Self-learning control method, device and equipment for air door motor of vehicle-mounted air conditioning cabinet and storage medium

Abstract

The embodiment of the invention relates to the related technical field of vehicles, and discloses a self-learning control method, device and equipment for an air door motor of a vehicle-mounted air conditioning cabinet and a storage medium, and the method comprises the steps that control instructions are sent to the air door motor in the vehicle-mounted air conditioning cabinet at intervals for multiple times; wherein the control instructions are used for instructing the air door motor to drive the air door blades to run to target positions, the control instructions comprise a first control instruction and a second control instruction, and when the target position instructed by the first control instruction is a full-open position, the target position instructed by the second control instruction is a full-closed position; when the target position indicated by the first control instruction is a fully closed position, the target position indicated by the second control instruction is a fully open position; and calibrating the air door motor according to the execution result of the control instruction. By means of the technical scheme, the accuracy and comprehensiveness of calibration data can be improved, and therefore the long-term precision, consistency and reliability of air door control are improved.

Description

Technical Field

[0001] This application relates to vehicle-related technical fields, specifically to a self-learning control method, device, equipment, and storage medium for a damper motor of an in-vehicle air conditioning unit. Background Technology

[0002] To improve the accuracy of vehicle air conditioning temperature control, it is usually necessary to determine the fully open and fully closed positions of the damper blades (i.e., zero position and end of stroke) through the self-learning process of the damper motor before formal control.

[0003] In existing technologies, the self-learning process of damper motors typically employs a single forward and reverse rotation learning strategy. However, due to the complex operating environment of vehicles (such as temperature changes, mechanical wear, and foreign object jamming), damper motors are prone to jamming and step loss during the self-learning process, leading to inaccurate damper position and consequently affecting the accuracy and reliability of the self-learning results. Summary of the Invention

[0004] In view of the above problems, this application provides a self-learning control method, device, equipment and storage medium for the damper motor of a vehicle air conditioning unit, which is used to solve the problems in the prior art that the damper motor is prone to jamming and step loss during the self-learning process, resulting in inaccurate damper position, and thus affecting the accuracy and reliability of the self-learning results.

[0005] According to one aspect of the embodiments of this application, a self-learning control method for the damper motor of a vehicle air conditioning unit is provided, the method comprising:

[0006] Multiple control commands are sent intermittently to the damper motor in the vehicle air conditioning unit; wherein, the control commands are used to instruct the damper motor to drive the damper blades to a target position, the control commands include a first control command and a second control command, when the target position indicated by the first control command is a fully open position, the target position indicated by the second control command is a fully closed position; when the target position indicated by the first control command is a fully closed position, the target position indicated by the second control command is a fully open position.

[0007] The damper motor is calibrated based on the execution result of the control command.

[0008] According to another aspect of the embodiments of this application, a self-learning control device for a damper motor of a vehicle air conditioning unit is provided, the device comprising:

[0009] The first processing unit is used to send control commands to the damper motor in the vehicle air conditioning unit at intervals multiple times; wherein, the control commands are used to instruct the damper motor to drive the damper blades to a target position, the control commands include a first control command and a second control command, when the target position indicated by the first control command is a fully open position, the target position indicated by the second control command is a fully closed position; when the target position indicated by the first control command is a fully closed position, the target position indicated by the second control command is a fully open position.

[0010] The second processing unit is used to calibrate the damper motor based on the execution result of the control command.

[0011] According to another aspect of the embodiments of this application, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0012] The memory is used to store at least one executable instruction, which causes the processor to perform the operation of the self-learning control method for the damper motor of the vehicle air conditioning unit as described above.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing an electronic device / apparatus to perform the operation of the self-learning control method for the damper motor of the vehicle air conditioning unit as described above.

[0014] This application's embodiments avoid motor overheating and mechanical wear by sending data intermittently, extending service life and ensuring data acquisition stability. Through bidirectional stroke verification and a dynamic fault-tolerant mechanism based on multiple command transmissions, the damper stroke is fully covered, improving the accuracy and comprehensiveness of calibration data. Furthermore, the self-learning calibration process requires no manual intervention and can adapt to characteristic drift caused by manufacturing tolerances, aging, temperature changes, etc., of the motor and transmission mechanism, thereby significantly improving the long-term accuracy, consistency, and reliability of damper control.

[0015] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0016] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0017] Figure 1 A schematic diagram of a motor-controlled damper for a vehicle air conditioning unit provided in an embodiment of this application;

[0018] Figure 2 A flowchart illustrating a self-learning control method for a damper motor of a vehicle air conditioning unit, provided in an embodiment of this application;

[0019] Figure 3 A flowchart illustrating another self-learning control method for the damper motor of a vehicle air conditioning unit provided in this application embodiment;

[0020] Figure 4 A flowchart illustrating another self-learning control method for the damper motor of a vehicle air conditioning unit provided in this application embodiment;

[0021] Figure 5 A schematic diagram of the structure of a self-learning control device for a damper motor of a vehicle air conditioning unit provided in this application embodiment;

[0022] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0024] With the increasing demand for intelligent and comfortable vehicles, vehicle air conditioning systems need to support independent temperature control in multiple zones to meet the personalized temperature needs of different passengers. For example, in a four-zone air conditioning system, the driver's seat, front passenger seat, second row left, and second row right each have independent air vents for blowing air onto the face and feet (a total of 8 vents). Each vent is driven by a motor controlled by a LIN bus to open and close the damper. The air conditioning controller coordinates the forward and reverse rotation of each LIN motor according to the airflow command to achieve precise temperature control.

[0025] For example, Figure 1 A schematic diagram of a motor-controlled damper for a vehicle air conditioning unit provided in this application embodiment is shown below. Figure 1 As shown, the left rear foot blowing damper is controlled by the left rear foot blowing actuator, the right rear foot blowing damper is controlled by the right rear foot blowing actuator, the left rear face blowing damper is controlled by the left rear face blowing actuator, and the right rear face blowing damper is controlled by the right rear face blowing actuator.

[0026] When a vehicle is powered on / off or the system is initialized, the damper motor typically needs to determine the fully open and fully closed positions of the damper (i.e., zero position and travel end point) through a self-learning process to ensure the accuracy of subsequent control commands. In existing technologies, the self-learning process of the air conditioning unit's LIN control motor usually employs a single forward and reverse rotation learning strategy. Specifically, when the vehicle is powered on / off, the controller drives the motor to move the damper blades from the current position to the fully open (or fully closed) position, determines the end point by detecting a stall signal, and sets a reference point. However, due to the complex operating environment of vehicles (such as temperature changes, mechanical wear, and foreign object jamming), the damper motor is prone to jamming and step loss during the self-learning process, leading to inaccurate damper positions. This results in insufficient reliability when dealing with complex operating conditions and cannot meet the high-precision control requirements of multi-zone air conditioning systems for damper positions.

[0027] To address the aforementioned issues, this application provides a self-learning control method for a damper motor in a vehicle air conditioning unit. This method involves periodically sending multiple control commands to the damper motor within the unit and calibrating the motor based on the execution results of these commands. Through bidirectional stroke verification and a dynamic fault-tolerance mechanism, the method solves the problem of inaccurate motor position calibration caused by mechanical jamming or signal interference in existing technologies. This achieves precise position calibration of the LIN-controlled motor during the self-learning process, improving the reliability of the calibration results and thus enhancing the accuracy and reliability of damper position control.

[0028] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0029] It should be noted that the execution subject of the self-learning control method for the damper motor of the vehicle air conditioning unit provided in the embodiments of the present invention can be a self-learning control device for the damper motor of the vehicle air conditioning unit. The self-learning control device can be deployed on electronic devices such as vehicle controllers and air conditioning controllers or other vehicle terminal devices. The embodiments of this application do not limit this, and the method of this application can be implemented by software, hardware or a combination of software and hardware.

[0030] Figure 2 This is a flowchart illustrating a self-learning control method for the damper motor of a vehicle air conditioning unit, provided in an embodiment of this application. This method can be executed by the air conditioning controller. Figure 2 As shown, the method may include the following steps:

[0031] Step 210: Send control commands to the damper motor in the vehicle air conditioning unit at intervals multiple times.

[0032] The control command is used to instruct the damper motor to drive the damper blades to the target position. The control command includes a first control command and a second control command. When the target position indicated by the first control command is the fully open position, the target position indicated by the second control command is the fully closed position; when the target position indicated by the first control command is the fully closed position, the target position indicated by the second control command is the fully open position.

[0033] For example, in this step, the air conditioning controller can send control commands to the damper motor in the vehicle's air conditioning unit multiple times at preset intervals during vehicle power-on / off or initialization phases for self-learning. The intervalic multiple transmissions mean that there is a time interval between adjacent control commands, rather than continuous transmission. This design avoids overheating or mechanical wear of the motor due to continuous operation, while allowing sufficient time for the system to collect motor response data, thus improving the stability of the self-learning process.

[0034] The control commands instruct the damper motor to drive the damper blades to a target position. The target position includes a fully open damper position and a fully closed damper position, corresponding to the fully open and fully closed states of the damper blades, respectively. The control commands specifically include a first control command and a second control command, which are complementary in terms of the target position. When the first control command indicates a fully open target position, the second control command indicates a fully closed target position; conversely, when the first control command indicates a fully closed target position, the second control command indicates a fully open target position.

[0035] In one example, sending control commands to the damper motor in the vehicle's air conditioning unit at intervals multiple times can include: first, sending a first control command to the damper motor at intervals N times; where N is a positive integer greater than or equal to 1; and then sending a second control command to the damper motor at intervals N times.

[0036] For example, the air conditioning controller can first send a first control command to the damper motor in the vehicle's air conditioning unit at intervals multiple times to perform position calibration in one direction. After completing the position calibration in that direction, it can then send a second control command at intervals multiple times to complete the position calibration in the other direction. In each direction, when sending control commands to the damper motor at intervals multiple times, it can be to execute one control cycle and send multiple (e.g., 3) control commands; or it can be to execute multiple control cycles continuously, with each control cycle sending multiple control commands. This application embodiment does not impose any limitations.

[0037] By sending control commands to the fully open and fully closed positions multiple times, calibration failures due to lost steps can be effectively avoided. This also ensures comprehensive coverage of the damper's travel distance, guaranteeing that the calibration process captures the complete operating characteristics of the motor between its two extreme positions. This improves the comprehensiveness and accuracy of the calibration data, providing a reliable basis for subsequent control. Furthermore, sending control commands intermittently can reduce the motor's thermal load and extend its service life.

[0038] Step 220: Based on the execution result of the control command, calibrate the damper motor.

[0039] For example, in this step, the air conditioning controller calibrates the damper motor based on the damper motor's response to the control command issued in step 210. The execution results include, but are not limited to, feedback information such as the time required for the motor to reach the specified position, the deviation between the actual stopping position and the target position, and the current change curve during motor operation.

[0040] For example, the calibration process may include: recording the actual operating data of the motor after each command is issued, paying particular attention to the motor parameters (such as pulse count, current threshold, etc.) corresponding to the fully open and fully closed positions of the damper blades; calculating the motor control parameters (such as position reference value, travel range, compensation coefficient, etc.) corresponding to the fully open and fully closed positions of the damper; and then storing the calculated calibration parameters in the non-volatile memory of the air conditioning controller for subsequent damper position control. Through the above calibration, the air conditioning system can automatically adapt to different vehicles, different operating environments, or changes in characteristics caused by motor wear, achieving precise control of the damper position.

[0041] The self-learning calibration process requires no manual intervention and can adapt to characteristic drifts in motors and transmission mechanisms caused by manufacturing tolerances, aging, temperature changes, and other factors, significantly improving the long-term reliability and consistency of damper control. Simultaneously, the calibration data can be used for fault diagnosis, such as detecting damper jamming or motor malfunctions.

[0042] In this embodiment, intelligent self-learning control of the vehicle air conditioning damper motor is achieved by sequentially sending control commands for the fully open and fully closed positions to the damper motor at intervals, and automatically completing calibration based on the execution results. Specifically, the interval sending avoids motor overheating and mechanical wear, extending its service life, while ensuring the stability of data acquisition; through bidirectional stroke verification and a dynamic fault-tolerant mechanism based on multiple command sending, the damper stroke is fully covered, improving the accuracy and comprehensiveness of calibration data; in addition, the self-learning calibration process requires no manual intervention and can adapt to characteristic drift of the motor and transmission mechanism caused by manufacturing tolerances, aging, temperature changes, etc., thereby significantly improving the long-term accuracy, consistency, and reliability of damper control.

[0043] Figure 3This is a flowchart illustrating another self-learning control method for the damper motor of a vehicle air conditioning unit provided in an embodiment of this application. This method can be executed by the air conditioning controller. Figure 3 As shown, the method may include the following steps:

[0044] Step 310: Send the first control command to the damper motor N times intermittently.

[0045] Where N is a positive integer greater than or equal to 1, and the specific value can be set according to the system reliability requirements, for example, N=3.

[0046] For example, in this step, the air conditioning controller continuously sends N first control commands to the damper motor at preset time intervals. The first control command instructs the damper motor to move the damper blades from the current position (which is currently an arbitrary, unknown position) to a target position at a preset speed. This target position is either the fully open or fully closed position of the damper blades. When the target position indicated by the first control command is the fully open position, the target position indicated by the second control command is the fully closed position; when the target position indicated by the first control command is the fully closed position, the target position indicated by the second control command is the fully open position.

[0047] This application employs a strategy of sending the same instruction N times at intervals, which constitutes an instruction redundancy mechanism. This can effectively address the problem of single instruction loss or instantaneous interference that may occur in the complex electromagnetic environment of a vehicle, ensuring that the target instruction can be stably and reliably received and executed by the motor, thereby improving the success rate and robustness of self-learning.

[0048] Optionally, the air conditioner controller sends N first control commands to the damper motor at intervals. This can be done by executing one control cycle and sending N (e.g., N=3) first control commands; or by executing multiple (e.g., 2) control cycles consecutively, with each control cycle sending N first control commands. This application embodiment does not impose any limitations.

[0049] Optionally, in one possible embodiment, sending N control commands to the damper motor at intervals may include:

[0050] S1. Within the current control cycle, send N control commands to the damper motor intermittently.

[0051] S2. Clear the abnormal events recorded in the current control cycle; among them, abnormal events include at least motor stall events;

[0052] S3. In the next control cycle, send N control commands to the damper motor intermittently.

[0053] For example, a control cycle is a logical or temporal segment, which can be a fixed duration window or a phase consisting of completing N command transmissions and waiting for their basic responses. For instance, it can be set to 10ms.

[0054] In practical applications, after initiating self-learning, the air conditioning controller can first send N first control commands to the damper motor at preset time intervals within the current control cycle. After the current control cycle ends and before entering the next cycle, the air conditioning controller proactively clears all relevant abnormal events recorded by the system during that cycle. Upon entering the next control cycle, step S1 is repeated, i.e., the first control commands are sent N times again at intervals. This process can be performed a fixed number of times according to a preset strategy, or it can continue until a certain success condition is met (e.g., ending after executing a preset number of cycles (e.g., 2), or ending after executing a maximum number of cycles (e.g., 3) without driving the damper blades to the target position). This embodiment of the application does not impose any limitations on this.

[0055] Among these, abnormal events are abnormal states detected by the system during instruction execution, including at least motor stall events. Motor stall events are typically triggered by a current detection circuit or software algorithm when the motor stops rotating due to excessive mechanical resistance but still has a driving current. This can be determined by detecting voltage, current, and the position of the damper blades, etc., and this application embodiment does not impose limitations. In addition, abnormal events may also include LIN signal loss, mechanical stall, and other faults.

[0056] The in-vehicle environment is complex, and temporary damper jamming (such as brief obstruction by ice particles or foreign objects) may occur, triggering anomalies such as stalling within a single control cycle. If these temporary anomaly records are not cleared, they may be misjudged as permanent faults by the system, thus interrupting the entire self-learning process. Proactively clearing anomaly events within a cycle is equivalent to providing the system with a "reset" opportunity, avoiding process termination due to momentary interference, and significantly enhancing the algorithm's environmental adaptability and robustness.

[0057] This optional embodiment achieves a progressive learning process with self-verification and recovery mechanisms by setting multiple consecutive control cycles. If learning fails due to a temporary fault in one cycle, the system can "start over" in a new cycle without being locked into a historical error state. This design ensures that the self-learning function can eventually overcome sporadic problems and complete reliable calibration, greatly improving the success rate and reliability of the calibration process under complex real-world vehicle conditions.

[0058] Optionally, in one possible embodiment, sending N control commands to the damper motor intermittently may further include: during the process of sending N control commands to the damper motor intermittently, if it is determined that the damper motor has driven the damper blade to the corresponding target position based on the i-th sent control command, then it is determined to perform the step of clearing the abnormal events recorded in the current control cycle; where i is a positive integer greater than or equal to 1 and less than or equal to N.

[0059] For example, the number of command retransmissions and the frame period can be dynamically adjusted based on the LIN bus communication quality to reduce communication resource consumption and improve response efficiency. Specifically, during the process of periodically sending N control commands (e.g., the first control command or the second control command) to the damper motor, the air conditioning controller continuously monitors the execution feedback of the damper motor. For example, by monitoring position sensor signals (e.g., potentiometer voltage stabilizing within the target range) or combining motor status (e.g., current entering the maintenance phase and pulse count stopping increasing), the real-time position of the damper blades is determined. If it is determined that the damper motor has successfully driven the damper blades to the target position indicated by the i-th control command, the step of "clearing the abnormal events recorded in the current control cycle" is immediately executed.

[0060] Where i is a positive integer greater than or equal to 1 and less than or equal to N, meaning that the triggering condition takes effect immediately after any valid instruction is successfully executed from the 1st to the Nth time. When i=1, it means that control is successful on the first instruction, and the exception can be cleared as quickly as possible and the process can proceed to the next stage; when i=N, it means that the first N-1 attempts were not completely successful (possibly due to interference or temporary resistance), and the success was achieved only on the last attempt. In this case, the exception event is cleared at the end of the cycle.

[0061] This optional embodiment avoids the time delay of waiting ineffectively until the end of the cycle after the goal is achieved by associating exception clearing with whether the control command is successfully executed. This allows the system to transition more quickly from one learning stage (such as finding all-open points) to the next stage (such as finding all-closed points), shortens the total time of the entire self-learning calibration, improves the execution efficiency of the calibration process, and enhances the accuracy of exception handling and the overall intelligence level of the system through more reasonable causal relationships.

[0062] Optionally, in one possible embodiment, after sending N control commands to the damper motor at intervals, the method provided in this application embodiment may further include:

[0063] S10. Poll and send a request message to the damper motor; wherein, the request message is used to instruct the damper motor to report the motor stall status and the current position of the damper blades;

[0064] S20. Receive multiple response messages from the damper motor; the response messages include the motor stall status and the current position of the damper blades.

[0065] S30. If, based on the current response message, it is determined that the damper motor is stalled and the damper blade has reached the target position, then execute the step of clearing the abnormal events recorded in the current control cycle.

[0066] For example, after periodically sending control commands to the damper motor, the air conditioning controller can also proactively and periodically poll the damper motor via the LIN bus to send request messages. Polling refers to repeatedly sending query requests to the motor at regular time intervals. For instance, after the last control command is sent and before the next control command is sent, a request message is sent. The request message is a predefined communication data frame; specifically, it instructs the damper motor to provide feedback on two key pieces of information: the motor's stall status and the current position of the damper blades.

[0067] Upon receiving a request message, the damper motor will organize and send back a "response message" based on its current state. The air conditioning controller will receive one or more such response messages. Each response message's data structure includes the motor stall status requested in the request message (e.g., a Boolean value or status code indicating whether it is stalled) and the current position of the damper blades (e.g., expressed as pulse count, angle value, or percentage). The air conditioning controller parses and logically judges the received response messages (especially the latest or most recent stable cycle response message). When both the damper motor stalling and the damper blades reaching the target position indicated by the current command (i.e., fully open or fully closed position) are simultaneously met—that is, reaching the expected, normal end-of-stroke stall—the air conditioning controller executes the step of clearing the abnormal events recorded in the current control cycle in order to enter the next control cycle.

[0068] This optional embodiment introduces a "request-response" proactive polling communication and constructs an intelligent judgment logic that dual-verifies "blocked state" and "target position," achieving a highly reliable and secure anomaly management method. It effectively distinguishes between normal and abnormal endpoint states during damper operation, ensuring that anomaly clearing operations are both timely (for normal endpoints) and cautious (to avoid masking faults). This enhances the robustness of the self-learning process while guaranteeing the long-term operational safety and maintainability of the system.

[0069] Optionally, in one possible embodiment, the method provided in this application embodiment may further include: if it is determined based on the last response message that the damper motor is stalled and the damper blade has not reached the target position, then an abnormal prompt message is output to the user.

[0070] For example, if after sending N control commands to the damper motor intermittently and polling it with multiple request messages, and based on the last received response message it is determined that the damper motor is stalled and the damper blades have not reached the target position, this indicates that the damper motor stalled before reaching the predetermined end of its travel, possibly due to an abnormal mechanical jamming fault. This could be caused by the damper blades being blocked by foreign objects (such as detached parts or ice), deformation of the linkage mechanism, or damage to the motor output mechanism, rather than a normal stall at the mechanical limit point. In this case, the air conditioning controller can display specific warning icons or text messages (such as "Air conditioning damper malfunction, please check") on the vehicle's instrument panel or central control information screen, or issue voice prompts through the multimedia system, so that users or maintenance personnel can handle the fault promptly.

[0071] This optional embodiment adds a crucial safety fallback mechanism and user communication channel to the vehicle air conditioning damper motor self-learning control system by defining precise fault determination rules based on the final polling response and transforming confirmed mechanical faults into user prompts. It ensures that while pursuing automation and adaptability, the system does not lose its vigilance and transparency regarding major faults, thereby comprehensively improving product reliability, safety, and user satisfaction.

[0072] Step 320: Send the second control command to the damper motor N times intermittently.

[0073] For example, in this step, after determining that the damper motor has completed the first control command, the air conditioning controller may send N second control commands to the damper motor in an intermittent manner. The target position indicated by the second control command is opposite to the target position indicated by the first control command; that is, if the first command is "fully open", then the second command is "fully closed", and vice versa.

[0074] By confirming that the damper motor has completed the first control command before executing the next stage of serialized operation, the cumulative stroke error caused by the incomplete previous action is eliminated, making the subsequently collected motor operation data more accurate and laying the foundation for high-precision calibration.

[0075] Similarly, when sending N second control commands to the damper motor at intervals, it can be done in the same way as sending the first control command. It can be to execute one control cycle and send N (e.g., N=3) second control commands; or it can be to execute multiple (e.g., 2) control cycles continuously, and send N second control commands in each control cycle. This application embodiment does not impose any restrictions.

[0076] In one example, sending N second control commands to the damper motor intermittently may include: first, sending N second control commands to the damper motor intermittently within the current control cycle; clearing the abnormal events recorded within the current control cycle; wherein the abnormal events include at least motor stall events; and then sending N second control commands to the damper motor intermittently within the next control cycle.

[0077] Optionally, the number of times the second control command is sent to the damper motor at intervals N times can be dynamically adjusted. Specifically, during the process of sending the second control command to the damper motor at intervals N times, if it is determined that the damper motor has driven the damper blade to the corresponding target position based on the second control command sent in the i-th time, then it is determined to execute the step of clearing the abnormal events recorded in the current control cycle.

[0078] Optionally, after sending N second control commands to the damper motor at intervals, the motor stall status and the current position of the damper blades can also be obtained in real time. Specifically, request messages are sent to the damper motor in a polling manner, and multiple response messages are received from the damper motor. If, based on the current response message, it is determined that the damper motor is stalled and the damper blades have reached the target position, then the step of clearing the abnormal events recorded in the current control cycle is executed; if, based on the last response message, it is determined that the damper motor is stalled and the damper blades have not reached the target position, then an abnormal prompt message is output to the user.

[0079] Understandably, in the forward and reverse self-learning process of this application embodiment, a dynamic fault-tolerant mechanism of sending control commands multiple times at intervals can be adopted to solve the problem of motor position calibration inaccuracy caused by mechanical jamming or signal interference in the prior art, thereby improving the accuracy and reliability of calibration results.

[0080] Step 330: Obtain the position information of the damper blades and the motor status information fed back by the damper motor after sending the control command.

[0081] For example, during the execution of the instructions in steps 310 and 320, the air conditioning controller synchronously collects and records the feedback information of the damper motor. The position information of the damper blades typically refers to signals directly or indirectly related to the damper blade rotation angle, such as potentiometer voltage values ​​and Hall element pulse counts. Motor status information includes, but is not limited to, parameters reflecting the motor's operating load and health status, such as real-time operating current, voltage, temperature, or stall detection signals.

[0082] By comprehensively collecting the position information of the damper blades and the motor status information, multi-dimensional data on the motor operation process can be obtained. This is not only used for positioning, but also reflects the resistance characteristics, wear condition, and potential jamming risk of the mechanical transmission mechanism. This makes the calibration process have the dual functions of performance calibration and condition monitoring, allowing for more efficient use of information.

[0083] Step 340: Based on the position information of the damper blades and the motor status information, determine the motor parameters corresponding to when the damper blades reach the fully open and fully closed positions.

[0084] For example, the air conditioning controller processes multiple sets of data collected in step 330, corresponding to the fully open and fully closed commands. For instance, it determines the final stable pulse count value when reaching the limit position by analyzing position information; and it assists in determining the mechanical limit point by analyzing motor status information (such as current surge points). Then, based on this information, the precise "motor parameters" corresponding to when the damper blades actually reach the fully open and fully closed positions can be calculated. These parameters may include the reference pulse count characterizing the position, the critical current value, and the travel range, etc.

[0085] The calibration is adaptive, calculated based on measured feedback data rather than relying on preset theoretical values. By processing multiple test data (such as averaging and removing outliers), random interference can be effectively filtered out, and core parameters that truly reflect the physical characteristics of the current motor-damper mechanism can be extracted, thereby significantly improving the accuracy and reliability of the calibration results.

[0086] Step 350: Record the motor parameters as the calibration values ​​of the damper motor to complete the calibration.

[0087] For example, at the end of the self-learning process, the air conditioning controller stores the calculated motor parameters corresponding to the fully open and fully closed positions as a complete set of "calibration values" in its non-volatile memory to complete this self-learning calibration process. During the subsequent normal operation of the air conditioning system, the controller will use these calibration values ​​as a reference to perform precise closed-loop control of any target position of the damper.

[0088] By solidifying dynamically learned parameters into calibration values, the air conditioning control system gains adaptive capabilities. Even when faced with factors such as component manufacturing tolerances, long-term wear and tear, and changes in ambient temperature, the system can perform precise control based on the latest calibration values ​​that match the actual conditions. This ensures the long-term consistency and stability of damper control throughout the entire product lifecycle and reduces the need for after-sales debugging and maintenance.

[0089] In this embodiment, a more robust and intelligent self-learning calibration method for vehicle air conditioning damper motors is constructed through a closed-loop process of redundant command sending, sequential execution confirmation, multi-dimensional information fusion, and dynamic parameter calibration. First, sending the same command N times at intervals constitutes a redundancy mechanism, effectively resisting vehicle electromagnetic interference and ensuring reliable delivery of control commands, significantly improving the initial robustness of the self-learning process. Second, strictly adhering to the sequential logic of sending the reverse command only after confirmation ensures the accuracy of the starting point for each travel test, fundamentally eliminating accumulated errors and laying an accurate foundation for data acquisition. Third, synchronously acquiring position information and motor status information achieves the fusion of performance calibration and health monitoring, enabling the system to more comprehensively perceive the mechanical transmission status. Furthermore, dynamically calculating motor parameters based on multiple sets of measured data allows the calibration values ​​to adaptively match characteristic drift caused by manufacturing tolerances, wear and aging, and environmental changes, ensuring long-term control accuracy. Finally, the parameters obtained through self-learning are solidified as calibration values ​​for storage, giving the system memory capabilities. This allows for continuous maintenance of the consistency, stability, and reliability of damper control without manual intervention, significantly improving the overall performance and service life of the air conditioning system. This method not only improves the anti-interference capability and accuracy of the calibration process itself, but also enhances the system's diagnostic capability by acquiring rich motor status information, ultimately achieving the accuracy, adaptability and high reliability of damper position control.

[0090] Figure 4 This is a flowchart illustrating another self-learning control method for the damper motor of a vehicle air conditioning unit provided in this application embodiment. This method can be executed by the air conditioning controller and can perform the self-learning process during the vehicle's power-on / power-off process. Figure 4 As shown, the method may include the following steps:

[0091] Step 401: Clear currently recorded exception events.

[0092] Step 402: Send three control commands to the damper motor at intervals to drive the damper motor to rotate the damper blades from the current position to the fully open position at a preset speed.

[0093] For example, a command is sent every 10ms for a total of 3 times. After receiving the control command, the damper motor not only drives the damper blades from their current position to the fully open direction at a preset speed, but also activates stall detection and damper blade position detection.

[0094] Step 403: Poll the damper motor and send a request message to obtain the motor stall status and the current position of the damper blades.

[0095] For example, a request message can be sent after the last control command is sent and before the next control command is sent.

[0096] Step 404: Determine whether the damper motor is stalled and whether the current position of the damper blade is fully open.

[0097] If yes, proceed to step 405; otherwise, proceed to step 403.

[0098] Step 405: Clear currently recorded exception events.

[0099] Step 406: Send three control commands to the damper motor at intervals to drive the damper motor to rotate the damper blades from the current position to the fully open position at a preset speed.

[0100] Step 407: Poll the damper motor and send a request message to obtain the motor stall status and the current position of the damper blades.

[0101] Step 408: Determine whether the damper motor is stalled and whether the current position of the damper blade is fully open.

[0102] If yes, proceed to step 409; otherwise, proceed to step 407.

[0103] Step 409: Set the current position of the damper blade as the end point of the stroke, record the motor parameters, and clear the currently recorded abnormal events.

[0104] Step 410: Send three control commands to the damper motor at intervals to drive the damper motor to rotate the damper blades from the current position to the fully closed position at a preset speed.

[0105] For example, a command is sent every 10ms for a total of 3 times. After receiving the control command, the damper motor not only drives the damper blades from their current position to the fully closed position at a preset speed, but also activates stall detection and damper blade position detection.

[0106] Step 411: Poll the damper motor and send a request message to obtain the motor stall status and the current position of the damper blades.

[0107] For example, a request message can be sent after the last control command is sent and before the next control command is sent.

[0108] Step 412: Determine whether the damper motor is stalled and whether the current position of the damper blade is fully closed.

[0109] If yes, proceed to step 413; otherwise, proceed to step 411.

[0110] Step 413: Clear currently recorded exception events.

[0111] Step 414: Send three control commands to the damper motor at intervals to drive the damper motor to rotate the damper blades from the current position to the fully closed position at a preset speed.

[0112] Step 415: Poll the damper motor and send a request message to obtain the motor stall status and the current position of the damper blades.

[0113] Step 416: Determine whether the damper motor is stalled and whether the current position of the damper blade is fully closed.

[0114] If yes, proceed to step 417; otherwise, proceed to step 415.

[0115] Step 417: Set the current position of the damper blade to zero and record the motor parameters.

[0116] Step 418: After the self-learning ends, normal control begins.

[0117] It should be noted that the specific implementation of this embodiment can refer to the description of other embodiments, which will not be repeated here. In practical applications, when the damper motor of the vehicle air conditioning unit performs self-learning, it can execute all or part of the above steps, and this application embodiment does not impose any limitations.

[0118] Understandably, the self-learning control method for the damper motor of the vehicle air conditioning unit in this application is also applicable to the simultaneous self-learning of multiple motors. With hardware support, the air conditioning controller can process the control commands and status feedback of multiple motors in parallel, avoiding the efficiency bottleneck caused by serial polling, and the self-learning of each motor does not interfere with each other.

[0119] In this embodiment, a more robust and intelligent self-learning calibration method for vehicle air conditioning damper motor is constructed through a closed-loop process of redundant command sending, sequential execution confirmation, multi-dimensional information fusion, and dynamic parameter calibration. This not only improves the anti-interference capability and accuracy of the calibration process itself, but also enhances the system's diagnostic capability by acquiring rich motor status information, ultimately achieving the accuracy, adaptability, and high reliability of damper position control.

[0120] Figure 5 This application provides a schematic diagram of the structure of a self-learning control device for a damper motor of a vehicle air conditioning unit, as shown in the embodiments of this application. Figure 5 As shown, the device 50 includes: a first processing unit 501 and a second processing unit 502.

[0121] The first processing unit 501 is used to send control commands to the damper motor in the vehicle air conditioning unit at intervals multiple times; wherein, the control commands are used to instruct the damper motor to drive the damper blades to a target position, and the control commands include a first control command and a second control command. When the target position indicated by the first control command is a fully open position, the target position indicated by the second control command is a fully closed position; when the target position indicated by the first control command is a fully closed position, the target position indicated by the second control command is a fully open position.

[0122] The second processing unit 502 is used to calibrate the damper motor based on the execution result of the control command.

[0123] In one alternative embodiment, the first processing unit 501 is specifically used for:

[0124] N control commands are sent intermittently to the damper motor; where the control command is the first control command; N is a positive integer greater than or equal to 1;

[0125] N control commands are sent intermittently to the damper motor; among them, the control command is the second control command.

[0126] In one alternative embodiment, the first processing unit 501 is specifically used for:

[0127] Within the current control cycle, N control commands are sent intermittently to the damper motor;

[0128] Clear all recorded abnormal events in the current control cycle; among these, abnormal events must include at least motor stall events.

[0129] In the next control cycle, N control commands are sent to the damper motor intermittently.

[0130] In one alternative embodiment, the first processing unit 501 is specifically used for:

[0131] During the process of sending N control commands to the damper motor at intervals, if it is determined that the damper motor has driven the damper blade to the corresponding target position based on the i-th control command sent, then it is determined to execute the step of clearing the abnormal events recorded in the current control cycle.

[0132] Where i is a positive integer greater than or equal to 1 and less than or equal to N.

[0133] In one alternative embodiment, after sending N control commands to the damper motor at intervals, the first processing unit 501 is further configured to:

[0134] The damper motor is polled and a request message is sent; the request message is used to instruct the damper motor to provide feedback on the motor stall status and the current position of the damper blades.

[0135] Receive multiple response messages from the damper motor; the response messages include the motor stall status and the current position of the damper blades;

[0136] If, based on the current response message, it is determined that the damper motor is stalled and the damper blades have reached the target position, then the step of clearing the abnormal events recorded in the current control cycle is executed.

[0137] In one alternative embodiment, the first processing unit 501 is further configured to:

[0138] If, based on the last response message, it is determined that the damper motor is stalled and the damper blades have not reached the target position, an error message is output to the user.

[0139] In one alternative embodiment, the second processing unit 502 is specifically used for:

[0140] After receiving the control command, the damper motor provides feedback on the position information of the damper blades and the motor status information.

[0141] Based on the position information of the damper blades and the motor status information, determine the corresponding motor parameters when the damper blades reach the fully open and fully closed positions;

[0142] Record the motor parameters as the calibration values ​​for the damper motor to complete the calibration.

[0143] As can be seen from the above, the self-learning control device for the damper motor of the vehicle air conditioning unit provided in this embodiment realizes intelligent self-learning control of the vehicle air conditioning damper motor by sequentially sending control commands for the fully open and fully closed positions to the damper motor at intervals, and automatically completing calibration based on the execution results. Specifically, the interval sending avoids motor overheating and mechanical wear, extends service life, and ensures the stability of data acquisition; through bidirectional stroke verification and a dynamic fault-tolerant mechanism based on multiple command sending, the damper stroke is fully covered, improving the accuracy and comprehensiveness of calibration data; in addition, the self-learning calibration process does not require manual intervention and can adapt to the characteristic drift of the motor and transmission mechanism caused by manufacturing tolerances, aging, temperature changes, etc., thereby significantly improving the long-term accuracy, consistency and reliability of damper control.

[0144] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0145] like Figure 6 As shown, the electronic device may include: a processor 602, a communications interface 604, a memory 606, and a communications bus 608.

[0146] The processor 602, communication interface 604, and memory 606 communicate with each other via communication bus 608. Communication interface 604 is used to communicate with other network elements such as clients or other servers. The processor 602 executes program 610, specifically performing the relevant steps in the above embodiment of the self-learning control method for the damper motor of an onboard air conditioning unit.

[0147] Specifically, program 610 may include program code, which includes computer-executable instructions.

[0148] Processor 602 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The electronic device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0149] Memory 606 is used to store program 610. Memory 606 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0150] Specifically, program 610 can be called by processor 602 to cause the electronic device to perform the following operations:

[0151] Multiple control commands are sent intermittently to the damper motor in the vehicle air conditioning unit. The control commands are used to instruct the damper motor to drive the damper blades to a target position. The control commands include a first control command and a second control command. When the target position indicated by the first control command is the fully open position, the target position indicated by the second control command is the fully closed position. When the target position indicated by the first control command is the fully closed position, the target position indicated by the second control command is the fully open position.

[0152] The damper motor is calibrated based on the execution results of the control commands.

[0153] In one alternative approach, control commands are periodically sent multiple times to the damper motor in the vehicle's air conditioning unit, including:

[0154] N control commands are sent intermittently to the damper motor; where the control command is the first control command; N is a positive integer greater than or equal to 1;

[0155] N control commands are sent intermittently to the damper motor; among them, the control command is the second control command.

[0156] In one alternative approach, N control commands are sent to the damper motor intermittently, including:

[0157] Within the current control cycle, N control commands are sent intermittently to the damper motor;

[0158] Clear all recorded abnormal events in the current control cycle; among these, abnormal events must include at least motor stall events.

[0159] In the next control cycle, N control commands are sent to the damper motor intermittently.

[0160] In one alternative approach, N control commands are sent to the damper motor intermittently, including:

[0161] During the process of sending N control commands to the damper motor at intervals, if it is determined that the damper motor has driven the damper blade to the corresponding target position based on the i-th control command sent, then it is determined to execute the step of clearing the abnormal events recorded in the current control cycle.

[0162] Where i is a positive integer greater than or equal to 1 and less than or equal to N.

[0163] In an alternative approach, after periodically sending N control commands to the damper motor, the method provided in this application embodiment further includes:

[0164] The damper motor is polled and a request message is sent; the request message is used to instruct the damper motor to provide feedback on the motor stall status and the current position of the damper blades.

[0165] Receive multiple response messages from the damper motor; the response messages include the motor stall status and the current position of the damper blades;

[0166] If, based on the current response message, it is determined that the damper motor is stalled and the damper blades have reached the target position, then the step of clearing the abnormal events recorded in the current control cycle is executed.

[0167] In an optional embodiment, the method provided in this application further includes:

[0168] If, based on the last response message, it is determined that the damper motor is stalled and the damper blades have not reached the target position, an error message is output to the user.

[0169] In one alternative approach, the damper motor is calibrated based on the execution result of the control command, including:

[0170] After receiving the control command, the damper motor provides feedback on the position information of the damper blades and the motor status information.

[0171] Based on the position information of the damper blades and the motor status information, determine the corresponding motor parameters when the damper blades reach the fully open and fully closed positions;

[0172] Record the motor parameters as the calibration values ​​for the damper motor to complete the calibration.

[0173] As can be seen from the above, the electronic device provided in this embodiment realizes intelligent self-learning control of the vehicle air conditioning damper motor by sequentially sending control commands for the fully open and fully closed positions to the damper motor at intervals, and automatically completing calibration based on the execution results. Specifically, the interval sending avoids motor overheating and mechanical wear, extending its service life, while ensuring the stability of data acquisition; through bidirectional stroke verification and a dynamic fault-tolerant mechanism based on multiple command sending, the damper stroke is fully covered, improving the accuracy and comprehensiveness of calibration data; in addition, the self-learning calibration process does not require manual intervention and can adapt to the characteristic drift of the motor and transmission mechanism caused by manufacturing tolerances, aging, temperature changes, etc., thereby significantly improving the long-term accuracy, consistency and reliability of damper control.

[0174] This application provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is executed on an electronic device, it causes the electronic device to perform the self-learning control method for the damper motor of the vehicle air conditioning unit in any of the above method embodiments.

[0175] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments in this application are not directed to any particular programming language.

[0176] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. Similarly, for the purpose of simplification and aiding understanding of one or more aspects of the invention, in the above description of exemplary embodiments of this application, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0177] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0178] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A self-learning control method for the damper motor of a vehicle air conditioning unit, characterized in that, The method includes: Multiple control commands are sent intermittently to the damper motor in the vehicle air conditioning unit; wherein, the control commands are used to instruct the damper motor to drive the damper blades to a target position, the control commands include a first control command and a second control command, when the target position indicated by the first control command is a fully open position, the target position indicated by the second control command is a fully closed position; when the target position indicated by the first control command is a fully closed position, the target position indicated by the second control command is a fully open position. The damper motor is calibrated based on the execution result of the control command.

2. The method according to claim 1, characterized in that, The intermittent sending of control commands to the damper motor in the vehicle's air conditioning unit includes: The control command is sent to the damper motor N times at intervals; wherein the control command is the first control command; and N is a positive integer greater than or equal to 1. The control command is sent to the damper motor N times at intervals; wherein the control command is the second control command.

3. The method according to claim 2, characterized in that, The step of periodically sending the control command N times to the damper motor includes: Within the current control cycle, the control command is sent to the damper motor N times at intervals; Clear the abnormal events recorded in the current control cycle; wherein, the abnormal events include at least motor stall events; In the next control cycle, the control command is sent to the damper motor N times at intervals.

4. The method according to claim 3, characterized in that, The step of periodically sending the control command N times to the damper motor includes: During the process of sending the control command to the damper motor N times at intervals, if it is determined that the damper motor has driven the damper blade to the corresponding target position based on the i-th sent control command, then it is determined to execute the step of clearing the abnormal events recorded in the current control cycle. Where i is a positive integer greater than or equal to 1 and less than or equal to N.

5. The method according to any one of claims 2-4, characterized in that, After sending the control command to the damper motor N times at intervals, the method further includes: The damper motor is polled and a request message is sent; wherein the request message is used to instruct the damper motor to provide feedback on the motor stall status and the current position of the damper blade; Receive multiple response messages from the damper motor; wherein the response messages include the motor stall status and the current position of the damper blade; If, based on the current response message, it is determined that the damper motor is stalled and the damper blade has reached the target position, then the step of clearing the abnormal events recorded in the current control cycle is executed.

6. The method according to claim 5, characterized in that, The method further includes: If, based on the last response message, it is determined that the damper motor is stalled and the damper blade has not reached the target position, an abnormal prompt message is output to the user.

7. The method according to claim 1, characterized in that, The calibration of the damper motor based on the execution result of the control command includes: After the control command is sent, the position information of the damper blade and the motor status information fed back by the damper motor are obtained; Based on the position information of the damper blade and the motor status information, determine the motor parameters corresponding to when the damper blade reaches the fully open position and the fully closed position; The motor parameters are recorded as the calibration values ​​of the damper motor to complete the calibration.

8. A self-learning control device for the damper motor of a vehicle air conditioning unit, characterized in that, The device includes: The first processing unit is used to send control commands to the damper motor in the vehicle air conditioning unit at intervals multiple times; wherein, the control commands are used to instruct the damper motor to drive the damper blades to a target position, the control commands include a first control command and a second control command, when the target position indicated by the first control command is a fully open position, the target position indicated by the second control command is a fully closed position; when the target position indicated by the first control command is a fully closed position, the target position indicated by the second control command is a fully open position. The second processing unit is used to calibrate the damper motor based on the execution result of the control command.

9. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation of the self-learning control method for the damper motor of the vehicle air conditioning unit as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on an electronic device, causes the electronic device to perform the operation of the self-learning control method for the damper motor of the vehicle air conditioning unit as described in any one of claims 1-7.

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