Blended wing body aircraft

Abstract

This application discloses an automatic calibration method for BLDC motors, relating to the field of motor calibration technology. The disclosed automatic calibration method for BLDC motors automates the calibration process and automates parameter derivation by automatically acquiring raw calibration data, optimizing and deriving full-speed control parameters, and generating lookup tables. This effectively avoids the inefficiencies of repetitive manual operations and single-point calibration. Furthermore, by combining real-time temperature monitoring and dynamic adjustment mechanisms, the stability of the calibration process and the reliability of the data are ensured, thereby improving calibration efficiency, reducing labor costs, and enhancing the consistency of control parameters.

Description

Technical Field

[0001] This application relates to the field of motor calibration technology, and in particular to an automatic calibration method for BLDC motors. Background Technology

[0002] With the increasing application of motor technology in the electric motorcycle industry, motor calibration, as a core link in ensuring motor operation stability and improving control accuracy, has a decisive impact on industry progress. Currently, the industry mainly employs two methods for motor calibration: traditional manual calibration and preliminary automatic calibration. Traditional manual calibration relies entirely on engineers manually operating a test bench, requiring repeated parameter settings, operating condition switching, and data recording. This highly cumbersome process is easily limited by the operator's experience, leading to increased calibration data errors and failing to meet high-precision control requirements. While preliminary automatic calibration establishes a communication link between the host computer and the motor controller to automate basic command issuance and data acquisition, it still essentially requires executing the complete calibration process for multiple key speed points during actual motor operation, resulting in prolonged test bench occupation and extremely low calibration efficiency. More significantly, existing technologies, whether manual or automatic, cannot eliminate manual data processing and table adaptation, failing to establish an efficient mechanism for deriving full-speed parameters from single-speed calibration data. Furthermore, they lack real-time monitoring and dynamic adjustment capabilities for motor and MOSFET temperatures during the calibration process.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide an automatic calibration method for BLDC motors, which aims to improve calibration efficiency, reduce labor costs, and enhance the consistency of control parameters.

[0005] To achieve the above objectives, this application proposes an automatic calibration method for a BLDC motor, the method comprising: The original calibration data of the calibration speed is obtained by the host computer based on the automatic calibration of the motor; the original calibration data includes the actual torque signal output by the dynamometer, the actual current and actual voltage fed back by the motor controller; The original calibration data is input into the calibration data processing tool for data optimization and derivation to obtain full speed control parameters; A lookup table that can be recognized by the motor controller is generated based on the full speed control parameters, and the lookup table is written into the storage unit of the motor controller; When the motor is running, the current control parameters are obtained by retrieving the lookup table based on the real-time speed and target torque of the motor through the controller's lookup table function. The current control parameters are used as inputs to the current loop, and the output current command drives the motor to run.

[0006] In one embodiment, the step of obtaining the original calibration data of the calibration speed based on the automatic motor calibration host computer includes: The calibration speed is set by the dynamometer, and the calibration current parameter range and temperature monitoring range are preset by the parameter configuration module to generate an automatic calibration current combination table. Based on the automatic calibration current combination table, a calibration current command is sent to the motor controller through the data acquisition module; The system simultaneously acquires the actual torque signal output by the dynamometer, the actual current and voltage fed back by the motor controller, and obtains the motor temperature and MOS temperature. The collected actual torque signal, actual current, actual voltage, motor temperature, and MOS temperature are integrated into the original calibration data for the calibration speed.

[0007] In one embodiment, the steps of obtaining the motor temperature and MOS temperature include: Receive CAN messages sent by the motor controller in real time via the CAN bus; The motor temperature and MOS temperature were extracted from the CAN message; When the motor temperature or the MOS temperature exceeds the preset temperature monitoring range, the calibration process is paused and temperature adjustment is performed to obtain the adjusted motor temperature and MOS temperature.

[0008] In one embodiment, when the motor temperature or the MOS temperature exceeds a preset temperature monitoring range, the calibration process is paused and a temperature adjustment process is performed to obtain the adjusted motor temperature and MOS temperature. This includes the following steps: When the detected temperature exceeds the upper limit of the set temperature range, the motor is controlled to output zero current to cool down and wait, in order to obtain the cooled temperature. When the detected temperature is lower than the lower limit of the set temperature range, the motor is controlled to output a preset d-axis current to perform a heating operation in order to obtain the heated temperature. When the temperature returns to the preset temperature monitoring range, the calibration process resumes and data acquisition continues.

[0009] In one embodiment, the step of inputting the original calibration data into a calibration data processing tool for data optimization and derivation to obtain full-speed control parameters includes: The original calibration data is input into the data filtering module for filtering to remove data points that exceed the preset range, so as to obtain the filtered calibration data. The filtered calibration data is input into the data optimization and derivation module, which calculates the optimal combination of current parameters at the calibration speed based on the maximum torque-current ratio algorithm to obtain the calibration speed parameters. Based on the calibrated speed parameters and the electromagnetic theory model of the motor, the current parameter combinations corresponding to different speed and torque conditions in the full speed range are derived to obtain the full speed derived parameters. The calibrated speed parameters and the full speed derived parameters are integrated into full speed control parameters.

[0010] In one embodiment, the step of inputting the filtered calibration data into the data optimization and derivation module, and calculating the optimal combination of current parameters at the calibration speed based on the maximum torque-current ratio algorithm to obtain the calibration speed parameters includes: Extract the actual torque corresponding to different current combinations from the filtered calibration data; The torque-current ratio for each current combination is calculated based on the actual torque. Compare the torque-current ratios of all current combinations, and select the current parameter combination corresponding to the maximum torque-current ratio as the calibration speed parameter.

[0011] In one embodiment, the step of deriving the current parameter combinations corresponding to different speed and torque conditions across the entire speed range, based on the calibrated speed parameters and the motor electromagnetic theory model, to obtain the full-speed derived parameters includes: Obtain the inherent parameters of the motor, including motor resistance, motor inductance, and back electromotive force coefficient; Based on the calibrated speed parameters and the inherent parameters, an electromagnetic theoretical model of the motor is established; Based on the electromagnetic theory model of the motor, the d-axis current and q-axis current combinations under different speed and torque conditions within the full speed range are calculated to obtain the full speed derivation parameters.

[0012] In one embodiment, the step of generating a lookup table recognizable by the motor controller based on the full-speed control parameters and writing the lookup table into the storage unit of the motor controller includes: The full-speed control parameters are input into the table generation module and formatted according to a one-dimensional or two-dimensional index format to obtain the formatted lookup table data. The formatted lookup data is converted into a data format that the motor controller can recognize in order to obtain a lookup table compatible with the controller; Write the lookup table compatible with the controller into the storage unit of the motor controller.

[0013] In one embodiment, the method further includes: Obtain motor efficiency test data; The motor efficiency test data is visualized to generate an efficiency heatmap. The efficiency heatmap marks the point of maximum efficiency and the efficiency area above the preset efficiency threshold.

[0014] In one embodiment, the step of visualizing the motor efficiency test data to generate an efficiency heatmap includes: The motor efficiency test data is processed into a grid according to the dimensions of speed and torque to obtain gridded efficiency data; A three-dimensional heat map of speed-torque-efficiency is generated based on the gridded efficiency data; Efficiency equipotential lines are plotted in the three-dimensional heat map to obtain an efficiency heat map.

[0015] The automatic calibration method for BLDC motors proposed in this application automates the calibration process and derives intelligent parameters by automatically acquiring raw calibration data, optimizing and deriving full-speed control parameters, and generating lookup tables. This effectively avoids the inefficiencies of repetitive manual operations and single-point calibration. Furthermore, by combining real-time temperature monitoring and dynamic adjustment mechanisms, the stability of the calibration process and the reliability of the data are ensured, thereby improving calibration efficiency, reducing labor costs, and enhancing the consistency of control parameters. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the automatic calibration method for BLDC motors provided in this application; Figure 2 For this application Figure 1 A detailed flowchart of step S100; Figure 3 For this application Figure 1 A detailed flowchart of step S200; Figure 4 For this application Figure 3 A detailed flowchart of step S220; Figure 5 For this application Figure 3 A detailed flowchart of step S230; Figure 6 For this application Figure 1 A detailed flowchart of step S300.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Existing motor calibration technologies, whether relying on traditional manual methods or rudimentary automation, suffer from drawbacks such as cumbersome operation, low data processing efficiency, long calibration cycles, high demands on engineer experience, and a lack of real-time temperature monitoring and adjustment capabilities. These limitations result in high calibration costs and difficulty in ensuring parameter consistency, thus hindering production efficiency in the electric motorcycle industry.

[0023] Based on this, the embodiments of this application provide an automatic calibration method for BLDC motors, referring to... Figure 1 The BLDC motor automatic calibration method includes steps S100 to S500, wherein: Step S100: Obtain the original calibration data of the calibration speed from the host computer based on the automatic motor calibration; the original calibration data includes the actual torque signal output by the dynamometer, the actual current and actual voltage fed back by the motor controller; Step S200: Input the original calibration data into the calibration data processing tool for data optimization and derivation to obtain full speed control parameters; Step S300: Generate a lookup table that the motor controller can recognize based on the full speed control parameters, and write the lookup table into the storage unit of the motor controller; Step S400: When the motor is running, the current control parameters are obtained by retrieving the lookup table based on the real-time speed and target torque of the motor through the controller lookup table function. In step S500, the current control parameters are used as the current loop input, and the current command is output to drive the motor to run.

[0024] In this embodiment, the automatic motor calibration host computer is a software system that can run on a personal computer or other computing device to communicate with the dynamometer and motor controller. Its function is to coordinate the calibration process, send control commands, and receive and process data from the lower-level devices. Raw calibration data refers to the unprocessed motor operating data directly collected by the testing equipment at a specific calibration speed. This includes the actual torque signal measured by the dynamometer, the actual current value reported by the motor controller, and the actual voltage value; these data form the basis for subsequent parameter optimization and derivation. The calibration data processing tool is a software module or program used to analyze, optimize, and derive the raw calibration data. Its goal is to extract useful information from the raw data and generate full-speed control parameters suitable for motor control.

[0025] In this embodiment, the full-speed control parameters refer to the set of current control parameters covering the entire operating speed range of the motor. These parameters can exist in the form of a table or function, allowing the motor controller to obtain the corresponding current command by looking up the table or calculating it under any speed and torque conditions. A lookup table is a specific representation of the full-speed control parameters, organized into a format that the motor controller can directly read and use. By retrieving this table, the controller can quickly obtain the required current control parameters based on the current operating state (such as real-time speed and target torque). Current control parameters are current commands used to drive the motor and can include d-axis current and q-axis current. These parameters directly affect the motor's output torque and efficiency and are the core of the motor controller's precise control. The current loop is a control circuit within the motor controller, responsible for adjusting the inverter's output current according to the input current control parameters to precisely control the motor's operating state.

[0026] In this embodiment, the BLDC motor automatic calibration method first obtains the raw calibration data of the calibration speed based on the motor automatic calibration host computer. The raw calibration data can consist of the actual torque signal output by the dynamometer, the actual current and voltage fed back by the motor controller. For example, it can be obtained by manually setting the dynamometer speed and recording the torque signal output by the dynamometer and the current and voltage fed back by the motor controller point by point. Alternatively, it can be obtained by preset a series of current commands and manually triggering data acquisition to obtain the torque, current and voltage data under the corresponding operating conditions.

[0027] In this embodiment, the original calibration data is input into a calibration data processing tool for data optimization and derivation to obtain full-speed control parameters. The original calibration data can be input into a data processing tool that can perform basic data processing and preliminary calculations, such as averaging repeated data or using simple linear interpolation methods to derive control parameters at other speeds from limited calibration point data, thereby converting the original data into a parameter form that can be used for subsequent control.

[0028] Furthermore, a lookup table recognizable by the motor controller is generated based on the full-speed control parameters, and this lookup table is written into the motor controller's storage unit. The full-speed control parameters can be manually organized into a tabular form, for example, by editing with spreadsheet software. This table can then be manually converted into a specific file format that the motor controller can read, and written into the motor controller's storage unit via a programmer or debugging tool to ensure that the controller can access the required control data during operation.

[0029] In this embodiment, during motor operation, the controller's lookup table function retrieves the current control parameters based on the motor's real-time speed and target torque. During actual motor operation, the motor controller continuously monitors the motor's real-time speed and the received target torque command. The controller's internal lookup table function can be configured to directly search for the corresponding current control parameters in a preset two-dimensional array based on these input parameters. This search process can be a simple index matching to quickly obtain the required control quantity.

[0030] In this embodiment, the current control parameter is finally used as the input to the current loop, and the output current command drives the motor to run. The acquired current control parameter is then sent to the motor's current control loop. The current control loop generates specific current commands based on these parameters, such as d-axis current and q-axis current commands, and drives the motor to execute the corresponding operating states, thereby achieving precise control of the motor's torque and speed.

[0031] In this embodiment, by automating data acquisition, optimizing derivation, and generating and writing tables, the problems of cumbersome operation, low efficiency, and inaccurate data processing in traditional calibration methods are effectively solved. This method enables rapid conversion from single-speed calibration to full-speed application, shortens the calibration cycle, reduces reliance on human experience, and improves the consistency of calibration parameters, thus providing the electric motorcycle industry with an efficient and reliable solution for acquiring motor control parameters.

[0032] In one feasible implementation, refer to Figure 2 Step S100 includes steps S110 to S140, wherein: Step S110: Set the calibration speed using a dynamometer, preset the calibration current parameter range and temperature monitoring range using the parameter configuration module, and generate an automatic calibration current combination table. Step S120: Based on the automatic calibration current combination table, a calibration current command is sent to the motor controller through the data acquisition module; Step S130: Simultaneously acquire the actual torque signal output by the dynamometer, the actual current and actual voltage fed back by the motor controller, and obtain the motor temperature and MOS temperature; Step S140: The collected actual torque signal, actual current, actual voltage, motor temperature and MOS temperature are integrated into the original calibration data of the calibration speed.

[0033] In this embodiment, the dynamometer can precisely control and maintain the BLDC motor at a preset calibration speed, providing stable mechanical load conditions for subsequent data acquisition. The parameter configuration module defines the current command range that the motor will experience during the calibration process, ensuring that the data covers the main operating area of ​​the motor. Simultaneously, a preset temperature monitoring range provides a safety boundary for the thermal state of the motor and controller during calibration, preventing overheating or overcooling from affecting the calibration data. Based on the preset calibration current parameter range, the system automatically generates a series of discrete current command combinations, forming an ordered calibration sequence, ensuring the comprehensiveness and systematic nature of the data acquisition.

[0034] In this embodiment, the data acquisition module sends current commands to the motor controller sequentially according to the order in the automatic calibration current combination table, driving the motor to operate under different current conditions. When the motor responds to each current command and reaches a steady state, the system synchronously acquires the actual torque signal from the dynamometer, the actual current and voltage from the motor controller, and the temperature information of the motor body and MOSFETs. This synchronous acquisition ensures time consistency between data points and improves data accuracy. Finally, all relevant data (actual torque, actual current, actual voltage, motor temperature, and MOSFET temperature) synchronously acquired under specific calibration speed and current commands are correlated and stored to form a complete original calibration dataset, laying the foundation for subsequent data processing.

[0035] In this embodiment, when acquiring the original calibration data for the calibration speed, the calibration speed can be precisely set and maintained using a dynamometer, and the calibration current parameter range and temperature monitoring range can be preset using a parameter configuration module, thereby generating a systematic automatic calibration current combination table. Based on this combination table, the data acquisition module can systematically send calibration current commands to the motor controller, and simultaneously acquire the actual torque signal output by the dynamometer, the actual current and voltage fed back by the motor controller, as well as the motor temperature and MOS temperature. This standardized and synchronized data acquisition process not only ensures the comprehensiveness and accuracy of the original calibration data under different current conditions, but also provides a basis for subsequent temperature anomaly handling by acquiring the motor and MOS temperatures, effectively avoiding calibration data distortion or calibration interruption caused by temperature fluctuations or overheating, and improving the quality of the original calibration data and the reliability of the calibration process.

[0036] In one feasible implementation, the steps of obtaining the motor temperature and MOS temperature include: receiving CAN messages sent by the motor controller in real time via the CAN bus; parsing the motor temperature and MOS temperature from the CAN messages; and pausing the calibration process and performing temperature adjustment when the motor temperature or the MOS temperature exceeds the preset temperature monitoring range, so as to obtain the adjusted motor temperature and MOS temperature.

[0037] In this embodiment, receiving CAN messages sent by the motor controller in real time via the CAN bus refers to establishing a real-time data link between the calibration host computer and the motor controller using the standardized serial communication protocol of the CAN (Controller Area Network) bus. The motor controller, as a node on the CAN bus, periodically encapsulates its own operating status information, including temperature sensor data inside the motor and temperature sensor data near the power MOSFETs, into CAN messages and broadcasts them to the bus. The calibration host computer, equipped with CAN interface hardware (such as a CAN card or CAN-USB converter) and corresponding drivers, can listen to and receive these messages in real time. This real-time reception mechanism ensures that the host computer can obtain the actual temperature status of the motor and controller during the calibration process in a timely and accurate manner, providing a data foundation for subsequent temperature monitoring and processing.

[0038] In this embodiment, parsing the motor temperature and MOS temperature from the CAN message means that after receiving the CAN message, the calibration host computer needs to decode the message payload according to a predefined communication protocol specification (e.g., a DBC file or a custom protocol document). Each CAN message can contain an identifier (ID) and an 8-byte data field. The protocol specification details which message ID corresponds to the temperature information, and at which position (byte offset) in the data field, in what data type (e.g., signed integer, unsigned integer), what scaling factor, and what offset are used to encode the motor temperature and MOS temperature. The parsing process involves converting the raw binary data into a readable physical quantity, such as degrees Celsius. Through precise parsing, accurate motor temperature and MOS temperature values ​​can be extracted from complex CAN messages.

[0039] In this embodiment, when the motor temperature or the MOS temperature exceeds the preset temperature monitoring range, the calibration process is paused and temperature adjustment is performed to obtain the adjusted motor and MOS temperatures. This means that during the calibration process, the calibration host computer continuously compares the parsed motor and MOS temperatures with the preset safe temperature range. This preset temperature monitoring range may include an upper limit and a lower limit, designed to protect the motor and power devices from overheating or overcooling damage and ensure that calibration data is collected under stable and reliable temperature conditions. Once either temperature is detected to exceed this range, the calibration host computer immediately sends a command to the motor controller to interrupt the currently ongoing calibration current command issuance, thereby pausing the calibration process. Subsequently, the system will activate the corresponding temperature adjustment strategy. For example, if the temperature is too high, the motor can be controlled to stop running or run under no-load to cool down naturally; if the temperature is too low, a small current can be applied for preheating. During temperature adjustment, the host computer continuously monitors temperature changes until both the motor and MOS temperatures return to the preset monitoring range. Only then will the system resume the paused calibration process and continue data acquisition.

[0040] In this embodiment, through the above technical solution, this application can acquire the operating temperature of the motor and MOSFETs in real time and accurately. When the temperature exceeds the preset safety monitoring range, the system can promptly pause the calibration process and activate the corresponding temperature adjustment mechanism, effectively avoiding data acquisition under abnormal temperature conditions, thereby ensuring the accuracy and reliability of the calibration data. This not only protects the motor and controller hardware from overheating or overcooling damage and extends equipment life, but also improves the stability and safety of the entire automatic calibration process, laying a solid foundation for the subsequent generation of high-quality full-speed control parameters.

[0041] In one feasible implementation, when the motor temperature or the MOS temperature exceeds the preset temperature monitoring range, the calibration process is paused and temperature adjustment is performed to obtain the adjusted motor temperature and MOS temperature. This includes: when the temperature is detected to exceed the upper limit of the set temperature range, the motor is controlled to output zero current to cool down and wait, in order to obtain the cooled temperature; when the temperature is detected to be below the lower limit of the set temperature range, the motor is controlled to output a preset d-axis current to heat up, in order to obtain the heated temperature; when the temperature returns to the preset temperature monitoring range, the calibration process is resumed and data acquisition continues.

[0042] In this embodiment, when the detected temperature exceeds the upper limit of the set temperature range, the system controls the motor to output zero current for cooling and waiting to obtain the cooled temperature. "Controlling the motor to output zero current" here means setting the current command applied to the motor windings to zero through the motor controller, thereby stopping the motor's torque output and internal heat generation. This allows the motor to dissipate heat to the environment through natural convection, radiation, or forced air cooling, achieving passive cooling. During the cooling waiting process, the motor temperature and MOS temperature are continuously monitored until they fall back within the preset temperature monitoring range.

[0043] On the other hand, when the detected temperature is below the lower limit of the set temperature range, the system controls the motor to output a preset amount of d-axis current to perform a heating operation, thereby achieving the desired temperature. Here, "controlling the motor to output a preset amount of d-axis current" means actively heating the motor by injecting current into the d-axis, utilizing the resistive losses (I²R losses) of the motor windings, without generating or generating very little torque. The d-axis current is mainly used for field-oriented control, and the heat loss it generates can effectively increase the temperature of the motor body and MOSFETs. The preset amount of d-axis current can be pre-set according to the motor characteristics and the required heating rate to ensure that the heating process is controllable and efficient.

[0044] In this embodiment, when the motor temperature or MOS temperature returns to the preset temperature monitoring range, the system will resume the calibration process and continue data acquisition. This means that once the temperature conditions meet the calibration requirements, the previously paused data acquisition task will restart, ensuring the continuity and integrity of the calibration process.

[0045] In this embodiment, through the above technical solution, this application provides a refined temperature regulation strategy during the automatic calibration of a BLDC motor, addressing situations where the motor temperature or MOS temperature exceeds the preset monitoring range. When the temperature is too high, passive cooling is achieved by controlling the motor to output zero current, effectively avoiding the risk of overheating and ensuring equipment safety. When the temperature is too low, active heating is achieved by controlling the motor to output a preset d-axis current, ensuring the motor operates within its optimal operating temperature range, thereby avoiding the impact of low temperature on calibration accuracy and efficiency. This intelligent temperature management mechanism not only quickly and accurately restores the motor temperature to a suitable range, shortening calibration interruption time caused by temperature anomalies and improving the continuity and efficiency of the calibration process, but also ensures that the raw calibration data collected throughout the calibration process is obtained under stable and controlled temperature conditions, thereby greatly improving the accuracy and reliability of the calibration data and laying a solid foundation for the subsequent derivation of full-speed control parameters.

[0046] In one feasible implementation, refer to Figure 3 Step S200 includes steps S210 to S240, wherein: Step S210: Input the original calibration data into the data filtering module for filtering processing, remove data points that exceed the preset range, and obtain the filtered calibration data; Step S220: Input the filtered calibration data into the data optimization and derivation module, and calculate the optimal combination of current parameters under the calibration speed based on the maximum torque-current ratio algorithm to obtain the calibration speed parameters; Step S230: Based on the calibrated speed parameters and the electromagnetic theory model of the motor, derive the current parameter combinations corresponding to different speed and torque conditions within the full speed range to obtain the full speed derived parameters; Step S240: Integrate the calibrated speed parameters and the full speed derived parameters into full speed control parameters.

[0047] In this embodiment, the original calibration data is input into a data filtering module for filtering, removing data points that exceed a preset range to obtain filtered calibration data. In actual data acquisition, due to sensor noise, environmental interference, or transient anomalies, the original calibration data may contain inaccurate or outlier data points. The data filtering module aims to identify and remove these outlier data points, ensuring the accuracy and reliability of subsequent data processing. Specifically, a reasonable physical range can be preset, such as setting upper and lower limits for torque, current, and voltage based on the motor's rated parameters, sensor accuracy, or empirical values. Any data point exceeding these preset ranges is considered an outlier and removed. Filtering can be implemented using various algorithms, such as median filtering or moving average filtering to smooth the data and eliminate transient spikes; or using statistical methods, such as setting data points that deviate from the mean by more than a specific standard deviation to be considered outliers and removed. Through this step, cleaner and more reliable filtered calibration data can be obtained.

[0048] In this embodiment, the filtered calibration data is input into the data optimization and derivation module, which calculates the optimal current parameter combination at the calibration speed based on the maximum torque-to-current ratio algorithm to obtain the calibration speed parameters. After obtaining accurate filtered data, it is necessary to find the current parameter combination that maximizes the motor's operating efficiency or performance at a specific calibration speed. The maximum torque-to-current ratio (MTPA) algorithm is an optimization strategy widely used in permanent magnet synchronous motor control. Its core objective is to minimize the total current amplitude of the motor under a given output torque, thereby maximizing the motor's operating efficiency. In specific implementation, for each filtered calibration data point, the corresponding torque-to-current ratio can be calculated based on the recorded actual torque and actual current. Then, among all calibration data points, the combination of d-axis current and q-axis current corresponding to the data point with the maximum torque-to-current ratio is selected as the optimal current parameter combination at that calibration speed, which is the calibration speed parameter.

[0049] In this embodiment, based on the calibration speed parameters and the motor electromagnetic theory model, the current parameter combinations corresponding to different speed and torque conditions across the entire speed range are derived to obtain the full-speed derived parameters. Since actual calibration is usually only performed at limited speed points and torque conditions, to achieve accurate and efficient control of the motor across the entire speed range, these limited calibration data need to be extended to the entire operating range. Therefore, it is first necessary to obtain the inherent parameters of the motor, such as motor resistance, motor inductance, and back electromotive force coefficient. Then, based on these inherent parameters and the calibration speed parameters obtained through the MTPA algorithm, an electromagnetic theory model of the BLDC motor is established or calibrated. This model can include the motor's voltage equation, torque equation, and flux linkage equation. Using this motor electromagnetic theory model, through simulation or analytical calculation, for each discrete speed-torque point across the entire speed range (e.g., from zero speed to maximum speed) and the entire torque range (e.g., from zero torque to maximum torque), combinations of d-axis and q-axis currents that satisfy MTPA or other optimization objectives can be derived. These derived current parameter combinations are the full-speed derived parameters.

[0050] In this embodiment, the calibrated speed parameters and the full-speed derived parameters are integrated into full-speed control parameters. This step aims to merge the optimal parameters obtained through actual calibration with the full-range parameters obtained through model derivation, forming a complete, accurate, and comprehensive set of control parameters. During the integration process, for speed points and torque conditions that have been actually calibrated, the calibrated speed parameters optimized based on actual measurement data can be used preferentially because they have higher accuracy. For operating points that have not been actually measured or are only derived from models, the full-speed derived parameters are used. To ensure a smooth transition of parameters between different speed and torque points and avoid abrupt changes in control parameters, it may be necessary to smooth or interpolate the integrated data. Finally, the resulting dataset is the full-speed control parameters, which can be used to generate a lookup table that the motor controller can recognize.

[0051] In this embodiment, by filtering the original calibration data, measurement noise and abnormal data points are effectively eliminated, improving data quality and reliability. Based on this, the maximum torque-to-current ratio algorithm is used to optimize the filtered calibration data, enabling precise calculation of the optimal combination of current parameters to achieve the highest efficiency or best performance of the motor at a specific calibration speed. Furthermore, by combining the motor's electromagnetic theory model, these optimal parameters obtained at limited calibration points are extended to all speed and torque conditions, thus overcoming the limitations of the actual calibration data coverage. Finally, by integrating the optimal parameters obtained from actual calibration with the full-range parameters derived from the model, this application can generate a comprehensive, accurate, and efficient set of full-speed control parameters, ensuring precise control and high-efficiency operation of the motor under various operating conditions, thereby improving the overall performance and reliability of the BLDC motor.

[0052] In one feasible implementation, refer to Figure 4 Step S220 includes steps S221 to S223, wherein: Step S221: Extract the actual torque corresponding to different current combinations from the filtered calibration data; Step S222: Calculate the torque-current ratio for each current combination based on the actual torque; Step S223: Compare the torque-current ratios of all current combinations, and select the current parameter combination corresponding to the maximum torque-current ratio as the calibration speed parameter.

[0053] In this embodiment, the actual torque corresponding to different current combinations is extracted from the filtered calibration data. The filtered calibration data can be stored in a structured form, containing information such as the actual torque signal output by the dynamometer, the actual current fed back by the motor controller, and the actual voltage, all synchronously acquired after the data acquisition module sends different calibration current commands to the motor controller at a specific calibration speed. The extraction process involves parsing this structured data, associating each specific current combination (e.g., a combination of d-axis and q-axis currents) with its corresponding actual torque value. This can be achieved by traversing data records, applying data parsing algorithms, or utilizing data query mechanisms, ensuring the accuracy of the data required for subsequent calculations.

[0054] In this embodiment, the torque-to-current ratio (TVR) of each current combination is calculated based on the actual torque. The TVR is a key indicator of motor performance, reflecting the torque generated per unit current and is closely related to motor efficiency. For each extracted current combination and its corresponding actual torque, the TVR can be calculated as: Torque-to-current ratio = Actual torque / Total current amplitude. The total current amplitude can be calculated using the square root of the sum of the squares of the d-axis and q-axis currents. This method quantifies the torque generation efficiency of each current combination.

[0055] In this embodiment, the torque-to-current ratio of all current combinations is compared, and the current parameter combination corresponding to the maximum torque-to-current ratio is selected as the calibration speed parameter. After calculating the torque-to-current ratio of all current combinations, the system compares these ratios and identifies the largest torque-to-current ratio. The current parameter combination corresponding to this maximum torque-to-current ratio (i.e., specific d-axis and q-axis current values) is determined as the optimal current parameter combination at the calibration speed and used as the calibration speed parameter. This selection process ensures that the motor can operate with the highest efficiency or the maximum torque output at the current calibration speed, providing an optimal reference point for the subsequent derivation of control parameters across the entire speed range.

[0056] In this embodiment, through the above technical solution, this application can systematically evaluate the performance of different current combinations from the filtered calibration data. By calculating the torque-to-current ratio of each current combination and selecting the largest one, the current parameter combination that enables the motor to operate with the highest efficiency or maximum torque output at a specific calibration speed can be accurately identified. This not only ensures the accuracy and optimality of the calibration speed parameters, providing a solid foundation for subsequent derivation of control parameters across the entire speed range based on the motor's electromagnetic theory model, but also avoids suboptimal control parameters caused by empirical judgment or simple data screening, thereby improving the overall efficiency and performance of the BLDC motor in actual operation.

[0057] In one feasible implementation, refer to Figure 5 Step S230 includes steps S231 to S233, wherein: Step S231: Obtain the inherent parameters of the motor, including motor resistance, motor inductance, and back electromotive force coefficient; Step S232: Based on the calibrated speed parameters and the inherent parameters, establish an electromagnetic theoretical model of the motor; Step S233: Calculate the d-axis current and q-axis current combinations under different speed and torque conditions within the full speed range based on the electromagnetic theory model of the motor, so as to obtain the full speed derivation parameters.

[0058] In this embodiment, the inherent parameters of the motor refer to the physical characteristics inherent in the motor body. These parameters remain unchanged during motor operation and form the basis for establishing an accurate mathematical model of the motor. Among them, motor resistance mainly refers to the DC resistance of the motor windings, which can be measured using specialized resistance testing equipment, such as the DC bridge method or the four-wire method. Motor inductance refers to the inductance value of the motor windings. For permanent magnet synchronous motors (BLDC motors can be considered square-wave permanent magnet synchronous motors), it is necessary to distinguish between d-axis inductance and q-axis inductance. These inductance values ​​can be measured using an inductance tester at a specific frequency, or obtained by applying an AC signal and analyzing the voltage and current response. The back electromotive force coefficient is a parameter that measures the motor's ability to generate back electromotive force during rotation. It can be calculated by driving the motor at a constant speed under no-load conditions and measuring the effective or peak value of its output line voltage, combined with the rotational speed. Accurate acquisition of these inherent parameters is a prerequisite for subsequently establishing the electromagnetic theoretical model of the motor.

[0059] In this embodiment, the electromagnetic theoretical model of the motor is a set of mathematical expressions describing the electrical and mechanical behavior of the BLDC motor. It reflects the relationship between voltage, current, flux linkage, and torque under different operating conditions. In the dq synchronous rotating coordinate system, the voltage equation, flux linkage equation, and torque equation of the BLDC motor constitute the core of its electromagnetic theoretical model. By substituting the acquired inherent parameters of the motor into these equations, and combining them with the optimal d-axis current and q-axis current combination (i.e., the calibration speed parameters) obtained through the maximum torque-to-current ratio algorithm at a specific calibration speed, the model can be verified and corrected to ensure its accuracy.

[0060] In this embodiment, after establishing an accurate electromagnetic theoretical model of the motor, this model can be used to derive the optimal d-axis and q-axis current combinations for the motor under any speed and torque conditions across the entire speed range. Specifically, a speed-torque grid covering all possible operating states of the motor needs to be defined first. For each speed-torque point in the grid, the target speed and target torque are used as inputs. Combining the electromagnetic theoretical model of the motor, the optimal d-axis and q-axis current combinations that can generate the target torque and meet the motor voltage and current limits are calculated in reverse by solving a system of equations or using numerical optimization methods (e.g., based on the maximum torque-to-current ratio (MTPA) control strategy or field weakening control (FWC) strategy). For example, in the low-speed, high-torque region, the MTPA strategy is mainly used to maximize the torque-to-current ratio; in the high-speed, field-weakening region, a negative d-axis current is applied to weaken the magnetic field to expand the speed range. These calculation results are the full-speed derived parameters, which can provide accurate current control basis for the motor at operating points that have not been actually calibrated.

[0061] In this embodiment, by accurately acquiring the inherent parameters of the motor and establishing a highly accurate electromagnetic theoretical model of the motor based on these parameters and existing calibration speed parameters, the model can truly reflect the operating characteristics of the motor. Based on this model, the optimal d-axis and q-axis current combinations under different speed and torque conditions across the entire speed range can be systematically calculated. This not only fills the gaps in the uncalibrated regions of the actual calibration data, ensuring that the motor obtains accurate current control parameters at all operating points, but also avoids problems such as low motor operating efficiency, decreased control accuracy, or slow dynamic response caused by inaccurate parameters. Ultimately, this solution improves the control performance, operating efficiency, and stability of the BLDC motor throughout its entire operating range.

[0062] In one feasible implementation, refer to Figure 6 Step S300 includes steps S310 to S330, wherein: Step S310: Input the full speed control parameters into the table generation module and format them according to a one-dimensional or two-dimensional index format to obtain the formatted lookup table data; Step S320: Convert the formatted lookup data into a data format that the motor controller can recognize in order to obtain a lookup table compatible with the controller; Step S330: Write the lookup table compatible with the controller into the storage unit of the motor controller.

[0063] In this embodiment, the full-speed control parameters are obtained after data optimization and derivation, representing the optimal combination of current parameters for the BLDC motor under different speed and torque conditions across the entire speed range, such as the combination of d-axis current and q-axis current. These are fundamental data for efficient motor operation and can exist as a set of numerical values. The table generation module is a software or hardware component that receives the full-speed control parameters and organizes them into a structured data format according to preset rules. This module can be integrated into the motor automatic calibration host computer, responsible for the initial processing of the data format. One-dimensional or two-dimensional index formats are data organization methods for lookup tables. A one-dimensional index format uses a single variable (such as speed) as an index, corresponding to a set of control parameters; a two-dimensional index format uses two variables (such as speed and torque) as indexes, forming a grid-like lookup structure, with each grid point corresponding to a set of control parameters. The choice of this format depends on the complexity of the controller and the required control accuracy. The formatting process aims to arrange, interpolate, or discretize the original full-speed control parameters according to the selected one-dimensional or two-dimensional index format, making them conform to the structural requirements of a lookup table. For example, continuous parameter values ​​can be mapped to discrete table index points to generate well-structured, easy-to-find formatted lookup data.

[0064] In this embodiment, the data format recognizable by the motor controller refers to the specific data structure and encoding method that the microprocessor or digital signal processor (DSP) inside the motor controller can directly read, parse, and use. For example, some controllers may require data to be stored in a specific byte order, data type (such as fixed-point number, floating-point number), or memory alignment. The controller-compatible lookup table is the final lookup data that has been formatted and converted into a data format recognizable by the motor controller. It fully conforms to the hardware and software interface specifications of the target motor controller and can be directly loaded into the controller's storage unit without requiring additional runtime conversion by the controller. The step of writing to the motor controller's storage unit involves transmitting and storing the controller-compatible lookup table through a specific communication interface (such as CAN, SPI, UART, or JTAG) into the non-volatile memory (such as Flash memory, EEPROM) inside the motor controller. This ensures that the lookup table is retained after the controller is powered off and is loaded and used by the controller the next time it is powered on.

[0065] In this embodiment, the full-speed control parameters are formatted according to a one-dimensional or two-dimensional index format using a table generation module, resolving the mismatch between the original parameters and the controller's storage structure. Subsequently, the formatted lookup data is converted into a data format recognizable by the motor controller, ensuring correct data parsing and use within the controller and avoiding control errors or performance degradation caused by data format incompatibility. Finally, the controller-compatible lookup table is written into the motor controller's storage unit, enabling the controller to quickly and accurately retrieve the required current control parameters during motor operation. This not only simplifies the controller's computational burden during real-time operation and improves the response speed and accuracy of the control system, but also ensures that the optimized control strategy obtained based on the automatic calibration method can be seamlessly deployed to the actual motor control system, thereby improving the overall operating efficiency and performance stability of the BLDC motor.

[0066] In one feasible implementation, the method further includes: acquiring motor efficiency test data; visualizing the motor efficiency test data to generate an efficiency heatmap; and marking the maximum efficiency point and the efficiency area above a preset efficiency threshold in the efficiency heatmap.

[0067] In this embodiment, acquiring motor efficiency test data refers to a series of data obtained during motor operation by measuring the motor's input power (e.g., the actual current and voltage fed back by the motor controller) and output power (e.g., the product of the actual torque signal output by the dynamometer and the motor speed). This data can be used to calculate the motor's efficiency under different speed and torque conditions. This data can be acquired after the calibration process is completed, or it can be collected synchronously during the calibration process to ensure consistency between the data and the calibration conditions.

[0068] In this embodiment, the motor efficiency test data is visualized to generate an efficiency heatmap. Visualization refers to presenting abstract efficiency data graphically, making it easier to understand and analyze. An efficiency heatmap is a commonly used visualization tool that uses color depth or gradients to represent motor efficiency values ​​under different combinations of speed and torque. For example, motor speed can be used as the X-axis, motor torque as the Y-axis, and efficiency values ​​represented by a color map along the Z-axis. This type of chart can intuitively display the efficiency distribution of the motor throughout its entire operating range.

[0069] Based on this, the maximum efficiency point and the efficiency range above the preset efficiency threshold are marked on the efficiency heatmap. The maximum efficiency point refers to the combination of speed and torque corresponding to the highest efficiency value that the motor can achieve under all test conditions. The preset efficiency threshold is a lower limit of efficiency set according to actual application requirements or design goals; the area above this threshold is considered a high-efficiency operating area. By marking the maximum efficiency point on the heatmap, for example, using specific symbols (such as asterisks or circles) or using different colors or shaded areas to highlight the area above the efficiency threshold, users can quickly identify the motor's optimal operating state and efficient operating range.

[0070] In this embodiment, through the above technical solution, after the BLDC motor is automatically calibrated, not only are control parameters obtained, but the motor's efficiency performance can also be intuitively evaluated. Acquiring motor efficiency test data and generating an efficiency heatmap makes the efficiency distribution of the motor under different speed and torque conditions clear at a glance, greatly improving the depth of understanding of motor performance. Furthermore, by marking the maximum efficiency point and efficiency areas above the preset efficiency threshold on the heatmap, users can quickly identify the most energy-efficient and economical operating conditions of the motor, providing clear guidance for optimizing motor control strategies and selecting appropriate operating modes. This not only helps improve the overall energy efficiency of the motor system and reduce operating costs, but also assists in motor design verification and fault diagnosis, ensuring that the motor always maintains optimal operating conditions in practical applications.

[0071] In one feasible implementation, the step of visualizing the motor efficiency test data to generate an efficiency heatmap includes: gridding the motor efficiency test data according to the speed and torque dimensions to obtain gridded efficiency data; generating a three-dimensional heatmap of speed-torque-efficiency based on the gridded efficiency data; and drawing efficiency equipotential lines in the three-dimensional heatmap to obtain the efficiency heatmap.

[0072] In this embodiment, to more clearly display the motor efficiency data, the motor efficiency test data is first gridded according to the speed and torque dimensions to obtain gridded efficiency data. Specifically, gridding involves mapping the discrete, potentially irregularly distributed motor efficiency test data onto a predefined, uniformly distributed two-dimensional speed-torque grid using mathematical methods such as interpolation and fitting. For example, linear interpolation, bilinear interpolation, or cubic spline interpolation algorithms can be used. For a given speed and torque range, it is divided into several small rectangular regions, i.e., grid cells. For each grid cell, if it contains multiple original data points, their average or weighted average can be taken as the efficiency value of that grid; if there are no data points in the grid, interpolation can be performed using data points from adjacent grids. This processing method can transform irregular raw data into a structured dataset that is easy to visualize, laying the foundation for subsequent accurate analysis.

[0073] Based on this, a three-dimensional heatmap of speed-torque-efficiency is generated using the gridded efficiency data. A three-dimensional heatmap is a visualization method that represents the relationship between three variables (speed, torque, and efficiency) using color and spatial location. It can intuitively show the efficiency performance of a motor under different combinations of speed and torque. For example, using professional plotting software libraries, the gridded speed and torque can be used as the X and Y axes respectively, and efficiency as the Z axis, with color depth or color bands representing the magnitude of the efficiency values. High-efficiency areas can be represented by warm colors (such as red and yellow), while low-efficiency areas can be represented by cool colors (such as blue and green). This chart provides an intuitive, global overview of efficiency, allowing users to quickly understand the efficiency distribution trend of the motor throughout its operating range.

[0074] In this embodiment, to further enhance visualization and analytical accuracy, efficiency equipotential lines are drawn in the 3D heatmap to obtain an efficiency heatmap. Efficiency equipotential lines, also known as contour lines, are curves connecting points with the same efficiency value on the 2D projection plane of the 3D heatmap. They clearly delineate the boundaries of different efficiency levels, allowing users to more accurately identify the shape and range of high-efficiency regions. For example, based on the generated 3D heatmap, contour lines of preset efficiency values ​​(e.g., 80%, 85%, 90%) can be drawn on the speed-torque plane using a contour line algorithm. These equipotential lines can be superimposed on the heatmap or presented as part of the heatmap. Through these equipotential lines, users can clearly see which speed-torque combinations achieve specific efficiency levels, thereby assisting in optimizing motor operating strategies and accurately defining high-efficiency operating areas.

[0075] In this embodiment, firstly, the discrete motor efficiency test data is gridded, effectively transforming irregular data points into a structured, continuous efficiency distribution. This eliminates the sparsity or unevenness that may exist in the original data, providing a solid data foundation for subsequent precise visualization and analysis. Secondly, a three-dimensional heatmap of speed-torque-efficiency is generated based on the gridded data. This heatmap comprehensively displays the motor's efficiency performance across the entire speed and torque operating range in an intuitive color gradient format, allowing users to quickly grasp the overall efficiency characteristics of the motor. Furthermore, efficiency equipotential lines are drawn on the three-dimensional heatmap, clearly outlining the boundaries of different efficiency levels. This allows for the precise identification and location of the shape, range, and optimal operating point of high-efficiency regions. This refined visualization method not only solves the problem of simple heatmaps failing to accurately identify efficiency distribution patterns but also greatly improves the accuracy and efficiency of motor performance analysis, providing strong technical support for optimizing motor operating strategies, defining high-efficiency operating areas, and ultimately achieving energy conservation and consumption reduction.

[0076] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. An automatic calibration method for a BLDC motor, characterized in that, The method includes: The original calibration data of the calibration speed is obtained by the host computer based on the automatic calibration of the motor; the original calibration data includes the actual torque signal output by the dynamometer, the actual current and actual voltage fed back by the motor controller; The original calibration data is input into the calibration data processing tool for data optimization and derivation to obtain full speed control parameters; A lookup table that can be recognized by the motor controller is generated based on the full speed control parameters, and the lookup table is written into the storage unit of the motor controller; When the motor is running, the current control parameters are obtained by retrieving the lookup table based on the real-time speed and target torque of the motor through the controller's lookup table function. The current control parameters are used as input to the current loop, and the output current command drives the motor to run. The steps for obtaining the raw calibration data of the calibration speed based on the automatic motor calibration host computer include: The calibration speed is set by the dynamometer, and the calibration current parameter range and temperature monitoring range are preset by the parameter configuration module to generate an automatic calibration current combination table. Based on the automatic calibration current combination table, a calibration current command is sent to the motor controller through the data acquisition module; The system simultaneously acquires the actual torque signal output by the dynamometer, the actual current and voltage fed back by the motor controller, and obtains the motor temperature and MOS temperature. The collected actual torque signal, actual current, actual voltage, motor temperature, and MOS temperature are integrated into the original calibration data for the calibration speed.

2. The automatic calibration method for BLDC motors as described in claim 1, characterized in that, The steps to obtain the motor temperature and MOSFET temperature include: Receive CAN messages sent by the motor controller in real time via the CAN bus; The motor temperature and MOS temperature were extracted from the CAN message; When the motor temperature or the MOS temperature exceeds the preset temperature monitoring range, the calibration process is paused and temperature adjustment is performed to obtain the adjusted motor temperature and MOS temperature.

3. The automatic calibration method for BLDC motors as described in claim 2, characterized in that, When the motor temperature or the MOS temperature exceeds the preset temperature monitoring range, the calibration process is paused and temperature adjustment is performed to obtain the adjusted motor temperature and MOS temperature. The steps include: When the detected temperature exceeds the upper limit of the set temperature range, the motor is controlled to output zero current to cool down and wait, in order to obtain the temperature after cooling. When the detected temperature is lower than the lower limit of the set temperature range, the motor is controlled to output a preset d-axis current to perform a heating operation in order to obtain the heated temperature. When the temperature returns to the preset temperature monitoring range, the calibration process resumes and data acquisition continues.

4. The automatic calibration method for BLDC motors as described in claim 1, characterized in that, The steps of inputting the original calibration data into a calibration data processing tool for data optimization and derivation to obtain full-speed control parameters include: The original calibration data is input into the data filtering module for filtering to remove data points that exceed the preset range, so as to obtain the filtered calibration data. The filtered calibration data is input into the data optimization and derivation module, which calculates the optimal combination of current parameters at the calibration speed based on the maximum torque-current ratio algorithm to obtain the calibration speed parameters. Based on the calibrated speed parameters and the electromagnetic theory model of the motor, the current parameter combinations corresponding to different speed and torque conditions in the full speed range are derived to obtain the full speed derived parameters. The calibrated speed parameters and the full speed derived parameters are integrated into full speed control parameters.

5. The automatic calibration method for BLDC motors as described in claim 4, characterized in that, The steps of inputting the filtered calibration data into the data optimization and derivation module, and calculating the optimal combination of current parameters at the calibration speed based on the maximum torque-current ratio algorithm to obtain the calibration speed parameters include: Extract the actual torque corresponding to different current combinations from the filtered calibration data; The torque-current ratio for each current combination is calculated based on the actual torque. Compare the torque-current ratios of all current combinations, and select the current parameter combination corresponding to the maximum torque-current ratio as the calibration speed parameter.

6. The automatic calibration method for BLDC motors as described in claim 4, characterized in that, Based on the calibrated speed parameters and the electromagnetic theoretical model of the motor, the steps for deriving the current parameter combinations corresponding to different speed and torque conditions across the entire speed range to obtain the full-speed derived parameters include: Obtain the inherent parameters of the motor, including motor resistance, motor inductance, and back electromotive force coefficient; Based on the calibrated speed parameters and the inherent parameters, an electromagnetic theoretical model of the motor is established; Based on the electromagnetic theory model of the motor, the d-axis current and q-axis current combinations under different speed and torque conditions within the full speed range are calculated to obtain the full speed derivation parameters.

7. The automatic calibration method for BLDC motors as described in claim 1, characterized in that, The steps of generating a lookup table recognizable by the motor controller based on the full-speed control parameters and writing the lookup table into the storage unit of the motor controller include: The full-speed control parameters are input into the table generation module and formatted according to a one-dimensional or two-dimensional index format to obtain the formatted lookup table data. The formatted lookup data is converted into a data format that the motor controller can recognize in order to obtain a lookup table compatible with the controller; Write the lookup table compatible with the controller into the storage unit of the motor controller.

8. The automatic calibration method for a BLDC motor as described in claim 1, characterized in that, The method further includes: Obtain motor efficiency test data; The motor efficiency test data is visualized to generate an efficiency heatmap. The efficiency heatmap marks the point of maximum efficiency and the efficiency area above the preset efficiency threshold.

9. The automatic calibration method for a BLDC motor as described in claim 8, characterized in that, The steps for visualizing the motor efficiency test data and generating an efficiency heatmap include: The motor efficiency test data is processed into a grid according to the dimensions of speed and torque to obtain gridded efficiency data; A three-dimensional heat map of speed-torque-efficiency is generated based on the gridded efficiency data; Efficiency equipotential lines are plotted in the three-dimensional heat map to obtain an efficiency heat map.

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