load-specific indicators (temperature gradient for low-load hotspots, spatial distribution characteristics for medium-load hotspots, and thermal mass index (HMI) for high-load hotspots), and the motor speed correlation index (SCI). This collection of feature vectors fully describes the key characteristics of the motor controller's temperature distribution, providing a data foundation for subsequent protection strategy development.

[0098] Taking a brushless motor controller for an electric vehicle air conditioning system as an example, the temperature analysis process under different load conditions is as follows: When the motor is operating at low load (phase current 30% of rated value), local extreme value detection identifies three temperature peaks: located in the power MOSFET region, the driver IC region, and the PWM control module region, with temperatures of 56°C, 42°C, and 38°C, respectively. The calculated temperature gradient index of the hot spot in the power MOSFET region is 1.5°C / mm, indicating relatively gentle heat diffusion. When the motor is switched to medium load (phase current 60% of rated value), the temperature in the power MOSFET region rises to 78°C, the hot spot area expands, the hot spot density increases, and the hot spot spacing decreases. When the motor is operating at high load (phase current 90% of rated value), the temperature in the power MOSFET region reaches 112°C, forming a continuous hot spot area of approximately 300 mm². The calculated thermal mass index (HMI) is 8400°C·mm²·°C / s, indicating a severe hot spot with rapid temperature rise over a large area. By calculating the speed correlation, we found that the SCI value of this hotspot is 0.87, indicating that its temperature is highly correlated with the motor speed. All these features are integrated into a hotspot feature vector set.

[0099] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0100] (1) Obtain the maximum junction temperature specification value of the power MOSFET area and the maximum operating temperature specification value of the driver IC area, and set them as the basic protection upper limit temperature;

[0101] (2) According to the location information of each hotspot in the hotspot feature vector set, the hotspot location weight is adjusted for the basic protection upper limit temperature to form a position-weighted protection temperature value;

[0102] (3) Based on the motor speed correlation index in the hotspot feature vector set, a speed-temperature rise relationship matrix is constructed to calculate the temperature correction coefficient in different speed ranges;

[0103] (4) Based on the load correlation in the hotspot feature vector set and combined with the temperature correction coefficient, five-level progressive protection thresholds are generated for the startup phase, acceleration phase, stable operation phase, and braking phase respectively;

[0104] (5) For the five-level progressive protection threshold, set the temperature warning trigger condition, PWM frequency reduction trigger condition, power limit trigger condition, forced frequency reduction trigger condition and safety shutdown trigger condition respectively;

[0105] (6) Combine the five-level progressive protection thresholds with the corresponding trigger conditions to generate a protection execution instruction set that includes the hierarchical trigger conditions and corresponding control strategies.

[0106] Specifically, the maximum junction temperature specifications for the power MOSFET and the driver IC are extracted from the motor controller's device datasheet. These values represent the maximum allowable operating temperatures specified by the device manufacturer. Typical maximum junction temperature specifications for power MOSFETs are 150-175°C, while those for driver ICs are typically 125-150°C. Considering long-term reliability, these values are set as the upper limit temperature for basic protection, serving as the baseline for subsequent protection threshold calculations.

[0107] Adjusting the basic protection upper limit temperature based on the location information of each hotspot in the hotspot feature vector set is a key step in considering the importance of the area where the hotspot is located. Hotspots in different locations have different impacts on system safety and need to be assigned different weights. The position weight adjustment uses a regional importance mapping table to divide the motor controller PCB board into several key areas and assign importance weights to each area. The power input area and the key signal processing area have higher weights (usually 0.8-0.9), while the auxiliary circuit area has a lower weight (usually 0.95-1.0). The position weighted protection temperature value is calculated as follows:

[0108]

[0109] in, is the position weighted protection temperature value at position (x, y), is the basic protection upper limit temperature of the device corresponding to this position, is the importance weight of the location. A weight value less than 1 results in a protection temperature lower than the basic upper limit temperature, thus providing a larger safety margin for the critical area.

[0110] First, divide the motor speed range into several intervals (such as low speed, medium speed, and high speed). Then, based on historical operating data, calculate the average temperature rise rate of each hotspot in each speed interval. The speed-temperature rise relationship matrix is a two-dimensional matrix, with rows representing different hotspots, columns representing different speed intervals, and matrix elements representing corresponding temperature rise rates. Based on this matrix, calculate the temperature correction coefficient for each speed interval s and each hotspot (x, y). :

[0111]

[0112] in, is the temperature rise rate of the hot spot (x, y) at the reference speed, is the temperature rise rate of the hot spot in the speed range s, is the motor speed correlation index of the hotspot, is the adjustment coefficient (usually 0.2-0.5). When the hotspot temperature is highly correlated with the speed (SCI is close to 1) and the current speed results in a high temperature rise rate, If the value is smaller, the protection threshold is lowered accordingly, and the protection is triggered in advance.

[0113] Based on the load correlation of the hotspot feature vectors and the temperature correction coefficient, the characteristics of each motor's operating phase must be considered when formulating protection thresholds for different operating phases. The instantaneous current value during the startup phase is large but short-lived, the power is continuously high during the acceleration phase, the temperature tends to be stable during the stable operation phase, and reverse energy feedback may be generated during the braking phase. For each operating phase p and each hotspot (x, y), a five-level progressive protection threshold is designed:

[0114]

[0115] in, is the k-th level protection threshold (k=1,2,3,4,5), is the basic temperature margin of the kth level protection (such as 20℃, 10℃, 5℃, 2℃, 0℃), is the temperature correction coefficient at the corresponding speed in the operating stage p, is the load-dependency correction factor, determined based on the hotspot's sensitivity to the load. This results in a set of five progressive protection thresholds tailored to different locations and operating phases, enabling dynamic adjustment of protection responses based on actual operating conditions.

[0116] Corresponding trigger conditions and control strategies are set for the five-level progressive protection thresholds. The first level (temperature warning trigger) is triggered when the temperature reaches the first threshold, increasing the temperature sampling frequency to prepare for possible subsequent protection actions. The second level (PWM frequency reduction trigger) is triggered when the temperature reaches the second threshold, reducing switching losses by reducing the PWM switching frequency (typically by 20%). The third level (power limit trigger) is triggered when the temperature reaches the third threshold, limiting the motor's maximum power output to 85% of the rated power. The fourth level (forced frequency reduction trigger) is triggered when the temperature reaches the fourth threshold, further limiting power to 70% and increasing the cooling fan speed. The fifth level (safety shutdown trigger) is triggered when the temperature reaches the fifth threshold, executing the safety shutdown procedure to prevent device damage. Each trigger condition includes not only the temperature threshold but also the temperature duration requirement to avoid unnecessary protection actions caused by transient temperature fluctuations. Combining the five-level progressive protection thresholds with the corresponding trigger conditions and control strategies generates a complete protection execution instruction set. Each instruction contains a trigger condition (temperature threshold, duration), an action to be executed (such as adjusting PWM parameters), and a recovery condition (temperature drops to a certain value and persists for a certain period of time). These instructions are prioritized, with higher-level protections taking precedence over lower-level ones. The protection execution instruction set is programmed into the motor controller's control program. During actual operation, temperature data is monitored in real time. Once the trigger condition is met, the corresponding control strategy is immediately executed.

[0117] For example, a controller for the cooling system of a new energy vehicle drive motor utilizes a multiphase bridge power drive circuit. The maximum junction temperature specification for the power MOSFET area is 175°C, and the maximum operating temperature for the driver IC is 150°C. Considering long-term reliability, these two values are set as the upper limit temperatures for basic protection. After adjusting the position weights, the protection temperature of the power MOSFET near the power input is reduced to 157.5°C (weight 0.9), and the protection temperature of the driver IC in the signal control area is reduced to 135°C (weight 0.9). Analysis of the controller's temperature rise characteristics at different speeds revealed that the temperature rise rate of a certain power MOSFET hotspot at high speeds is 2.5 times that at low speeds, and its speed correlation index (SCI) is 0.85. Calculated by a formula, the temperature correction coefficient for this hotspot in the high-speed range is 0.37, meaning that the protection threshold is significantly lowered at high speeds. The resulting five-level protection thresholds are: warning trigger temperature 137.5°C, PWM frequency reduction trigger temperature 147.5°C, power limit trigger temperature 152.5°C, forced frequency reduction trigger temperature 155.5°C, and safety shutdown trigger temperature 157.5°C. When the motor temperature reaches 137.5°C during operation, the controller immediately increases the temperature sampling frequency from 1Hz to 20Hz. If the temperature continues to rise to 147.5°C, the PWM frequency automatically decreases from 20kHz to 16kHz, effectively reducing the heat generated by switching losses. If the temperature still rises to 152.5°C, the controller will limit the maximum output power to ensure safe system operation. This multi-level protection strategy not only handles overheating conditions of varying severity but also dynamically adjusts protection parameters based on the motor's operating status, ensuring safe and reliable operation of the motor controller while minimizing unnecessary performance losses.

[0118] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0119] (1) Receive the temperature warning trigger signal in the protection execution instruction set and increase the sampling rate of the PWM controller from the standard state to the high-speed sampling state;

[0120] (2) According to the PWM frequency reduction trigger signal in the protection execution instruction set, the motor PWM switching frequency is reduced according to the slope limit function, and the switching frequency is gradually reduced from the original frequency value to 80% of the original frequency, thereby reducing the switching loss heat;

[0121] (3) According to the power limit trigger signal in the protection execution instruction set, the upper limit of the motor PWM duty cycle is controlled to be lowered, limiting the maximum power output of the motor to 85% of the normal value;

[0122] (4) Based on the forced frequency reduction trigger signal in the protection execution instruction set, the PWM duty cycle and switching frequency are jointly regulated to further limit the motor power to 70% of the normal value, and the temperature change rate during the regulation process is recorded;

[0123] (5) Record the trigger temperature, motor operating parameters and temperature response data of each temperature protection action into the temperature event log database to construct the operating condition-temperature rise characteristic map;

[0124] (6) By analyzing the temperature variation patterns under different seasons and environmental conditions in the working condition-temperature rise characteristic diagram, the hierarchical trigger condition thresholds in the protection execution instruction set are periodically optimized and adjusted.

[0125] Specifically, upon receiving a temperature warning trigger signal from the protection execution instruction set, the controller captures this signal through an interrupt service routine, triggering the temperature warning process. At this point, the controller immediately modifies the internal timer configuration parameters, increasing the PWM controller's sampling rate from the standard state (typically 1Hz) to a high-speed sampling state (typically 20Hz). This sampling rate adjustment is achieved by reconfiguring the timer's frequency division coefficient and count value. The frequency division coefficient is reduced from the original value to 1 / 20, resulting in a corresponding increase in the frequency of temperature sampling interrupt triggers. High-speed sampling allows for more precise capture of temperature trends, providing more timely data support for potential subsequent protection actions. Simultaneously, the controller sets the temperature warning status flag, allowing other modules to query the system's current protection status. If the temperature continues to rise, triggering a PWM frequency reduction protection level, the controller receives a PWM frequency reduction trigger signal from the protection execution instruction set and initiates the smooth frequency modulation process. To prevent motor instability or current surges caused by sudden frequency changes, the controller uses a slope limit function to gradually reduce the motor's PWM switching frequency. The slope limit function defines the maximum frequency variation allowed per control cycle, typically set to 4% of the original frequency per 100 milliseconds. Assuming the original PWM frequency is 20kHz and the target frequency is reduced to 80% of the original frequency, or 16kHz, for a total frequency reduction of 4kHz, a smooth transition requires at least 500 milliseconds based on the slope limit. Specifically, the controller calculates the current PWM frequency setting during each control cycle (e.g., 10 milliseconds) and updates the PWM generator's frequency register. During the frequency reduction process, the controller continuously monitors temperature changes and records the frequency-temperature relationship. Reducing the PWM frequency effectively reduces switching losses in the power transistors. Switching losses are approximately proportional to the switching frequency, so a 20% frequency reduction can reduce switching heat generation by nearly 20%.

[0126] When the temperature protection level escalates to a power limit, the controller initiates the PWM duty cycle limiting process based on the power limit trigger signal in the protection execution instruction set. This controlled reduction in the duty cycle upper limit is achieved by modifying the PWM generator's comparator threshold, lowering the duty cycle upper limit from its original maximum value (e.g., 95%) to a limit value (e.g., 80.75%, or 85% of the nominal value). Given that motor output power is proportional to the square of the duty cycle, limiting the duty cycle to 85% will limit the power to approximately 72.25%. Duty cycle adjustment also uses slope limiting to prevent mechanical shock caused by sudden changes. The controller also dynamically fine-tunes the duty cycle upper limit based on actual temperature changes to keep motor power within a safe range.

[0127] When the forced frequency reduction protection level is triggered, the controller initiates a combined control process for the PWM duty cycle and switching frequency based on the forced frequency reduction trigger signal in the protection execution instruction set. At this point, stricter power limits are applied, further limiting the motor power to 70% of the nominal value. This is achieved by simultaneously reducing the PWM switching frequency and the upper duty cycle limit. The switching frequency may be further reduced to 70% of the original frequency, and the upper duty cycle limit to approximately 84% of the original value (calculated based on 0.84²≈0.7). During the combined control process, the controller also calculates and records the temperature rate of change in real time. This is done by recording the current temperature during each control cycle and calculating the average rate of change together with the temperature values from previous cycles. The temperature rate of change serves as an important indicator of control effectiveness, guiding the controller in determining whether the current protection measures are sufficient.

[0128] Temperature event logging is a crucial tool for tracking and analyzing protection processes. The controller records complete information about each temperature protection action in a temperature event log database stored in non-volatile memory (such as EEPROM or Flash). This information includes the trigger temperature, trigger time, motor operating parameters (such as speed, load, phase current, and bus voltage) at the time, post-trigger control measures, and the temperature response curve (temperature change data over a period of time after protection action). Each record includes a timestamp and environmental parameters (such as ambient temperature and humidity) to facilitate subsequent analysis of temperature behavior under different conditions. These records are organized chronologically to form a structured temperature event dataset. Based on this data, the controller constructs a working condition-temperature rise characteristic map, which maps different motor operating states to temperature trends.

[0129] Periodic analysis of the operating condition-temperature rise characteristic map is key to adaptively optimizing protection strategies. The controller sets a fixed analysis cycle (such as every 100 hours of operation or once a month) and batch processes the accumulated temperature event logs. This analysis includes comparing temperature rise characteristics across seasons, varying temperature responses under similar operating conditions, and evaluating the effectiveness of protection actions. The analysis utilizes pattern recognition to identify patterns in environmental factors influencing temperature behavior. For example, in high summer temperatures, the temperature rise rate under the same operating conditions is approximately 30% faster than in winter. Alternatively, protection thresholds may be found to be overly conservative or overly aggressive under certain specific operating conditions. Based on the analysis results, the controller optimizes the hierarchical trigger thresholds within the protection execution instruction set. For example, in summer, the controller automatically lowers each level of protection temperature threshold by 5-10°C, or adjusts the intensity of protection actions based on specific operating conditions. These adjusted protection parameters take effect in the next operating cycle, forming a closed-loop, self-optimizing protection mechanism.

[0130] For example, a fan motor controller for an electric vehicle thermal management system, operating in a springtime ambient temperature of 15°C, triggered a temperature warning when the power MOSFET temperature reached 125°C. The sampling rate was rapidly increased from 1Hz to 20Hz. The temperature then continued to rise to 135°C, triggering PWM frequency reduction protection. The controller smoothly reduced the PWM frequency from 25kHz to 20kHz over 600 milliseconds. However, the effect was not significant, and the temperature continued to rise to 142°C, triggering power limit protection. At this point, the duty cycle limit was reduced from 95% to 80.75%, and the motor power dropped to approximately 72% of normal. This measure effectively controlled the temperature rise, stopping at 145°C and then slowly decreasing. Detailed data from the entire protection process was recorded, including the trigger point temperature, ambient temperature, motor speed (4500 rpm), current (8.2A), and the temperature response curve after frequency and power reduction. As the seasons changed, the controller accumulated a large amount of temperature event data under different environmental conditions. After analysis, it was found that when the ambient temperature in summer exceeds 30°C, the temperature rise rate under the same operating conditions is about 25% faster than in spring. Therefore, the controller automatically lowers all protection thresholds in summer by 7°C, intervening in advance, and effectively avoiding performance fluctuations caused by frequent triggering of high-level protection in high-temperature environments in summer.

[0131] The above describes the temperature monitoring method for the motor controller in the embodiment of the present application. The following describes the temperature monitoring system for the motor controller in the embodiment of the present application. Figure 2 In one embodiment of the present application, a temperature monitoring system for a motor controller includes:

[0132] The acquisition module is used to collect multi-point temperatures of the power MOSFET area, driver IC area, and PWM control module area of the motor controller through a temperature sensor array, and obtain a three-dimensional temperature data stream containing timestamps, spatial positions, and temperature values;

[0133] a processing module, configured to perform filtering, noise reduction, and outlier marking on the three-dimensional temperature data stream to obtain a standardized temperature matrix and a reliability mark;

[0134] An analysis module is configured to analyze the temperature distribution during motor startup, acceleration, stable operation, and braking based on the standardized temperature matrix and the reliability mark, and obtain a two-dimensional temperature distribution field matrix associated with the motor operation state;

[0135] A correlation module is used to detect and perform correlation analysis on the temperature peak points under different load conditions based on the two-dimensional temperature distribution field matrix to obtain a hotspot feature vector set including the correlation between position, motor speed and load correlation;

[0136] A generation module is used to determine adaptive protection thresholds corresponding to different operating stages based on the hotspot feature vector set, combined with the power device temperature specifications and the current operating parameters of the motor, and generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies;

[0137] The control module is used to control the motor PWM duty cycle and switching frequency in real time according to the protection execution instruction set, and optimize and adjust the protection parameters through historical data on the relationship between operating conditions and temperature rise.

[0138] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0139] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0140] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0141] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0142] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part 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 for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Claims

1. A temperature monitoring method for a motor controller, characterized in that: The temperature monitoring method for the motor controller includes: The temperature sensor array collects multi-point temperature data from the power MOSFET area, driver IC area, and PWM control module area of the motor controller, generating a three-dimensional temperature data stream containing timestamps, spatial positions, and temperature values. According to the three-dimensional temperature data stream, filtering, denoising and outlier marking are performed on the data to obtain a standardized temperature matrix and reliability marks; Based on the standardized temperature matrix and reliability mark, the temperature distribution during the motor startup, acceleration, stable operation and braking process is analyzed to obtain a two-dimensional temperature distribution field matrix associated with the motor operation state, including: obtaining the motor operation state signal from the motor controller, dividing the motor operation process into the startup stage, acceleration stage, stable operation stage and braking stage; grouping the standardized temperature matrix in time series according to the motor operation stage to form a stage temperature data set; assigning a trust weight to each data point in the stage temperature data set based on the reliability mark to generate weighted stage temperature data; mapping the weighted stage temperature data to the physical layout of the motor controller according to the spatial position to construct a temperature space distribution map; performing heat conduction interpolation on the non-measurement point positions in the temperature space distribution map to fill the temperature blank area; and performing time synchronization association between the temperature space distribution map and the motor operation state signal to generate a two-dimensional temperature distribution field matrix associated with the motor operation state. According to the two-dimensional temperature distribution field matrix, the temperature peak points under different load conditions are detected and correlated, and a hotspot feature vector set containing position, motor speed correlation and load correlation is obtained, including: extracting the low-load working condition temperature distribution submatrix, the medium-load working condition temperature distribution submatrix and the high-load working condition temperature distribution submatrix from the two-dimensional temperature distribution field matrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the temperature peak point position and temperature value under the low-load state, and calculating the low-load hotspot temperature gradient index; performing temperature peak point identification on the medium-load working condition temperature distribution submatrix, extracting the medium-load working condition temperature distribution submatrix and the high-load working condition temperature distribution submatrix ... medium-load working condition temperature distribution submatrix and the high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the medium-load working condition temperature distribution submatrix and the high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the medium-load working condition temperature distribution submatrix and the high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the medium-load working condition temperature distribution submatrix and the high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the medium-load working condition temperature distribution submatrix and the high-load working condition temperature The coordinates and temperature values of the high temperature points in the load state are used to analyze the spatial distribution characteristics of the medium-load hotspots; hotspot screening is performed on the temperature distribution submatrix of the high-load working condition, the boundaries and center points of the high-load hotspot area are located, and the high-load thermal quality index is calculated; the positions of the low-load hotspots, medium-load hotspots, and high-load hotspots are correlated with the motor speed data to generate the motor speed correlation index of each hotspot; the low-load hotspot temperature gradient index, the medium-load hotspot spatial distribution characteristics, the high-load thermal quality index, the hotspot positions, and the motor speed correlation index are integrated to form a hotspot feature vector set including position, motor speed correlation, and load correlation; Based on the hotspot feature vector set, combined with the power device temperature specifications and the current operating parameters of the motor, adaptive protection thresholds corresponding to different operating stages are determined, and a protection execution instruction set containing hierarchical trigger conditions and corresponding control strategies is generated, including: obtaining the maximum junction temperature specification value of the power MOSFET region and the maximum operating temperature specification value of the driver IC region, and setting them as the basic protection upper limit temperature; adjusting the hotspot position weight of the basic protection upper limit temperature based on the position information of each hotspot in the hotspot feature vector set to form a position-weighted protection temperature value; constructing a speed-temperature rise relationship matrix based on the motor speed correlation index in the hotspot feature vector set, and calculating the temperature correction coefficients for different speed ranges; based on the load correlation in the hotspot feature vector set and combined with the temperature correction coefficients, five-level progressive protection thresholds are generated for the startup stage, acceleration stage, stable operation stage, and braking stage respectively; for the five-level progressive protection thresholds, temperature warning trigger conditions, PWM frequency reduction trigger conditions, power limit trigger conditions, forced frequency reduction trigger conditions, and safety shutdown trigger conditions are set respectively; the five-level progressive protection thresholds are combined with the corresponding trigger conditions to generate a protection execution instruction set containing hierarchical trigger conditions and corresponding control strategies; According to the protection execution instruction set, the motor PWM duty cycle and switching frequency are controlled in real time, and the protection parameters are optimized and adjusted based on the historical data of the relationship between operating conditions and temperature rise.

2. The temperature monitoring method for a motor controller according to claim 1, wherein: The temperature sensor array is used to collect multi-point temperatures of the power MOSFET area, driver IC area, and PWM control module area of the motor controller to obtain a three-dimensional temperature data stream containing timestamps, spatial positions, and temperature values, including: Place NTC thermistor sensors in the power MOSFET area of the motor controller to collect the surrounding temperature of the power device with high precision; Place a digital temperature sensor in the driver IC area of the motor controller to accurately measure the temperature of the control circuit; Temperature monitoring points are placed in the PWM control module area to track the temperature of the signal processing area in real time. The temperature sampling frequency is dynamically adjusted according to the motor's operating status, with sampling at 1Hz when the motor is operating normally and at 10Hz during high load or startup phases. via a single bus or The bus aggregates the collected temperature data and adds timestamp information; it adds a spatial location identifier to each temperature data point to construct a three-dimensional temperature data stream containing timestamps, spatial locations, and temperature values.

3. The temperature monitoring method for a motor controller according to claim 1, wherein: The method of filtering, denoising and marking outliers on the three-dimensional temperature data stream to obtain a standardized temperature matrix and reliability marks includes: Eliminating high-frequency noise from the temperature data of each sampling point in the three-dimensional temperature data stream by means of a sliding window median filter to generate preliminary filtered temperature data; applying an exponentially weighted moving average process to the preliminary filtered temperature data to form a stabilized temperature sequence; Reorganize the stabilized temperature sequence into a two-dimensional temperature data table according to the spatial distribution of the sensors, and calculate the Z-score value of each point based on the historical temperature mean; Screening temperature mutation points according to the Z-score value, and marking them as potential abnormal points when the Z-score value exceeds a preset threshold; By analyzing the correlation between the potential abnormal point and the spatially adjacent sensor data, the sensor failure is distinguished from the real temperature abnormality, and the sensor reliability weight is generated; The stabilized temperature sequence is normalized and combined with the sensor reliability weight to form a normalized temperature matrix and a reliability mark.

4. The temperature monitoring method for a motor controller according to claim 1, wherein: The protection execution instruction set is used to control the motor PWM duty cycle and switching frequency in real time, and the protection parameters are optimized and adjusted based on the historical data of the relationship between operating conditions and temperature rise, including: Receiving a temperature warning trigger signal in the protection execution instruction set, and increasing the sampling rate of the PWM controller from a standard state to a high-speed sampling state; According to the PWM frequency reduction trigger signal in the protection execution instruction set, the motor PWM switching frequency is reduced according to the slope limit function, and the switching frequency is gradually reduced from the original frequency value to 80% of the original frequency, thereby reducing switching loss heat; Based on the power limit trigger signal in the protection execution instruction set, the upper limit of the motor PWM duty cycle is controlled to be lowered, limiting the maximum power output of the motor to 85% of the normal value; based on the forced frequency reduction trigger signal in the protection execution instruction set, the PWM duty cycle and switching frequency are jointly regulated to further limit the motor power to 70% of the normal value, and the temperature change rate during the regulation process is recorded; The trigger temperature, motor operating parameters and temperature response data of each temperature protection action are recorded in the temperature event log database to build a working condition-temperature rise characteristic map; By analyzing the temperature variation patterns under different seasons and environmental conditions in the working condition-temperature rise characteristic map, the hierarchical trigger condition thresholds in the protection execution instruction set are periodically optimized and adjusted.

5. A temperature monitoring system for a motor controller, used to implement the temperature monitoring method for a motor controller according to any one of claims 1 to 4, characterized in that: The temperature monitoring system for the motor controller includes: The acquisition module is used to collect multi-point temperatures of the power MOSFET area, driver IC area, and PWM control module area of the motor controller through a temperature sensor array, and obtain a three-dimensional temperature data stream containing timestamps, spatial positions, and temperature values; a processing module, configured to perform filtering, noise reduction, and outlier marking on the three-dimensional temperature data stream to obtain a standardized temperature matrix and a reliability mark; An analysis module is used to analyze the temperature distribution during motor startup, acceleration, stable operation and braking based on the standardized temperature matrix and reliability mark to obtain a two-dimensional temperature distribution field matrix associated with the motor operation state, including: obtaining a motor operation state signal from a motor controller, dividing the motor operation process into a startup phase, an acceleration phase, a stable operation phase and a braking phase; grouping the standardized temperature matrix in time series according to the motor operation phase to form a phase temperature data set; assigning a trust weight to each data point in the phase temperature data set based on the reliability mark to generate weighted phase temperature data; mapping the weighted phase temperature data to the physical layout of the motor controller according to spatial position to construct a temperature spatial distribution map; performing heat conduction interpolation on non-measurement point positions in the temperature spatial distribution map to fill temperature blank areas; and performing time synchronization association between the temperature spatial distribution map and the motor operation state signal to generate a two-dimensional temperature distribution field matrix associated with the motor operation state. The correlation module is used to detect and correlate the temperature peak points under different load conditions according to the two-dimensional temperature distribution field matrix to obtain a hotspot feature vector set containing the correlation degree of position, motor speed and load correlation, including: extracting the low-load working condition temperature distribution submatrix, the medium-load working condition temperature distribution submatrix and the high-load working condition temperature distribution submatrix from the two-dimensional temperature distribution field matrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the temperature peak point position and temperature value under the low-load state, and calculating the low-load hotspot temperature gradient index; performing temperature peak point identification on the medium-load working condition temperature distribution submatrix, and obtaining the high-load working condition temperature distribution submatrix ... high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, and obtaining the high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the high-load working condition temperature distribution submatrix; performing local extreme value detection on the low-load working condition temperature distribution submatrix, identifying the high-load working condition temperature distribution submatri The coordinates and temperature values of the high temperature points in the medium load state are taken to analyze the spatial distribution characteristics of the medium load hot spots; the high load working condition temperature distribution submatrix is subjected to hot spot screening, the boundaries and center points of the high load hot spot area are located, and the high load thermal mass index is calculated; the positions of the low load hot spots, medium load hot spots, and high load hot spots are correlated with the motor speed data to generate a motor speed correlation index for each hot spot; the low load hot spot temperature gradient index, the medium load hot spot spatial distribution characteristics, the high load thermal mass index, the hot spot positions, and the motor speed correlation index are integrated to form a hot spot feature vector set containing position, motor speed correlation, and load correlation; A generation module is used to determine the adaptive protection threshold corresponding to different operating stages based on the hotspot feature vector set, combined with the power device temperature specification and the current operating parameters of the motor, and generate a protection execution instruction set containing hierarchical trigger conditions and corresponding control strategies, including: obtaining the maximum junction temperature specification value of the power MOSFET area and the maximum operating temperature specification value of the driver IC area, and setting them as the basic protection upper limit temperature; adjusting the hotspot position weight of the basic protection upper limit temperature according to the position information of each hotspot in the hotspot feature vector set to form a position weighted protection temperature value; based on the motor speed in the hotspot feature vector set Correlation index, constructing a speed-temperature rise relationship matrix, and calculating temperature correction coefficients for different speed ranges; based on the load correlation in the hotspot feature vector set and combined with the temperature correction coefficient, generating five-level progressive protection thresholds for the startup phase, acceleration phase, stable operation phase, and braking phase respectively; for the five-level progressive protection thresholds, setting temperature warning trigger conditions, PWM frequency reduction trigger conditions, power limit trigger conditions, forced frequency reduction trigger conditions, and safety shutdown trigger conditions respectively; combining the five-level progressive protection thresholds with the corresponding trigger conditions to generate a protection execution instruction set containing hierarchical trigger conditions and corresponding control strategies; The control module is used to control the motor PWM duty cycle and switching frequency in real time according to the protection execution instruction set, and optimize and adjust the protection parameters through historical data on the relationship between operating conditions and temperature rise.

6. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the temperature monitoring method for a motor controller according to any one of claims 1 to 4 is implemented. 7 . A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is caused to execute the temperature monitoring method for a motor controller according to claim 1 .

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