Anti-pinch control method for power window, vehicle and storage medium

Abstract

The invention provides an anti-pinch control method for a power window, a vehicle and a storage medium, and relates to the technical field of automobile electronic control. The method comprises the steps that a vehicle posture signal, a vehicle door deformation signal and a current vehicle window resistance signal of a target vehicle are obtained; on the basis of the vehicle posture signal and the vehicle door deformation signal, the reference anti-pinch force threshold value is compensated, and a target anti-pinch force threshold value is obtained; based on the current vehicle window resistance signal and a base line resistance signal, the first deviation value of the current vehicle window resistance signal is determined, and the base line resistance signal is used for representing inherent resistance needing to be overcome when the power window ascends and descends; and if the first deviation value is larger than the target anti-pinch force threshold value, the power window is controlled to execute the anti-pinch action. Therefore, when the first deviation value is larger than the target anti-pinch force threshold value, the vehicle window is controlled to execute the anti-pinch action, it is ensured that the real clamping signal can effectively reach the trigger threshold value to avoid anti-pinch failure, and the accuracy and reliability of judgment of the power window anti-pinch system under the complex working condition are improved.

Description

Technical Field

[0001] This application relates to the field of automotive electronic control technology, and more specifically, to an anti-pinch control method for electric windows, a vehicle, and a storage medium. Background Technology

[0002] With the rapid development of the automotive industry, power windows have now become standard equipment on all types of vehicles. Their anti-pinch function, as a core module to ensure the safety of drivers and passengers, is one of the key elements in measuring vehicle safety performance.

[0003] Currently, existing technologies rely on fixed thresholds. By monitoring the motor's operating current or the pulse frequency of a Hall sensor (used to calculate the motor speed), and comparing the instantaneous deviation of these parameters with a preset fixed threshold, clamping events are determined and the window is controlled to reverse. This distinguishes between normal lifting and abnormal clamping states, replacing the earlier purely mechanical control schemes without anti-pinch functions, and achieving basic anti-pinch protection under ideal working conditions.

[0004] However, traditional power window anti-pinch systems rely on fixed thresholds, making them ill-suited for complex conditions such as non-horizontal vehicle parking. From a force perspective, when a vehicle is parked on a slope or other non-horizontal location, the component of gravity along the window's lifting direction is always opposite to the window's upward direction, and its magnitude is smaller than on a level surface. This lowers the baseline load for the motor's upward movement. Anti-pinch systems using fixed threshold strategies cannot detect this change in baseline and may misinterpret the low load baseline on a slope as an abnormal signal. This misinterpretation could lead to motor parameter fluctuations reaching the anti-pinch threshold even when there are no obstacles, resulting in incorrect anti-pinch reversal; or, during actual clamping, insufficient signal increment might prevent reaching the trigger threshold, causing anti-pinch failure. Summary of the Invention

[0005] The electric window anti-pinch control method, vehicle, and storage medium provided in this application construct a target anti-pinch force threshold that can dynamically adjust the electric window anti-pinch function by real-time fusion of vehicle attitude signals and door deformation signals, thereby enabling it to have adaptive capabilities for complex working conditions such as vehicle tilt and door deformation, and achieving synergistic optimization of safety and reliability.

[0006] In a first aspect, a method for anti-pinch control of an electric window is provided. The method includes: acquiring a vehicle attitude signal, a door deformation signal, and a current window resistance signal of a target vehicle; compensating a reference anti-pinch force threshold based on the vehicle attitude signal and the door deformation signal to obtain a target anti-pinch force threshold; determining a first deviation of the current window resistance signal based on the current window resistance signal and the baseline resistance signal, wherein the baseline resistance signal is used to represent the inherent resistance that the electric window needs to overcome when raising and lowering; and controlling the electric window to perform an anti-pinch action if the first deviation is greater than the target anti-pinch force threshold.

[0007] In the above technical solution, by acquiring the vehicle attitude signal, door deformation signal, and current window resistance signal of the target vehicle, the vehicle attitude signal can accurately sense non-horizontal postures such as parking on an incline, and the door deformation signal can capture the inherent resistance changes caused by door deformation. The combination of these two signals dynamically compensates for the reference anti-pinch force threshold and obtains a target anti-pinch force threshold adapted to the real-time operating conditions of the vehicle. This effectively eliminates the reference deviation of the anti-pinch force threshold caused by the reduction of the motor load reference due to the gravity component when the vehicle is not parked horizontally, and the change in inherent resistance caused by door deformation. This solves the core problem of the inaccuracy of the reference caused by the inability of traditional fixed thresholds to sense changes in operating conditions. At the same time, based on the current window resistance signal and the inherent resistance of the electric window lifting mechanism, the solution is more effective. The baseline resistance signal of the force determines the first deviation, which accurately represents the actual resistance change exceeding the inherent resistance during the window's raising and lowering process. Then, by comparing the first deviation with the dynamically compensated target anti-pinch force threshold, if the first deviation is greater than the target anti-pinch force threshold, the window is controlled to perform an anti-pinch action. This judgment logic can accurately identify the actual clamping resistance signal, which avoids the false reversal of unobstructed anti-pinch caused by the low load baseline state under the slope being misjudged as an abnormal signal. It also solves the problem of anti-pinch failure caused by insufficient signal increment to trigger the threshold during actual clamping. Ultimately, it achieves the accuracy of anti-pinch judgment under complex working conditions, and improves the detection accuracy, action reliability and working condition adaptability of the electric window anti-pinch system.

[0008] Secondly, a vehicle anti-pinch device for power windows is provided. The device includes: an acquisition module for acquiring vehicle attitude signals, door deformation signals, and current window resistance signals of the target vehicle; a compensation module for compensating a reference anti-pinch force threshold based on the vehicle attitude signals and door deformation signals to obtain a target anti-pinch force threshold; a determination module for determining a first deviation of the current window resistance signal based on the current window resistance signal and a baseline resistance signal, wherein the baseline resistance signal represents the inherent resistance that the power window needs to overcome when raising and lowering; and a control module for controlling the power window to perform an anti-pinch action if the first deviation is greater than the target anti-pinch force threshold.

[0009] Thirdly, a vehicle is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods described in the first aspect or any possible implementation thereof.

[0010] Fourthly, a computer-readable storage medium is provided that stores a program or instructions that cause a computer to perform the methods described in the first aspect or any possible implementation thereof.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. 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: Figure 1 A schematic diagram of the structure of a vehicle provided in an embodiment of this application; Figure 2 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 1 ; Figure 3 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 2 ; Figure 4 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 3 ; Figure 5 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 4 ; Figure 6 This is a schematic diagram of the structure of an anti-pinch control device for an electric window provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0014] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0015] Before elaborating on the technical solution, the technical terms used in this application will be explained to facilitate subsequent understanding.

[0016] Baseline resistance refers to the sum of constant frictional resistance and mechanical transmission resistance that the electric window glass must overcome during the raising and lowering process when the vehicle is parked horizontally, the door is not deformed, and the guide rail is clean. It is the core baseline for the anti-pinch system to determine whether the window raising and lowering is abnormal.

[0017] The dynamic anti-pinch force threshold refers to a variable that is dynamically adjusted according to the real-time status of the vehicle. Its core idea is to make the trigger sensitivity of the anti-pinch system adapt to the actual physical environment in which the vehicle is located, thereby minimizing the probability of false triggering of the system while ensuring driving safety.

[0018] Vehicle static attitude refers to the tilt angle of the vehicle body relative to the horizontal plane when the vehicle is stationary without acceleration, braking, or turning. It is typically described by two parameters: longitudinal tilt angle and lateral tilt angle. Relevant data can be directly obtained from the vehicle's Electronic Stability Program (ESP) or Inertial Measurement Unit (IMU). The ESP is the core unit of the vehicle's active safety control; the IMU is a high-precision motion state perception sensor component, integrating a three-axis accelerometer and a three-axis gyroscope. The ESP can measure the acceleration of an object in the X, Y, and Z directions and calculate the gravitational component, while the IMU can measure the angular velocity of an object around the three axes and calculate the rotation angle. Furthermore, the IMU can accurately calculate data such as the vehicle body's tilt angle and attitude changes relative to the horizontal plane through sensor fusion algorithms such as Kalman filtering.

[0019] The door deformation index quantifies the parameters of geometric deformation of the door frame caused by external impact or long-term aging. It can be indirectly calculated in two ways: first, by analyzing the output distribution changes of a thin-film pressure sensor array embedded in the door's perimeter sealing strip (such as the contact area between the door and the body frame); and second, by monitoring the resistance changes of micro-strain gauges installed in key parts of the door frame. Micro-strain gauges, based on the strain resistance effect, are high-precision sensors for measuring minute tensile and compressive deformations of objects and are commonly used for monitoring stress changes in mechanical structures. The core function of this door deformation index is to reflect the potential impact of the door's structural integrity on the resistance to the window's lifting movement.

[0020] As a core module to ensure driving and riding safety, the anti-pinch function of electric windows has become a standard feature in vehicles. Existing technologies mostly adopt a fixed threshold scheme, which monitors the motor operating current or the pulse frequency of the Hall sensor (to calculate the motor speed), compares the instantaneous deviation of the parameters with the preset threshold to determine the clamping and controls the window to reverse. This has replaced the early purely mechanical control scheme, which can only achieve basic anti-pinch protection under ideal working conditions. However, this fixed threshold scheme has significant adaptation flaws and struggles to handle complex conditions such as non-horizontally parked vehicles. From a force perspective, when a vehicle is parked on a slope or other non-horizontal location, the component of gravity along the window's lifting direction is always opposite to the window's upward direction, and its magnitude changes compared to a level surface. This directly lowers the load reference value for the motor-driven window lifting. Traditional fixed threshold anti-pinch systems cannot detect this dynamic change in load reference, easily leading to two core problems: First, they may misinterpret the low load baseline state on a slope as an abnormal clamping signal. In the absence of obstacles, even slight fluctuations in motor parameters can reach the anti-pinch threshold, causing the window to incorrectly execute the anti-pinch reversal action, severely impacting the driving experience. Second, in actual clamping situations, the load baseline shift results in insufficient incremental effective resistance signals, making it difficult to reach the preset fixed trigger threshold. This prevents the anti-pinch function from responding promptly, ultimately causing anti-pinch failure and creating a safety hazard. Therefore, there is an urgent need for a dynamic, adaptable electric window anti-pinch technology that balances safety and reliability to address the inherent flaws of existing fixed threshold schemes. The following detailed description, in conjunction with the accompanying drawings and through multiple embodiments, describes the anti-pinch control method for electric windows, the vehicle, and the storage medium according to embodiments of this application.

[0021] Figure 1 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Figure 1 As shown, the vehicle 100 may include a processor 110 and a memory 120.

[0022] The memory 120 stores machine-executable instructions that can be executed by the processor 110. When the vehicle 100 is running, these machine-executable instructions are executed. The processor 110 and the memory 120 communicate via a bus. The processor 110 can execute these machine-executable instructions to implement an anti-pinch control method for the power windows.

[0023] The memory 120, processor 110, and various bus components are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected via one or more communication buses or signal lines. The memory 120 includes at least one software function module, which is embedded in the vehicle's operating system (OS) as software or firmware. This software function module has an executable module. The processor 110 executes the executable module stored in the memory 120, such as the software function module and computer program included in the anti-pinch control method for the power window.

[0024] The memory 120 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0025] The vehicle 100 includes software that enables anti-pinch control of power windows.

[0026] The anti-pinch control method for power windows provided in this application embodiment can be executed by a processor in the vehicle 100. The anti-pinch control method for power windows provided in this application embodiment will be explained further below. Figure 2 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 1 .like Figure 2 As shown, the method may include: S210. Acquire the vehicle attitude signal, door deformation signal, and current window resistance signal of the target vehicle.

[0027] The vehicle attitude signal is obtained by the anti-pinch system from the integrated inertial measurement unit (IMU) data read by the system from the vehicle domain controller or electronic stability system via the vehicle's CAN bus. This IMU data includes three-axis acceleration and three-axis gyroscope information. Using a pre-set sensor fusion algorithm (such as complementary filtering or Kalman filtering), the vehicle's longitudinal tilt angle (θ) relative to the horizontal plane can be accurately calculated. x (front and rear slope directions, rotation around the vehicle's lateral axis) and lateral tilt angle (θ) The calculation accuracy can reach ±0.5°, which can accurately calculate the component force of gravity generated along the upward direction of the glass. Since the direction of this component force is opposite to the upward direction of the window, and its magnitude changes with θ (composite tilt angle): on the slope (θ>0), this component force is less than the gravity on the horizontal road surface (θ=0), which leads to a decrease in the reference value of the motor's upward load. By quantifying the change in gravity resistance caused by the slope, the judgment reference of the anti-pinch algorithm can be dynamically compensated, thereby solving the problem of false anti-pinch or decreased anti-pinch sensitivity caused by load baseline offset, and ensuring that the anti-pinch performance of the vehicle is consistent and reliable under various postures.

[0028] Among them, the door deformation signal is a door deformation index signal collected and calculated by a flexible thin film pressure sensor array in the sealing strip or micro-strain gauges in key parts of the door frame. It is used to quantify the slight deformation of the door frame caused by minor collisions or long-term use, and accurately determine the degree of permanent increase in the baseline of frictional resistance of the glass guide rail caused by the deformation. This solves the serious safety hazard of leakage and anti-pinch failure caused by the resistance increment generated by actual clamping under this working condition that may not be able to break through the fixed threshold.

[0029] The current window resistance signal F_current (unit: N) is obtained by monitoring the motor operating current or Hall sensor pulse frequency, as well as the current-torque and speed-torque relationship models. Since the motor load is positively correlated with the window resistance, changes in current and speed parameters can directly reflect the total resistance encountered by the window during lifting. Therefore, the electrical signal is converted into the actual total resistance encountered by the window glass in the direction of movement to monitor the actual resistance status of the window lifting in real time, which serves as a direct basis for anti-pinch safety decisions.

[0030] In one possible implementation, by acquiring the vehicle attitude signal, door deformation signal, and current window resistance signal F_current of the target vehicle, the resistance compensation amount that fits the current actual working condition can be calculated based on these three types of signals. This provides a data foundation for the baseline resistance drift problem caused by the fixed trigger threshold of traditional electric window anti-pinch systems, which cannot cope with complex working conditions such as vehicle tilt and door structural deformation, thus fundamentally reducing the probability of false and missed anti-pinch. It is also a prerequisite for building a baseline resistance prediction and compensation algorithm based on a physical model and realizing dynamic adjustment of the anti-pinch trigger threshold. At the same time, it can also expand the decision-making basis of the anti-pinch system from a single motor parameter to a comprehensive perception basis of multi-dimensional information fusion, so that the judgment of the anti-pinch system has working condition context awareness.

[0031] S220. Based on the vehicle attitude signal and the door deformation signal, the reference anti-pinch force threshold is compensated to obtain the target anti-pinch force threshold.

[0032] In one possible implementation, the fixed baseline anti-pinch force threshold F_critical_nominal used in traditional anti-pinch systems is calibrated under ideal conditions where the vehicle is parked horizontally, the door is not deformed, and the guide rail is clean. This cannot adapt to the systematic drift of the window glass lifting baseline resistance caused by changes in vehicle posture and deformation of the door frame during actual vehicle use. The vehicle posture signal and the door deformation signal are the main basis for quantifying the baseline resistance deviation caused by these two key factors. By compensating through these two types of signals, the target anti-pinch force threshold F_critical_dynamic can be made to fit the current actual physical conditions of the vehicle. Among these methods, vehicle attitude signals can accurately calculate the resistance compensation amount caused by the gravitational component to the window operation; door deformation signals can accurately quantify the resistance compensation amount caused by door deformation to the window operation. Therefore, these two types of signals can comprehensively capture the core physical state changes that cause baseline resistance drift. However, the baseline anti-pinch force threshold F_critical_nominal itself cannot cope with these resistance changes under non-ideal working conditions. Therefore, it is necessary to rely on these two types of signals and use a resistance compensation model built based on mechanical principles to calculate the total resistance compensation amount caused by both vehicle attitude and door deformation. Then, this total resistance compensation amount is compared with the baseline anti-pinch force threshold F_critical_nominal. The system performs superimposed compensation to obtain a target anti-pinch force threshold F_critical_dynamic that adapts to the vehicle's current actual operating conditions. This allows the target anti-pinch force threshold F_critical_dynamic to dynamically adjust according to the actual baseline resistance, fundamentally solving the problems of false anti-pinch caused by vehicle tilt and the masking of the clamping signal. It also addresses the problem of missed anti-pinch caused by door deformation, achieving adaptive anti-pinch trigger sensitivity to complex operating conditions. Ultimately, without compromising safety standards and ensuring timely response to real clamping events, it minimizes false triggers in unobstructed conditions, improves the robustness of the anti-pinch system under non-ideal operating conditions, and balances the safety of the anti-pinch system with the user experience. The resistance compensation amount (unit: N) represents the additional resistance offset caused by vehicle posture and door deformation.

[0033] For example, when a vehicle is going uphill, the component of gravity decreases, which reduces the baseline resistance for the window to rise. In this case, the target anti-pinch force threshold should be lowered accordingly to avoid false triggering. Conversely, when door deformation increases frictional resistance, the target anti-pinch force threshold should be increased to prevent missed triggering.

[0034] S230. Based on the current window resistance signal and the baseline resistance signal, determine the first deviation of the current window resistance signal.

[0035] Among them, the baseline resistance signal F_baseline is used to represent the inherent resistance that the electric window needs to overcome during the raising and lowering of the window. It is used to characterize the sum of constant frictional resistance and mechanical transmission resistance that needs to be overcome during the raising and lowering of the window glass under ideal working conditions, such as the vehicle being parked horizontally, the door not deformed, and the guide rail being clean. It is the core benchmark for the electric window anti-pinch system to judge abnormal resistance.

[0036] In one possible implementation, since the current window resistance signal F_current reflects the actual force state at the moment the window is raised or lowered, directly using the absolute value of the current window resistance signal for anti-pinch judgment will result in serious judgment deviation due to baseline resistance drift caused by complex working conditions such as vehicle body tilt and door deformation during actual use. Just as in the traditional fixed threshold scheme, the slope condition (whether uphill or downhill) will cause the baseline resistance component contributed by gravity to decrease, while the door deformation will cause the friction resistance component to increase. The combined effect of the two will cause the actual load reference to deviate from the calibration value. If the anti-pinch system still uses a fixed threshold, small fluctuations can easily be misjudged as clamping when the load reference is low. When the load reference deviates, the actual increase in clamping resistance may be difficult to identify effectively. Therefore, by calculating the difference between the current window resistance signal F_current and the baseline resistance signal F_baseline, the first deviation ΔF1 can be obtained. This allows for the precise removal of the inherent constant influence of the baseline resistance and the extraction of the actual increase in resistance caused by external clamping events, obstacles, changes in vehicle posture, or door deformation during window lifting, rather than the absolute value of resistance affected by baseline resistance drift. This provides an objective and accurate basis for comparing the first deviation ΔF1 with the anti-pinch force threshold dynamically calculated by fusing vehicle posture and door deformation information to accurately determine whether an effective clamping event has occurred.

[0037] The first deviation ΔF1 of the current window resistance signal can be determined by the following formula (1).

[0038] △F1=F_current-F_baseline formula (1) S240. If the first deviation is greater than the target anti-pinch force threshold, control the electric window to perform the anti-pinch action.

[0039] In one possible implementation, when the first deviation ΔF1 is greater than the target anti-pinch force threshold F_critical_dynamic, it indicates that during the normal raising and lowering process under the current operating conditions, the window glass is subjected to additional abnormal resistance in addition to overcoming the baseline resistance under the current operating conditions. This abnormal resistance is not caused by normal operating conditions such as vehicle posture or door deformation, but by the clamping resistance generated when the glass comes into contact with an obstacle during the raising and lowering process. Based on this, the anti-pinch system can accurately determine that an effective clamping event has occurred. In order to eliminate the safety hazards caused by clamping in a timely manner and ensure the personal safety of the driver and passengers, the anti-pinch system will trigger the anti-pinch action in milliseconds according to the preset control logic, control the electric window to perform anti-pinch operations such as reversing, so that the window glass drops a safe distance to release the clamped object.

[0040] The anti-pinch control method for electric windows provided in this application acquires the vehicle attitude signal, door deformation signal, and current window resistance signal of the target vehicle. The vehicle attitude signal accurately senses non-horizontal postures such as when the vehicle is parked on an incline, while the door deformation signal captures the inherent resistance changes caused by door deformation. Combining these two signals dynamically compensates for the baseline anti-pinch force threshold, resulting in a target anti-pinch force threshold adapted to the vehicle's real-time operating conditions. This effectively eliminates the baseline deviation of the anti-pinch force threshold caused by the reduction in motor load due to gravity when the vehicle is not parked horizontally, and by the change in inherent resistance caused by door deformation. This solves the core problem of traditional fixed thresholds failing to sense changes in operating conditions, leading to baseline inaccuracies. Furthermore, based on the current window resistance signal and the characteristics of the electric window… The baseline resistance signal of the inherent resistance of the window lift determines the first deviation. This first deviation accurately represents the actual resistance change exceeding the inherent resistance during the window lift process. The first deviation is then compared with the dynamically compensated target anti-pinch force threshold. If the first deviation is greater than the target anti-pinch force threshold, the window is controlled to perform an anti-pinch action. This judgment logic can accurately identify the actual clamping resistance signal, avoiding the misjudgment of the low-load baseline state under the slope as an abnormal signal, which leads to the erroneous reversal of the unobstructed anti-pinch action. It also solves the problem of anti-pinch failure caused by insufficient signal increment during actual clamping. Ultimately, it achieves the accuracy of anti-pinch judgment under complex working conditions, improving the detection accuracy, action reliability, and working condition adaptability of the electric window anti-pinch system.

[0041] Figure 3 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 2 .like Figure 3 As shown, the above method compensates for the baseline anti-pinch force threshold based on vehicle attitude signals and door deformation signals to obtain the target anti-pinch force threshold, including: S310. Input the vehicle attitude signal and door deformation signal into the drag compensation model for processing to obtain the drag compensation amount of the target vehicle.

[0042] The resistance compensation amount ΔF is used to characterize the influence of vehicle attitude signals and door deformation signals on the baseline resistance signal. The resistance compensation model is an algorithm or mathematical model that quantifies the influence of various resistances during the operation of the anti-pinch system and outputs corresponding compensation amounts to offset resistance interference and ensure that the anti-pinch system operates as expected.

[0043] In one possible implementation, vehicle attitude signals can reflect the overall attitude changes of the vehicle under different operating conditions such as driving and parking. These overall attitude changes directly alter the relative position of the door and the vehicle body, the airflow environment around the door, and the mechanical force angle of the door's movement, thus significantly affecting the resistance during the door's opening and closing processes. Door deformation signals can accurately reflect the actual degree of deformation of the door under its own force, the transmission of vehicle attitude changes, and the effects of the external environment. The deformation of the door further changes its own trajectory and the contact state with the vehicle body, seals, and other components, resulting in a secondary change in the door's movement resistance. Both are key and direct factors affecting the door's movement resistance. Relying on a single signal alone cannot comprehensively and accurately capture the true dynamic changes in resistance during the door's movement, nor can it accurately quantify the actual impact of various factors on resistance. By inputting these two key signals (vehicle attitude signal and door deformation signal) into the drag compensation model, the model can fully combine the basic characteristics of drag influence under the overall working conditions reflected by the vehicle attitude signal, and the specific characteristics of drag influence caused by the changes in the door's own state reflected by the door deformation signal. The model performs collaborative analysis, quantitative modeling, and precise calculation on the two types of signals, comprehensively identifying and analyzing the actual drag composition and variation law of the target vehicle door during movement. Based on this variation law, a drag compensation amount ΔF that highly matches the actual working conditions is generated. This accurately compensates for the deviation in door movement drag caused by changes in vehicle attitude and door deformation, providing a reliable drag compensation basis for precise door control and ensuring the stability and accuracy of door movement control.

[0044] S320. Based on the resistance compensation amount, the benchmark anti-pinch force threshold is corrected to obtain the target anti-pinch force threshold.

[0045] in, In one possible implementation, the target anti-pinch force threshold F_critical_dynamic is obtained by formula (2) based on the resistance compensation amount ΔF and the reference anti-pinch force threshold F_critical_nominal.

[0046] F_critical_dynamic=F_critical_nominal+ΔF formula (2) The target anti-pinch force threshold F_critical_dynamic can adapt to different working conditions. For example, when the load reference shifts due to vehicle posture or door status (e.g., the reference decreases due to a slope, or the reference increases due to door deformation), the resistance compensation amount ΔF is adjusted accordingly, causing the target anti-pinch force threshold F_critical_dynamic to be dynamically corrected. This ensures that the target anti-pinch trigger threshold F_critical_dynamic always maintains a relatively stable relationship with the changing load reference, thereby avoiding false triggering due to the system being too sensitive when the reference decreases, or failure due to the actual clamping signal increment not reaching the threshold caused by reference changes. This achieves a balance between safety and reliability, completely solving the problem that traditional fixed thresholds cannot adapt to complex working conditions.

[0047] The anti-pinch control method for electric windows provided in this application inputs vehicle attitude signals and door deformation signals into a resistance compensation model for processing, obtaining the resistance compensation amount of the target vehicle. This provides a scientific physical quantitative basis for the dynamic correction of the target anti-pinch force threshold, avoiding the deviation problem of traditional empirical threshold adjustment. Subsequently, based on the resistance compensation amount, the baseline anti-pinch force threshold is specifically adapted and corrected to obtain a target anti-pinch force threshold that highly matches the current actual working conditions of the vehicle. This effectively offsets the baseline resistance drift caused by changes in vehicle attitude and door deformation, enabling the target anti-pinch force threshold to dynamically adjust with the real-time physical state of the vehicle. This fundamentally avoids the problems of false anti-pinch and missed anti-pinch due to the mismatch of working conditions caused by fixed threshold schemes, while ensuring the accuracy of anti-pinch judgment and achieving a synergistic improvement in the safety and reliability of the electric window anti-pinch system.

[0048] Figure 4 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 3 .like Figure 4 As shown, in the above method, the vehicle attitude signal and door deformation signal are input into the drag compensation model for processing to obtain the drag compensation amount of the target vehicle, including: S410. Input the vehicle attitude signal and door deformation signal into the drag compensation model.

[0049] S420. Using the resistance compensation model, calculate the attitude compensation amount corresponding to the vehicle attitude signal and the deformation compensation amount corresponding to the door deformation signal.

[0050] In one possible implementation, during actual driving, vehicles experience pitch, roll, and yaw changes due to complex road conditions such as bumps, inclines, and turns. These changes cause a shift in the mounting reference of various environmental perception and positioning detection devices, leading to deviations in the environmental data and positioning information collected by these devices. Failure to correct these deviations directly reduces the accuracy of vehicle environmental perception and positioning, potentially impacting the effectiveness of subsequent vehicle decision-making and control, and posing driving safety hazards. Vehicle attitude signals accurately and in real-time reflect the current attitude change and specific degree of deviation. Therefore, determining the corresponding attitude compensation amount based on this vehicle attitude signal allows for targeted quantitative correction of detection data deviations caused by vehicle attitude shifts. This attitude compensation amount can adjust the acquisition reference of the detection devices or correct the collected deviation data, effectively offsetting the adverse effects of vehicle attitude changes. This ensures that various detection devices always complete data acquisition and processing with accurate references, guaranteeing the accuracy and reliability of vehicle environmental perception, positioning, and subsequent decision-making and control, providing crucial technical support for stable and safe vehicle operation.

[0051] In one possible implementation, during actual use, car doors are subjected to various factors such as road impacts, vehicle vibrations, mechanical forces from opening and closing, and changes in ambient temperature and humidity. This makes them highly susceptible to unexpected deformations, including elastic deformation and minor structural deformations. The sensors and actuators on the car door used for anti-pinch, closure detection, and sealing control are all installed and operate with reference to the original structural state of the door. When the door deforms, the operating references of these components shift accordingly. If uncorrected original detection data or control commands are directly used for door-related functions, the reference deviation caused by deformation will lead to distorted detection results, a significant decrease in control accuracy, and may even cause false triggering of anti-pinch mechanisms, misjudgment of closure completion, and actuator malfunctions. To address issues such as operational deviations and functional failures, the deformation compensation amount is determined based on the door deformation signal. This allows for precise capture of the real-time deformation state, direction, and degree of deformation of the door under different operating conditions. This compensation amount can then be used to quantitatively compensate for deviations in the detection reference, sensor data acquisition, and actuator action reference caused by door deformation. The compensation amount is integrated into the correction of relevant door detection data and the adjustment of control commands, thus calibrating and offsetting various deviations caused by deformation. This effectively avoids the adverse effects of door deformation on its detection and control functions, ensuring the accuracy of various door detection functions and the stability and reliability of control functions. It guarantees that under various deformation conditions, the door's core functions, such as anti-pinch, closing detection, and sealing control, can be accurately and stably implemented according to design standards.

[0052] S430. The attitude compensation amount and deformation compensation amount are summed to obtain the resistance compensation amount of the target vehicle.

[0053] In one possible implementation, during the vehicle's movement, changes in vehicle attitude (such as pitch, roll, yaw, etc.) alter the vehicle's contact and interaction with the airflow and road surface, resulting in corresponding drag deviations. These deviations need to be corrected using attitude compensation. Simultaneously, components such as the vehicle's body and chassis deform due to driving forces and vibrations. These deformations alter the vehicle's original aerodynamic shape and contact area, causing another drag deviation. This deviation needs to be corrected using deformation compensation. Furthermore, the drag deviations caused by attitude changes and component deformations are independent influencing factors, and their effects on vehicle drag are directly superimposed (i.e., drag compensation = attitude compensation + deformation compensation), without complex coupling or cancellation relationships. The drag compensation model is a dedicated model established to correct deviations in vehicle driving drag. It can accurately adapt to the calculation logic of vehicle drag-related parameters. Therefore, by summing the attitude compensation amount and deformation compensation amount based on this drag compensation model, the combined effect of attitude change and component deformation on the target vehicle's driving drag can be comprehensively considered. The drag deviation correction amount brought by the two types of factors is integrated to accurately obtain the target vehicle's drag compensation amount that can fully cover the influence of both attitude and deformation factors. This achieves a comprehensive correction of the calculation error of the vehicle's actual driving drag, making the calculation result of the drag compensation amount highly consistent with the actual driving conditions of the vehicle, improving the accuracy and reliability of drag estimation, and providing accurate drag parameter support for subsequent vehicle power control, energy consumption optimization and other operations.

[0054] The anti-pinch control method for electric windows provided in this application inputs vehicle attitude signals and door deformation signals into a resistance compensation model. Based on the vehicle attitude signals, the resistance compensation model determines the attitude compensation amount, effectively quantifying the dynamic changes in baseline resistance caused by the gravitational component when the vehicle is not parked horizontally, thus avoiding resistance judgment errors caused by differences in vehicle attitude. Based on the door deformation signals, the deformation compensation amount is determined, accurately capturing the permanent offset of the glass guide rail friction resistance baseline caused by door frame deformation, compensating for the technical deficiencies of traditional solutions in dealing with structural deformation. Then, the attitude compensation amount and deformation compensation amount are summed to obtain the resistance compensation amount for the target vehicle adapted to the current actual working conditions. This calculation method achieves quantitative integration of the two core working condition influencing factors: vehicle attitude change and door structural deformation, accurately characterizing the overall drift of baseline resistance under the current working conditions. This provides a quantitative basis for subsequent dynamic adjustment of the target anti-pinch force threshold, fundamentally solving the problems of false anti-pinch and missed anti-pinch caused by baseline resistance drift in traditional fixed threshold solutions.

[0055] Optionally, the method described above determines the attitude compensation amount based on the vehicle attitude signal, including: The vehicle attitude signals are fused to obtain the longitudinal tilt angle and lateral tilt angle of the target vehicle body.

[0056] In one possible implementation, the anti-pinch system reads raw data from the IMU (Integrated Mutual Memory) at a high frequency of 100Hz from the vehicle's CAN bus. This raw data mainly includes three-axis accelerometer data (detecting linear acceleration in the X, Y, and Z directions) and three-axis gyroscope data (detecting angular rates around the X, Y, and Z axes). These two types of data are the core basis for calculating the vehicle's tilt angle. Under static vehicle posture (e.g., without acceleration, braking, or turning), the accelerometer detects a resultant acceleration approximately equal to the gravitational acceleration g, allowing for a preliminary calculation of the tilt angle using the gravitational component, but it is susceptible to vibration interference. The gyroscope, on the other hand, can calculate angle changes through angular rate integration, offering good high-frequency response and vibration resistance, but suffers from zero drift and cumulative errors. The two systems need to be fused to compensate for each other's shortcomings. Specifically, after data acquisition, preprocessing is performed to remove zero drift and filter noise, retaining only the effective data under static vehicle posture (i.e., vehicle posture signals). Subsequently, a preset complementary filtering or Kalman filtering algorithm is used for fusion processing.

[0057] If a pre-defined complementary filtering algorithm is used, the tilt angle change is first calculated by integrating the gyroscope angular rate. Refer to formula (3) below.

[0058] The tilt angle of the gyroscope in this step = the tilt angle of the previous filter output + the angular rate × T (Formula 3) The sampling period T is 0.01s, corresponding to 100Hz sampling.

[0059] Then, the longitudinal tilt angle and the lateral tilt angle are calculated using the accelerometer according to the following formula (4).

[0060] Accelerometer longitudinal tilt angle θ x 1=arctan(a_X / √(a_Y²+a_Z²)); accelerometer lateral tilt angle θ 1=arctan(a_Y / √(a_X²+a_Z²)) formula (4) Where a_X, a_Y, and a_Z are the triaxial accelerometer values, and g is the gravitational acceleration, which is typically taken as 9.8 m / s².

[0061] Then, the static observation tilt angle is calculated and low-pass filtered. Finally, the longitudinal tilt angle and lateral tilt angle of the vehicle body are output by fusion correction through preset filter coefficients α (0.98~0.99, pre-stored calibration value). It can be obtained by the following formula (5).

[0062] θ x =α×current gyroscope tilt angle + (1-α)×θx 1; θ =α×current gyroscope tilt angle + (1-α)×θ 1Formula (5) If a pre-defined Kalman filter algorithm is used, the optimal tilt angle estimate is obtained by automatically allocating the weights of the gyroscope predictions and accelerometer observations through a closed loop of "state prediction - prediction error covariance update - Kalman gain calculation - state update - error covariance update". Finally, through the above fusion processing, the longitudinal tilt angle θ of the vehicle relative to the horizontal plane is calculated. x and lateral tilt angle θ The accuracy can reach within ±0.5°.

[0063] The attitude compensation amount is determined based on the longitudinal tilt angle and the lateral tilt angle.

[0064] In one possible implementation, based on the longitudinal tilt angle θ x and lateral tilt angle θ The attitude compensation amount can be determined by the following formula (6).

[0065] Attitude compensation amount = k1·|sinθ x | + k2·|sinθ |Formula (6) The attitude compensation amount is used to counteract the baseline drag drift caused by vehicle tilt; the sine function |sinθ is used in formula (6). x |and|sinθ This is because it can accurately convert the longitudinal and lateral tilt angles into effective gravity components along the direction of glass lifting and lowering, i.e., the longitudinal tilt angle θ when going uphill. x If positive, then sinθ x A positive value is used to calculate the load reference offset caused by changes in the component of gravity; the longitudinal tilt angle θ during downhill sections. x If it is negative, then sinθ x The absolute value is also used to quantify the reference offset, ensuring that the impact of changes in the component of gravity on the load reference can be accurately quantified regardless of whether the vehicle is tilted forward or backward.

[0066] k1 and k2 are tilt angle compensation coefficients, which need to be pre-calibrated through bench testing. Their physical meaning is the influence of the gravitational component on the window resistance caused by a unit tilt angle (sine value), and they are fixed parameters pre-stored in the anti-pinch system. In actual calculations, the anti-pinch system first calls the calculated longitudinal tilt angle θ. x and lateral tilt angle θ The absolute value of each sine value is calculated, and then multiplied by the calibrated k1 and k2. The sum of the two values ​​is the attitude compensation amount. This attitude compensation amount directly reflects the increase (or decrease) in the resistance of the window baseline caused by the component of gravity under the current vehicle tilt state. It is the core basis for the subsequent synthesis of dynamic anti-pinch threshold and provides quantitative support for solving the problems of false anti-pinch and missed anti-pinch caused by vehicle tilt in traditional fixed threshold.

[0067] The anti-pinch control method for electric windows provided in this application fuses vehicle attitude signals to obtain the longitudinal and lateral tilt angles of the target vehicle body, effectively eliminating noise interference and cumulative drift problems of single sensors, and ensuring the accuracy and stability of tilt angle detection. Based on the calculated longitudinal and lateral tilt angles, the actual impact of the component of gravity along the glass lifting direction on the baseline resistance is quantified and the attitude compensation amount is determined, so that the attitude compensation amount is highly matched with the actual working condition of the vehicle not being parked horizontally. This provides a reliable physical basis for the accurate calculation of the subsequent dynamic anti-pinch threshold, thus laying the core foundation for the anti-pinch system to adapt to different vehicle attitude conditions such as uphill, downhill, and side slope, and effectively avoiding baseline resistance drift interference caused by the component of gravity.

[0068] Optionally, the above method, based on the door deformation signal, determines the deformation compensation amount, including: The door deformation signal and the reference pressure signal are compared.

[0069] The reference pressure signal is used to characterize the target vehicle before it leaves the factory or during the calibration phase. The target vehicle is placed in a reference state, and distributed pressure sensors collect the full-area pressure distribution of the door sealing strips (especially the felt grooves and rubber strips that mate with the glass guide rails, as the pressure distribution of the sealing strips directly reflects the contact state between the glass and the guide rails). This includes at least the magnitude, distribution location, and uniformity of the contact pressure. After filtering, noise reduction, and normalization, the signal is stored as the reference pressure signal, forming a standardized pressure distribution database. This reference pressure signal indirectly maps the contact gap and frictional resistance between the glass and the guide rails under the reference state, providing a "reference scale" for subsequent comparisons of pressure changes caused by door deformation.

[0070] In one possible implementation, the door deformation signal is acquired in real time by onboard sensors (such as an IMU inertial measurement unit, door pressure sensor, or displacement sensor) to capture minor door frame deformations caused by minor vehicle collisions or long-term use. This data is then converted into quantifiable electrical signals (such as pressure values ​​or displacement amounts), and the acquisition range covers the corresponding area of ​​the glass guide rail. The onboard controller (MCU / ECU) performs point-by-point, full-process quantification and comparison of the two sets of signals (i.e., the door deformation signal and the reference pressure signal) to calculate the deviation between the pressure signal corresponding to the real-time door deformation and the reference pressure signal (such as pressure difference or distribution offset). This provides data support for subsequently determining the compensation amount. Essentially, it uses the difference in pressure signals to infer the degree of influence of door deformation on the frictional resistance of the guide rail.

[0071] It should be noted that abnormal pressure signal distribution indicates that there may be deformation in the door frame that is difficult to detect with the naked eye. Based on the comparison results, the deformation compensation amount is determined.

[0072] In one possible implementation, the deformation compensation amount is determined based on the comparison results using the following formula (7).

[0073] Deformation compensation amount = k3 * δ Formula (7) Wherein, k3 is the deformation compensation coefficient, reflecting the average impact of a unit change in door deformation on the resistance to glass movement. δ is the deformation index, which is the comparison result between the door deformation signal and the reference pressure signal. The larger the deformation index δ, the more obvious the door deformation, the greater the contact pressure between the guide rail and the glass, and the greater the increase in the frictional resistance baseline.

[0074] The deformation compensation amount can be used to correct the original fixed threshold or baseline resistance, solve the problem that the actual clamping resistance increment caused by door deformation cannot exceed the fixed threshold, resulting in missed anti-pinch, and realize the dynamic adaptation of the anti-pinch system to the door deformation condition.

[0075] The anti-pinch control method for electric windows provided in this application compares the door deformation signal and the reference pressure signal. Based on the comparison results, the anti-pinch system can determine the deformation compensation amount to quantify the influence of door deformation on the pressure distribution of the sealing strip and the frictional resistance of the glass guide rail. This provides a reliable quantitative compensation basis for the accurate correction of the baseline resistance of the subsequent window lifting, effectively avoiding the problem of permanent drift of the baseline resistance caused by the deformation of the door frame, and ensuring the accuracy and reliability of the subsequent anti-pinch system's resistance anomaly judgment.

[0076] Optionally, the above method, based on the door deformation signal, determines the deformation compensation amount, including: Based on the door deformation signal and the preset reference door deformation signal, the second deviation of the door deformation signal is determined.

[0077] The preset reference door deformation signal is the ideal operating condition baseline value pre-stored by the anti-pinch system. The ideal operating condition refers to a door without collision, aging, or deformation, with the glass guide rail in its standard installation position and the frame in normal shape. This ideal operating condition baseline value is determined through bench calibration before the vehicle leaves the factory, and combined with the guide rail material and door structural characteristics, it is solidified as the anti-pinch system's baseline parameter. This parameter serves as a reference standard for judging whether the door has deformed and the degree of deformation, ensuring consistency with the ideal operating condition of the baseline resistance.

[0078] In one possible implementation, onboard sensors collect actual deformation data of the car door. These sensors need to specifically monitor the deformation state of the door frame and glass guide rails. Displacement sensors, pressure sensors, or vehicle posture-related sensors built into the door can be used to capture in real time the minute deformations of the door frame caused by collisions or aging (such as guide rail misalignment or slight frame bending), and convert the physical quantities of deformation (displacement, angle, pressure, etc.) into calculable electrical signals, i.e., the door deformation signal.

[0079] Then, the difference between the actual door deformation signal and the preset reference door deformation signal is calculated (usually the actual door deformation signal minus the preset reference door deformation signal) to obtain the second deviation amount △F2. The magnitude of this second deviation amount △F2 directly reflects the severity of the door deformation. If the second deviation amount △F2 is 0, it means that the door has no deformation and the guide rail friction resistance is at the baseline level. If the deviation amount is positive (or the deviation meets the preset threshold range), it means that the door has deformation. The larger the second deviation amount △F2 is, the more significant the influence of deformation on the guide rail is, and the greater the increase in the friction resistance baseline.

[0080] Based on the second deviation, the deformation compensation amount is determined.

[0081] In one possible approach, a mapping relationship is established between the second deviation ΔF2 and the deformation compensation amount. This involves pre-storing a "second deviation - deformation compensation" correspondence model (or lookup table) within the anti-pinch system, calibrated through bench testing. This model is based on parameters such as the door structure, guide rail material, and friction characteristics, precisely quantifying the increase in guide rail friction resistance corresponding to different deformation deviations. For example, by experimentally determining that when the second deviation ΔF2 reaches a certain value, the guide rail friction resistance will increase by a corresponding value, this correspondence is solidified into the algorithm logic, ensuring that the compensation amount accurately offsets the resistance drift caused by deformation.

[0082] Then, based on this mapping model, the corresponding increase in guide rail friction resistance is matched according to the determined second deviation ΔF2. This increase in guide rail friction resistance is the deformation compensation amount. The physical meaning of the deformation compensation amount is the portion of the guide rail friction resistance baseline that increases due to door deformation. Its value is positively correlated with the second deviation ΔF2. That is, the larger the second deviation ΔF2, the more severe the door deformation, the greater the increase in guide rail friction resistance baseline, and the larger the deformation compensation amount, ensuring accurate coverage of the resistance deviation caused by deformation. Finally, the determined deformation compensation amount is transmitted to the resistance compensation module of the anti-pinch system, and superimposed with the attitude compensation amount caused by the gravity component to jointly constitute the total resistance compensation amount ΔF, which is used to dynamically correct the anti-pinch trigger threshold.

[0083] It should be noted that whether the deformation compensation amount is determined based on the second deviation amount or based on the comparison result, the deformation compensation amount can be calculated according to the above formula (7). The deformation compensation amount is determined based on the second deviation amount, where the second deviation amount is the deformation index δ in formula (7), and the deformation compensation coefficient k3 remains unchanged.

[0084] In addition, it should be noted that the resistance compensation amount ΔF can be calculated using the above formulas (6) and (7), and can be expressed by the following formula (8): ΔF = k1 * |sin(θx)| + k2 * |sin(θy)| + k3 * δ formula (8) Among them, the resistance compensation amount ΔF is a calculation formula built into the resistance compensation model.

[0085] The anti-pinch control method for electric windows provided in this application compares and analyzes the door deformation signal with a preset benchmark door deformation signal to determine a second deviation of the door deformation signal, thereby quantifying the actual deformation degree of the door frame and achieving calibration detection of door deformation. Based on the second deviation, a deformation compensation amount is determined to convert the change in frictional resistance of the glass guide rail caused by door deformation into a quantifiable compensation parameter. This provides a reliable quantitative basis for the subsequent anti-pinch system to accurately correct the baseline resistance and offset the resistance baseline drift caused by door deformation, effectively avoiding the risk of missed anti-pinch due to permanent door deformation and improving the adaptability of the anti-pinch system to door deformation conditions.

[0086] Figure 5 A flowchart illustrating an anti-pinch control method for an electric window provided in this application embodiment. Figure 4 .like Figure 5 As shown, the above method also includes: S510 Record the vehicle status data during each normal closing process of the target vehicle's power window.

[0087] The vehicle status data includes at least: vehicle attitude signal, door deformation signal and corresponding window resistance signal.

[0088] The normal closing process refers to the real-time monitoring of motor current and Hall sensor speed signals by the window anti-pinch system. If there is no parameter deviation exceeding the temporary threshold, no reversal command is triggered, and the window smoothly closes from the fully open position to the sealed state, it is considered a normal closing.

[0089] In one possible implementation, when it is determined that the target vehicle's power window is normally closed, the recording timing is determined from the moment the window closing command is issued until the window triggers the sealing feedback signal at the end of its travel. The entire process is synchronized with the window closing action to ensure data timing consistency. The data storage medium is either the local cache of the vehicle's ECU (Electronic Control Unit) or the storage module of the body domain controller. Data is packaged and stored as "single closing events," associated with timestamps and window numbers (e.g., front left / front right), facilitating subsequent traceability and model retrieval.

[0090] It should be noted that triggering the data recording process requires excluding data from abnormal scenarios such as accidental clamping, missed clamping, and glass jamming.

[0091] S520: Based on vehicle state data, the parameters of the resistance compensation model are updated and learned.

[0092] In one possible implementation, the drag compensation model first calculates the corresponding drag compensation amount ΔF based on the vehicle attitude signal and door deformation signal from the vehicle state data, correcting the original fixed threshold. Therefore, the parameters of this drag compensation model mainly include: tilt compensation coefficients k1 and k2, and deformation compensation coefficient k3. The update learning adopts an incremental iterative mode, that is, after each normal window closure, the recorded three types of data are input into the drag compensation model and compared with the predicted drag value calculated by the current parameters of the drag compensation model to calculate the prediction deviation (i.e., the difference between the actual drag signal and the compensated drag value output by the drag compensation model). If the prediction deviation exceeds the preset accuracy range (e.g., ±5%), parameter updates are triggered. For example, for vehicle attitude data, the tilt compensation coefficients k1 and k2 in the gravity component decomposition formula (6) are optimized so that the resistance compensation model can accurately match the additional influence of gravity component on resistance under different tilt angles; for door deformation data, the guide rail friction correction coefficient is adjusted to adapt to the resistance baseline drift caused by deformation; at the same time, combined with historical data of similar working conditions (such as resistance data under the same tilt angle and similar deformation degree), weighted iteration is performed to avoid parameter mutation caused by single abnormal data (such as instantaneous vibration interference). The updated parameters cover the resistance compensation model parameters stored in the ECU in real time. When the window is closed later, the resistance compensation model can call the latest parameters to calculate the resistance compensation amount ΔF, dynamically adjust the target anti-pinch trigger threshold, and achieve adaptive adaptation to complex working conditions.

[0093] The anti-pinch control method for electric windows provided in this application involves the anti-pinch system collecting and recording corresponding vehicle status data during each normal closing process of the target vehicle's electric window. Based on this multi-dimensional vehicle status data, the system dynamically updates and autonomously learns the parameters of the resistance compensation model. This enables the resistance compensation model to continuously adapt to the window baseline resistance drift caused by complex working conditions such as vehicle posture changes and door deformation, and continuously corrects the resistance compensation model parameters to conform to the actual change law of window resistance. This effectively improves the working condition adaptability and resistance calculation accuracy of the resistance compensation model, providing reliable resistance compensation model support for the dynamic and accurate adjustment of the target anti-pinch force threshold of the electric window.

[0094] Optionally, the above method updates and learns the parameters of the resistance compensation model based on vehicle state data, including: Based on the vehicle attitude signal and door deformation signal in the vehicle state data, the predicted resistance deviation of the resistance compensation model is determined.

[0095] In one possible implementation, the resistance compensation model is a pre-defined mathematical model that incorporates the correspondence between vehicle attitude signals (gravity component), door deformation signals (guide rail friction increment), and window baseline resistance deviation, i.e., the above formula (8). By substituting the real-time collected and processed vehicle attitude signals and door deformation signals into this resistance compensation model, the theoretical deviation of the baseline resistance under the current working condition (such as uphill or slight door deformation) compared to the ideal working condition can be calculated, i.e., the predicted resistance deviation. This predicted resistance deviation is essentially a prediction of the resistance drift caused by gravity component and guide rail deformation.

[0096] Calculate the error between the window resistance signal in the vehicle status data and the predicted resistance deviation.

[0097] In one possible implementation, the resistance of the window is calculated by real-time acquisition of the motor's operating current or the pulse frequency of a Hall sensor (pulse frequency is used to calculate motor speed) and combined with motor characteristic parameters (such as motor internal resistance and torque coefficient). Since the resistance of the window lift is directly and positively correlated with the motor load—the greater the resistance, the greater the motor load, resulting in a larger current and more significant speed changes—these two types of parameters can be indirectly converted into a real-time window resistance signal (including the ideal baseline resistance, the drift portion corresponding to the predicted resistance deviation, and other random interference). Then, using the ideal window baseline resistance as a benchmark, it is added to the predicted resistance deviation to obtain the theoretical total window resistance under the current operating condition. Finally, the real-time acquired and converted window resistance signal is subtracted from this theoretical total window resistance under the current operating condition; the result is the error between the two. This error reflects the prediction accuracy of the resistance compensation model. The larger the error, the more significant the deviation between the preset parameters of the resistance compensation model and the actual operating condition, requiring adjustment of the resistance compensation model parameters through subsequent algorithms. By using a pre-defined recursive least squares algorithm and error, the parameters of the resistance compensation model are iteratively updated to obtain the parameters of the resistance compensation model.

[0098] Among them, the selection of the preset recursive least squares algorithm is adapted to the needs of the vehicle embedded system. Compared with the ordinary least squares algorithm, it does not need to store all historical data. It can gradually optimize parameters through "real-time recursion" to meet the real-time requirements of 100Hz high-frequency acquisition and millisecond-level response of the window anti-pinch system.

[0099] In one possible implementation, the calculated error is used as input to a pre-defined recursive least squares algorithm. This algorithm dynamically adjusts the parameters in the resistance compensation model (such as tilt compensation coefficients k1 and k2, and deformation compensation coefficient k3) based on the criterion of minimizing the sum of squared errors. For example, each time new vehicle state data is collected, the deviation calculation and error solving process of the previous two steps is repeated. Then, the parameters of the resistance compensation model are iteratively updated using the new error by the pre-defined recursive least squares algorithm, overwriting the old parameters from the previous round. During the iteration process, the pre-defined recursive least squares algorithm continuously corrects the parameters to reduce the error, making the resistance deviation predicted by the resistance compensation model increasingly closer to the actual window resistance drift. After multiple rounds of iteration and convergence, the optimal resistance compensation model parameters adapted to the current vehicle state are finally obtained, ensuring that the resistance compensation model can accurately and dynamically compensate for baseline resistance drift and solve the problems of false and missed anti-pinch measures in fixed threshold schemes.

[0100] The anti-pinch control method for electric windows provided in this application accurately determines the predicted resistance deviation of the resistance compensation model based on vehicle attitude signals and door deformation signals in vehicle state data, specifically capturing the baseline resistance drift caused by non-horizontal parking and door frame deformation. It then calculates the error between the actual window resistance signal in the vehicle state data and the predicted resistance deviation, providing a precise basis for parameter optimization of the resistance compensation model. Subsequently, a preset recursive least squares algorithm is used to iteratively update the parameters of the resistance compensation model in real time, ultimately obtaining the optimal parameters of the resistance compensation model that adapt to the current actual vehicle conditions. This process relies on the efficient computational characteristics of the preset recursive least squares algorithm to achieve rapid and accurate parameter correction of the resistance compensation model, allowing the resistance compensation model to continuously conform to the dynamic changes in the actual window resistance, laying a precise and reliable model foundation for the subsequent dynamic adaptation of the anti-pinch threshold.

[0101] To facilitate understanding of the above-mentioned anti-pinch control method for electric windows, this application embodiment also provides a specific example of the anti-pinch control method for electric windows: First, the data vector φ(k) = [|sin(θx(k))|,|sin(θy(k))|,δ(k)]^T is defined as consisting of the absolute values ​​of the longitudinal tilt angle sine and the lateral tilt angle sine collected at a specific moment k when the window is raised, and the door deformation index δ. The parameter vector θ = [k1,k2,k3]^T to be identified consists of three compensation coefficients that need to be fine-tuned.

[0102] During the initial run of the anti-pinch system, the RLS (Recursive Least Squares) algorithm is initialized. The parameter vector θ is initialized to a small non-zero vector, and the covariance matrix P is initialized to an identity matrix with large diagonal elements. This reflects the uncertainty of the initial parameters of the anti-pinch system, making the RLS algorithm more sensitive to new data in the initial stage. Afterwards, the system enters the real-time data acquisition and processing stage. During each normal window rise without triggering the anti-pinch mechanism, the system continuously records the data vector φ(k) and the actual total resistance F_current(k) of the window calculated from the current or rotational speed. Upon acquiring new observation data, the RLS algorithm's recursive coefficient update step is initiated, completing the recursive parameter updates in a predetermined order. First, the gain matrix K(k) that determines the weight of the new observation data on the parameter correction is calculated. Then, the prediction error between the resistance compensation amount predicted by the model and the actual resistance measurement value is calculated. Next, according to the rule of "new estimate = old estimate + gain matrix × prediction error", the current parameter vector θ is corrected using the gain matrix K(k) and the prediction error. Finally, the covariance matrix P(k) is updated to prepare for the next update. After the parameter update is completed, the updated parameter vector θ(k) will be directly used to calculate the dynamic target anti-pinch force threshold for the next window lifting. This process of data collection, parameter update, and parameter activation will be continuously repeated with each use of the window to achieve continuous optimization of the compensation coefficient.

[0103] Taking the example of a vehicle parked daily on a fixed 10° longitudinal slope with its doors and windows closed, the learning process of this anti-pinch control method can be clearly demonstrated. Initially, calibration is performed on a level road surface. Since the influence of gravity is considered minimal, the tilt compensation coefficient k1_initial = 0.5 N is set. On the first day, when the vehicle's window is raised on the slope, the anti-pinch system measures θx = 10° (sin(10°)≈0.17). Due to the reduced gravity, theoretically, the load benchmark for the current operating condition should be approximately ΔF_predicted lower than that on a level road surface. However, the resistance compensation ΔF_predicted predicted by the initial model calibrated for the level operating condition is only 0.085 N, a significant deviation from the actual required compensation. The RLS algorithm significantly adjusts its parameters based on this deviation, increasing the tilt compensation coefficient k1 from 0.5 to 8.0 N. On the second day, under the same conditions, the actual resistance measured when the window is raised is still about 10 N higher than when it is level. At this time, the resistance compensation model predicts a resistance compensation of 8.0 * 0.17 = 1.36 N. Although the prediction result is still low, it is much more accurate than the first day, with a corresponding prediction deviation of 8.64 N. The RLS algorithm will continue to adjust the tilt compensation coefficient k1, with an update range much smaller than the first day, for example, adjusting it to 9.5 N. After about 20 such continuous learning iterations, the tilt compensation coefficient k1 will gradually converge to a stable value of 10.2 N. At this time, the resistance compensation model predicts a resistance compensation of about 1.73 N, and the actual resistance increment fluctuates slightly around 10 N. The deviation between the two is very small, and the algorithm determines that the resistance compensation model can accurately predict the resistance effect of this slope condition. The subsequent parameter update range will become extremely small, and the anti-pinch system thus achieves accurate adaptation to this specific condition.

[0104] Based on the same inventive concept, this application also provides an anti-pinch control device for electric windows. Since the principle of the device in this application is similar to the anti-pinch control method for electric windows described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0105] Figure 6 This is a schematic diagram of the structure of an anti-pinch control device for an electric window provided in an embodiment of this application. Figure 6 As shown, the anti-pinch control device 600 for the electric window may include: The acquisition module 601 is used to acquire the vehicle attitude signal, door deformation signal and current window resistance signal of the target vehicle; The compensation module 602 is used to compensate the reference anti-pinch force threshold based on the vehicle attitude signal and the door deformation signal to obtain the target anti-pinch force threshold. The determination module 603 is used to determine the first deviation of the current window resistance signal based on the current window resistance signal and the baseline resistance signal. The baseline resistance signal is used to represent the inherent resistance that the electric window needs to overcome when it is raised or lowered. The control module 604 is used to control the power window to perform an anti-pinch action if the first deviation is greater than the target anti-pinch force threshold.

[0106] In one optional implementation, the compensation module 602 is specifically used to: input the vehicle attitude signal and the door deformation signal into the resistance compensation model for processing to obtain the resistance compensation amount of the target vehicle; the resistance compensation amount is used to characterize the degree of influence of the vehicle attitude signal and the door deformation signal on the baseline resistance signal; and correct the reference anti-pinch force threshold based on the resistance compensation amount to obtain the target anti-pinch force threshold.

[0107] In one optional implementation, the compensation module 602 is specifically used to: input the vehicle attitude signal and the door deformation signal into the drag compensation model; calculate the attitude compensation amount corresponding to the vehicle attitude signal and the deformation compensation amount corresponding to the door deformation signal through the drag compensation model; and sum the attitude compensation amount and the deformation compensation amount to obtain the drag compensation amount of the target vehicle.

[0108] In one optional implementation, the compensation module 602 is specifically used to: perform fusion processing on the vehicle attitude signal to obtain the longitudinal tilt angle and lateral tilt angle of the target vehicle body; and determine the attitude compensation amount based on the longitudinal tilt angle and lateral tilt angle.

[0109] In one optional implementation, the compensation module 602 is specifically used to: compare the door deformation signal and the reference pressure signal; wherein the reference pressure signal is used to characterize the pressure distribution of the door sealing strip of the target vehicle in a reference state; and determine the deformation compensation amount based on the comparison result.

[0110] In one optional implementation, the compensation module 602 is specifically used to: determine a second deviation of the door deformation signal based on the door deformation signal and a preset reference door deformation signal; and determine a deformation compensation amount based on the second deviation.

[0111] In an optional implementation, the compensation module 602 is further configured to: record vehicle status data during each normal closing process of the target vehicle's electric window; the vehicle status data includes at least: vehicle posture signal, door deformation signal and corresponding window resistance signal; and update and learn the parameters of the resistance compensation model based on the vehicle status data.

[0112] In one optional implementation, the compensation module 602 is specifically used to: determine the predicted resistance deviation of the resistance compensation model based on the vehicle attitude signal and door deformation signal in the vehicle state data; calculate the error between the window resistance signal in the vehicle state data and the predicted resistance deviation; and iteratively update the parameters of the resistance compensation model using a preset recursive least squares algorithm and the error to obtain the parameters of the resistance compensation model.

[0113] It should be noted that for details not disclosed in the anti-pinch control device for electric windows in this application embodiment, please refer to the details disclosed in the anti-pinch control method for electric windows in this application embodiment, which will not be repeated here.

[0114] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0115] Optionally, embodiments of this application also provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor performs the steps of the anti-pinch control method for the electric window of the movable storage medium in the above embodiments. The specific implementation and technical effects are similar and will not be described again here.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0117] Optionally, this embodiment also provides a computer program product, which, when run on a computer, causes the computer to perform the above-mentioned related steps to implement the anti-pinch control method for electric windows provided in the above embodiment.

[0118] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0119] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0120] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for preventing pinching of electric windows, characterized in that, include: Acquire the target vehicle's attitude signal, door deformation signal, and current window resistance signal; Based on the vehicle attitude signal and the door deformation signal, the reference anti-pinch force threshold is compensated to obtain the target anti-pinch force threshold. Based on the current window resistance signal and the baseline resistance signal, a first deviation of the current window resistance signal is determined, wherein the baseline resistance signal is used to represent the inherent resistance that the electric window needs to overcome when it is raised or lowered. If the first deviation is greater than the target anti-pinch force threshold, then the power window is controlled to perform an anti-pinch action.

2. The method according to claim 1, characterized in that, The step of compensating for the baseline anti-pinch force threshold based on the vehicle attitude signal and the door deformation signal to obtain the target anti-pinch force threshold includes: The vehicle attitude signal and the door deformation signal are input into the drag compensation model for processing to obtain the drag compensation amount of the target vehicle; the drag compensation amount is used to characterize the degree of influence of the vehicle attitude signal and the door deformation signal on the baseline drag signal; The reference anti-pinch force threshold is corrected based on the resistance compensation amount to obtain the target anti-pinch force threshold.

3. The method according to claim 2, characterized in that, The step of inputting the vehicle attitude signal and the door deformation signal into the drag compensation model for processing to obtain the drag compensation amount of the target vehicle includes: The vehicle attitude signal and the door deformation signal are input into the drag compensation model; Using the aforementioned resistance compensation model, the attitude compensation amount corresponding to the vehicle attitude signal and the deformation compensation amount corresponding to the door deformation signal are calculated respectively. The attitude compensation amount and the deformation compensation amount are summed to obtain the drag compensation amount of the target vehicle.

4. The method according to claim 3, characterized in that, Determining the attitude compensation amount based on the vehicle attitude signal includes: The vehicle attitude signal is fused to obtain the longitudinal tilt angle and lateral tilt angle of the target vehicle body; The attitude compensation amount is determined based on the longitudinal tilt angle and the lateral tilt angle.

5. The method according to claim 3, characterized in that, The step of determining the deformation compensation amount based on the door deformation signal includes: The door deformation signal and the reference pressure signal are compared; wherein, the reference pressure signal is used to characterize the pressure distribution of the door sealing strip of the target vehicle under reference conditions; Based on the comparison results, the deformation compensation amount is determined.

6. The method according to claim 3, characterized in that, The step of determining the deformation compensation amount based on the door deformation signal includes: Based on the door deformation signal and the preset reference door deformation signal, a second deviation of the door deformation signal is determined; The deformation compensation amount is determined based on the second deviation.

7. The method according to claim 2, characterized in that, The method further includes: Record vehicle status data during each normal closing process of the target vehicle's power windows; the vehicle status data includes at least: the vehicle attitude signal, the door deformation signal, and the corresponding window resistance signal; Based on the vehicle state data, the parameters of the resistance compensation model are updated and learned.

8. The method according to claim 7, characterized in that, The step of updating and learning the parameters of the resistance compensation model based on the vehicle state data includes: Based on the vehicle attitude signal and the door deformation signal in the vehicle state data, the predicted resistance deviation of the resistance compensation model is determined. Calculate the error between the window resistance signal in the vehicle status data and the predicted resistance deviation; The parameters of the resistance compensation model are iteratively updated using a pre-defined recursive least squares algorithm and the error, thus obtaining the parameters of the resistance compensation model.

9. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that cause a computer to perform the method as described in any one of claims 1 to 8.

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