A self-learning calibration method after height sensor switching

Abstract

The application discloses a self-learning calibration method after height sensor switching and relates to the technical field of automobile electronic control and sensor calibration, and comprises the following steps: in the vehicle factory stage, the height sensor calibration is completed through an external height measuring device, the old height sensor bias value corresponding to each wheel, the real physical height and the air spring pressure data are acquired, the vehicle body posture angle data in the standard horizontal state of the vehicle are recorded, and after the air spring is completely exhausted to the mechanical limiting position, the original output signal of the old height sensor at the position is collected, the corresponding physical height is calculated, and the bias value, the real physical height, the air spring pressure data, the vehicle body posture angle data and the mechanical limiting position physical height are stored together. The application realizes pure software calibration, does not need external equipment, relies on self-learning and multiple physical benchmarks to ensure high precision, has online fault diagnosis capacity, is simple to realize, low in cost and suitable for embedded system deployment.

Description

Technical Field

[0001] This invention relates to the field of automotive electronic control and sensor calibration technology, specifically to a self-learning calibration method after a height sensor switch. Background Technology

[0002] The air suspension system adjusts the vehicle height by controlling the air pressure inside the air springs to balance vehicle passability, handling stability, and ride comfort. The height sensor, as the core feedback element of the air suspension closed-loop control, directly determines the height control precision through the accuracy of its output signal. During vehicle mass production, OEMs typically use specific models of height sensors and calibrate them at specialized production line stations using high-precision external height measurement equipment (such as infrared height measurement equipment). During calibration, the suspension is adjusted to the design height, and the bias values ​​of each height sensor are recorded and stored in the non-volatile memory of the suspension ECU. These bias values, along with the sensor's MAP function, work together to convert the sensor's raw output signal into the actual physical height value.

[0003] Once a vehicle enters mass production, due to supply chain adjustments or component upgrades, it may be necessary to switch from an older model to a newer one for the height sensor. The output characteristics of the old and new sensors differ, altering their MAP (Modular Mapping) relationship, rendering the original bias values ​​unusable and requiring recalibration. Current technologies, such as using external diagnostic equipment to take over suspension control and initialize the reference position, or using a program to control the suspension at different height positions to read sensor values ​​for calibration, all rely on external measuring equipment or known position information. Calibration cannot be completed independently at a dedicated work station, especially for vehicles already sold, which require return to a repair shop. Furthermore, current technologies only address the initial calibration problem, neglecting data migration after sensor hardware changes and failing to fully utilize existing pressure sensors, IMUs, and historical data, thus failing to achieve a self-calibration process without external equipment.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a self-learning calibration method after switching altitude sensors, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a self-learning calibration method after a height sensor switch, comprising the following steps: During the vehicle manufacturing process, the height sensor is calibrated using an external height measurement device. This process acquires the old height sensor offset value, actual physical height, and air spring pressure data for each wheel. It also records the vehicle body attitude angle data when the vehicle is in a standard level state. Simultaneously, after the air springs are fully vented to the mechanical limit position, the original output signal of the old height sensor at that position is collected, and the corresponding physical height is calculated. The offset value, actual physical height, air spring pressure data, vehicle body attitude angle data, and physical height at the mechanical limit position are stored together. During the daily operation of the vehicle, when the vehicle is stationary, the body posture angle data falls within the factory record range, and the suspension is in a stable height gear, the air spring pressure data of each gear is collected. When the deviation between the sensor output and the target height and the change in air spring pressure meet the preset conditions, the air spring pressure data corresponding to each height gear is recorded as pressure-height correspondence data, forming a multi-gear pressure mapping relationship. After completing the height sensor model switch and obtaining the new height sensor mapping relationship, the actual physical height recorded at the factory is read. The corresponding target air spring pressure is obtained through the pressure mapping relationship and the air spring is adjusted to reach the pressure state. When the vehicle body attitude angle data meets the factory recorded range, the original output signal of the new height sensor is collected and the uncompensated height value is calculated. The new offset value is calculated based on the actual physical height and the uncompensated height value. Then, the height deviation is checked under multiple height positions. After the air spring is fully vented to the mechanical limit position, the current physical height is calculated and compared with the physical height of the mechanical limit position recorded at the factory to complete the self-learning calibration after the height sensor switch.

[0007] Preferably, focusing on the process of recording vehicle attitude parameters, highlighting the logic of acquiring and determining vehicle attitude reference data, the steps are as follows: The inertial measurement unit acquires multiple consecutive frames of roll and pitch angle data, performs time-series buffering on each frame of data, and removes abrupt values ​​and abnormal jump signals that occur during the sampling process. The cached data is averaged using a sliding window method, and the results from different time windows are weighted and fused to generate stable attitude angle reference values ​​and store them. Construct upper and lower limit ranges for attitude angles, set independent threshold ranges for roll and pitch angles respectively, and establish a bivariate joint judgment rule; The real-time attitude sampling data is input into the judgment logic, and the deviations of the roll angle and pitch angle are compared item by item, and the attitude matching status indicator is output.

[0008] Preferably, the process of acquiring extreme position data and forming height benchmarks, taking into account mechanical structure characteristics, is described as follows: Execute the exhaust control command to keep the air spring exhaust valve open and collect airbag pressure data in real time until the detected value reaches the preset atmospheric pressure judgment threshold. After the air pressure stabilizes, the suspension is kept in a mechanically limited state, and structural vibration data is continuously collected within a set time to confirm that the system is in a static state. The height sensor output signal is read multiple times, and the reading results are subjected to consistency verification and discrete value filtering. The processed raw signal is input into the mapping function for height calculation, and the offset correction value is superimposed to generate the extreme position height data and complete the storage.

[0009] Preferably, for the data collection condition filtering process under normal operating conditions, the consistency judgment logic of the triggering scenario is emphasized, and the steps are as follows: Collect vehicle operating status signals, including vehicle speed signals, wheel speed signals, and position change information, and confirm that the vehicle is completely stationary through multi-signal cross-verification; Read the door opening / closing status signal and the seat occupancy detection signal, and perform consistency verification on the status of multiple sensors; Acquire real-time attitude angle data and perform filtering processing, then compare the processing results with the attitude reference interval dimension by dimension; Extract the status of the suspension control signal, determine whether there is a height adjustment execution command, and confirm that the height setting is in a stable range.

[0010] Preferably, starting from the operational status, the process of data validity judgment and anomaly identification and handling is described, with the following steps: The current height value is calculated by the sensor output signal, and the standard height parameter corresponding to the target gear is read simultaneously to construct real-time height difference data; The height difference data is continuously sampled, the trend of deviation change is calculated, and the frequency and duration of exceeding the limit are recorded. The air spring pressure signal is sampled at high frequency, and the pressure change rate and change curve characteristics within the time window are calculated. The pressure change rate is matched with a preset change model for analysis, and anomaly identification signals and corresponding fault status records are output.

[0011] Preferably, combining data from multiple operating conditions, the data organization and recording method for the relationship between pressure and height is explained in the following steps: When the acquisition conditions are met, the air spring pressure signals of each wheel are acquired, and multi-cycle continuous sampling is performed to form pressure sequence data; The pressure sequence is filtered, outliers are removed, and stability analysis is performed to extract representative pressure values ​​within the stable interval. The pressure values ​​are categorized according to the height gear label, and the data for different wheel positions are stored independently. The pressure data for multiple gears is structured and organized to form a mapping table structure that includes gear information and pressure data for multiple gears.

[0012] Preferably, in the process of generating the multi-level pressure mapping table, a pressure change consistency judgment and data update mechanism is introduced for the pressure data corresponding to each height level. The pressure sequence formed by continuous sampling is compared with the interval stability. The data writing operation is only performed when the pressure change trend meets the preset stability judgment condition. The historical pressure records under the same level are replaced or updated, thereby forming a pressure and height mapping relationship data structure that is dynamically corrected according to the working conditions.

[0013] Preferably, focusing on the pressure acquisition stage, the numerical processing method for cases where data for different gear positions is missing is described, with the following steps: Access the mapping table structure and read the pressure data item corresponding to the target gear, while checking the data integrity flag; When no pressure data is recorded for the target gear, retrieve the data range of the adjacent recorded gears and determine the upper and lower boundary gears; The pressure data of adjacent gears are calculated to find the difference, and a linear interpolation function is constructed by combining the gear spacing parameter. Substitute the target gear position into the interpolation function to calculate the corresponding pressure value and output it for control input.

[0014] Preferably, based on the verification process after calibration, the method for verifying results under multiple operating conditions and extreme states is described, and the steps are as follows: The system controls the suspension to execute multi-gear shifting commands and triggers data acquisition after maintaining a stable state in each gear. Read the output signal of the new height sensor and combine it with the offset value to calculate the height and form the current gear height data; The calculated height is compared with the target height gear by gear, and the deviation data and changing trend of each gear are recorded. Under mechanical limit conditions, the sensor signal is collected again and the limit height is calculated. The difference between the limit height and the reference height is calculated and the verification result data is output.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention achieves fully software-based calibration, completely eliminating reliance on external measuring equipment and dedicated calibration stations. During the factory phase, key data such as altitude sensor offset values, actual physical altitude, air spring pressure, and attitude references are collected and stored, providing a complete data foundation for subsequent online calibration. During daily vehicle operation, a self-learning mechanism gradually establishes the mapping relationship between pressure and altitude, enabling the system to continuously optimize the data model under real-world conditions. After a model change in the altitude sensor, the calibration process is entirely automated by the algorithm within the onboard control unit, eliminating the need for factory returns or external diagnostic equipment. This supports remote software upgrades for calibration after hardware changes, significantly reducing maintenance costs and greatly improving operational efficiency.

[0016] This invention achieves high calibration accuracy by constructing a multi-layered physical benchmark system to constrain and verify the calibration results at each level. First, the attitude angles acquired by the onboard inertial measurement unit are used as a consistency criterion, ensuring that data acquisition and calibration are conducted in a mechanical environment consistent with the factory specifications, thus guaranteeing data validity from the outset. Second, the pressure-altitude mapping relationship accumulated during daily operation enables the model to accurately reflect the dynamic characteristics of the vehicle in its current state. Third, the mechanical limit position, a physical hard point that does not change over time, is used as an absolute reference to finally verify the calibration results, thus forming a complete guarantee chain from relative consistency to absolute accuracy, significantly improving the overall calibration accuracy.

[0017] This invention, while performing calibration, also possesses online diagnostic capabilities for system health. During routine self-learning data acquisition, a multi-layered verification mechanism is introduced to screen data validity. This includes judging the reasonableness of the deviation between the altitude sensor output and the target altitude, and continuously monitoring the trend of air spring pressure changes. This allows for early identification of potential problems such as sensor malfunctions or airbag leaks. When anomalies are detected, timely marking or alarms are issued to prevent erroneous data from entering the model and affecting calibration results. Furthermore, it provides a basis for subsequent maintenance, contributing to improved safety, reliability, and operational stability of the entire vehicle system.

[0018] The method of this invention is simple in structure, low in implementation cost, and has good engineering feasibility. The entire solution relies only on the existing height sensor, pressure sensor, and inertial measurement unit in the vehicle's air suspension system, without requiring additional hardware, thus avoiding increased system complexity. In terms of data processing, a linear mapping table is used to store and query the relationship between pressure and height. Compared with complex algorithm models, this method has advantages such as low computational load, high real-time performance, and simple implementation, and can run stably in resource-constrained embedded control units. Furthermore, this method has low software architecture requirements, is easy to integrate into existing control systems, and has high engineering application value and promising prospects for widespread adoption. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a flowchart of a self-learning calibration method after switching altitude sensors according to the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] This invention provides, for example Figure 1 The self-learning calibration method shown includes the following steps after switching altitude sensors: During the factory calibration of the vehicle's air suspension system, the vehicle is first placed at curb weight on a level surface that meets the production line's accuracy requirements. This condition ensures that the entire calibration process is conducted in a uniform and reproducible physical environment, thereby avoiding the accumulation of measurement errors due to load variations or ground slope. Based on this, the suspension is adjusted to the designed height position, and the actual physical height of the four wheel ends is measured using external high-precision height measuring equipment. This process forms the basic data source for the height sensor calibration.

[0023] This calibration operation obtains the offset values ​​corresponding to each altitude sensor. subscript This offset value is used to distinguish different wheel positions, specifically front left (FL), front right (FR), rear left (RL), and rear right (RR). It compensates for systematic errors between the sensor output and the actual height. Simultaneously, the actual physical height of each wheel is obtained under this calibration state. This physical height represents a standard reference value measured by high-precision equipment and is related to the internal pressure of the air spring. Together they constitute a complete physical description of this state, in which This represents the stable pressure value of each air spring at the design height. These three types of data together constitute important basic parameters for subsequent calibration migration and are stored uniformly in non-volatile memory to ensure long-term traceability and data stability.

[0024] After completing the basic calibration, the vehicle's attitude information under standard conditions is further recorded for subsequent state consistency assessment. Specifically, after the suspension reaches a stable state, the vehicle attitude angle data output by the onboard inertial measurement unit is read, including the roll angle. With pitch angle This is stored as the factory attitude reference. It indicates the angle of rotation of the vehicle about its longitudinal axis, and is used to reflect the left and right tilt of the vehicle body; This represents the vehicle's rotation angle around its lateral axis, used to describe the vehicle's pitch and roll. Since the calibration environment is a level surface, this attitude angle data characterizes the vehicle's ideal attitude under standard load and standard ground conditions, and is subsequently used as a reference to determine whether the vehicle's current state meets the calibration conditions. To accommodate minor disturbances in actual use, a certain tolerance range is allowed in attitude judgment; for example, roll angle deviation should not exceed 0.5° and pitch angle deviation should not exceed 0.5°. When the real-time measurement value falls within this range, the vehicle's current attitude is considered consistent with its factory condition, thus ensuring the effectiveness and reliability of subsequent data acquisition.

[0025] After completing the attitude baseline recording, the data acquisition process for the mechanical limit position begins. This process is used to obtain an absolute physical reference independent of the electrical characteristics of the sensors. By continuously controlling the air spring exhaust valve to keep the airbag completely vented until the suspension structure contacts the mechanical limit block, this position is fixed by the mechanical structure and does not change with time, temperature, or component aging. During the venting process, pressure sensors monitor the pressure inside the airbag to ensure it approaches atmospheric pressure, assisting in determining whether the venting process is complete. After reaching the mechanical limit, the system remains stationary for a predetermined period (e.g., 10 seconds) to eliminate residual stress and dynamic vibration within the system, thereby ensuring stable and reliable measurement data.

[0026] Record the raw output signal of the old altitude sensor under this steady-state condition. ,in This represents the raw output value of the sensor when each wheel is in its mechanically limited position. This output can be a voltage, duty cycle, or angle signal, depending on the sensor type. Based on this raw output value, a mapping function from the old sensor is used... Calculate the corresponding uncompensated height and combine it with the offset value to obtain the true physical height at that location. The calculation relationship is as follows: in, Indicates the first The physical height of each wheel in its mechanically limited position; This represents the mapping function of the old height sensor, used to convert the raw output signal into the corresponding height value; This represents the sensor's raw output under mechanical limit conditions; This represents the calibration offset value for the corresponding wheel. This formula reflects the complete process of height calculation, namely, obtaining the base height through a mapping function, then using the offset value for error compensation, thereby obtaining the final physical height.

[0027] At the same time, the attitude angle data in this state is also recorded. and ,in This indicates the roll angle under mechanically limited conditions. This represents the pitch angle under mechanical limit conditions. These data are used for subsequent verification of the consistency of the mechanical limit conditions. Since the mechanical limit position is entirely determined by the suspension structure and the geometry of the limit block, its corresponding physical height has absolute stability and is unaffected by sensor characteristics, temperature drift, or airbag aging. This can serve as an absolute reference benchmark for verifying the accuracy of results during subsequent calibration processes. All the above data, including... , , and All data are stored in non-volatile memory, providing reliable data support for subsequent calibration after sensor replacement.

[0028] During daily vehicle operation, controlling the height and maintaining the stability of the air suspension system requires continuously acquiring data reflecting the current physical state under real-world conditions to gradually establish the correlation between pressure and height. In this process, setting self-learning trigger conditions plays a fundamental role, aiming to select representative data samples suitable for modeling, thereby avoiding data contamination caused by external interference or abnormal conditions.

[0029] Specifically, when the vehicle is completely stationary, i.e., the speed is... At this time, the influence of dynamic operating conditions on suspension attitude and pressure can be effectively eliminated, and the relationship between the internal pressure of the air spring and the vehicle height tends to be stable. Simultaneously, with no seats occupying space and all four doors and two hoods closed, it means the vehicle is in an unloaded or standard load state, avoiding changes in vehicle attitude caused by uneven distribution of passengers or cargo, thus ensuring the consistency of data collection. Based on this, the vehicle's roll and pitch angles are measured in real time by the onboard inertial measurement unit, and it is determined whether they fall within the tolerance range defined by the factory attitude reference. This tolerance range describes the allowable minute attitude deviations, ensuring a high degree of consistency between the current vehicle attitude and the factory calibration state. When this attitude condition is met, it indicates that the vehicle is in a stable physical environment suitable for data collection.

[0030] In addition, the suspension height needs to be stabilized at a certain known height setting, which includes... , , , , Each of the three gear positions corresponds to a specific target height range. This condition ensures that the collected pressure data has a clear height semantic identifier. Simultaneously, the suspension system should not be in the process of performing height adjustment to avoid introducing transient errors before the pressure changes have stabilized. Data acquisition is only triggered when all the above conditions are met simultaneously, thus ensuring high consistency and reliability of the acquired data.

[0031] Before each data acquisition is performed, a strict health check of the operating status of the suspension-related components is required to prevent erroneous data from entering the subsequent modeling process due to hardware abnormalities or system failures.

[0032] First, sensor validity is verified by comparing the height value calculated by the current height sensor with the design height corresponding to the target gear in the control logic. If the deviation between the two exceeds a preset threshold (e.g., ...), the sensor will be checked. When an anomaly occurs, the current sensor output is determined to be abnormal. In this case, the current data acquisition is discarded, and an abnormal event is recorded. The number of anomalies is also accumulated. When an anomaly occurs consecutively a set number of times (e.g., ...), the anomaly is stopped. When the altitude sensor is flagged as potentially faulty (e.g., once), a flag indicating a possible malfunction is triggered, prompting system or maintenance personnel to conduct further checks. The core of this verification process lies in using the target altitude as a reference to make a reasonable judgment on the sensor output, thereby identifying problems such as sensor drift, damage, or signal abnormalities.

[0033] Next, suspension leak detection is performed. While the vehicle is stationary and the suspension remains in a fixed position, the rate of change of pressure values ​​for each air spring is continuously monitored. When a sustained decrease in pressure within a unit of time is detected, exceeding a preset threshold, it is determined that the air spring has a leak risk. In this case, no data is recorded, and the suspension system is marked as potentially leaking. This verification mechanism filters out abnormal data caused by airtightness issues before the data enters the learning process, thus ensuring the accuracy of the subsequent pressure model. Only when both sensor validity verification and suspension leak detection pass are the currently collected data considered valid and allowed to enter the data recording and modeling stages.

[0034] After completing the trigger condition judgment and health status verification, the filtered data is recorded and organized to form a stable mapping relationship between pressure and altitude. Specifically, pressure data of each air spring at the current altitude setting is collected, and high-frequency noise and transient fluctuations are removed through filtering to obtain a stable pressure value, denoted as . subscript This indicates the different wheel positions, corresponding to four positions: front left, front right, rear left, and rear right. This represents reliable pressure data collected under all constraints and serves as the foundational variable for constructing the mapping relationship. Based on different gear heights, the pressure values ​​for each wheel are recorded and stored in the form of a linear mapping table, forming a gear-pressure correspondence. The specific data structure is as follows: in, Indicates the left front wheel is Stable pressure value in gear, Indicates the right front wheel is Stable pressure value in gear, Indicates the left rear wheel is Stable pressure value in gear, Indicates the right rear wheel is The stable pressure value at the specified gear position; all other parameters are defined according to the same naming convention, using letters... , , , These indicate the positions of the front left, front right, rear left, and rear right wheels, respectively. , , , , These represent different suspension height settings. This mapping exists as a discrete table, with each setting corresponding to a complete set of four-wheel pressure data, thus establishing a one-to-one correspondence between pressure and height. When the target pressure corresponding to a specific height setting needs to be obtained, it can be directly looked up in this mapping table, avoiding complex calculations and improving system real-time performance.

[0035] When data acquisition for a certain gear has not yet been completed, the corresponding pressure data may not exist in the mapping table. In this case, linear interpolation can be used to estimate the approximate pressure value of the target gear by utilizing the pressure data between adjacent known gears. The basic idea of ​​linear interpolation is to assume that the pressure change between adjacent gears is approximately linear, and to estimate the unknown point by using two known data points, thereby maintaining a certain level of accuracy while ensuring computational simplicity. As the fundamental data source in this process, its accuracy directly affects the reliability of the interpolation results. Therefore, the aforementioned triggering conditions and health verification mechanisms play a crucial role in ensuring the quality of this parameter. Through continuous daily data collection and updates, the pressure data corresponding to each altitude level is gradually improved, and the completeness of the mapping table is continuously enhanced, thereby reducing the frequency of interpolation and improving the overall system accuracy. This pressure mapping relationship plays a core bridging role in the subsequent calibration process, establishing a unified physical reference between different sensor characteristics and achieving effective conversion between altitude control and calibration parameters.

[0036] After switching to a different altitude sensor model, the new altitude sensor needs to be calibrated online based on existing historical data and the vehicle's current operating status, without relying on external measuring equipment. This requires meeting a series of preconditions to ensure that subsequent calibration calculations are based on a stable and reliable physical environment.

[0037] The vehicle must have already physically replaced the old height sensor with the new one. At this point, the new height sensor is installed in the corresponding position on the suspension system and is functioning normally. Simultaneously, the electronic control unit can identify the type of sensor installed through configuration words or hardware recognition signals, thus distinguishing between the old and new models. Based on this, the mapping function corresponding to the new height sensor... The mapping function, which describes the functional relationship between the original output signal of the new sensor and the physical height, has been written to the control unit via remote update. It is the core parameter for subsequent height calculation.

[0038] At the same time, the vehicle must be stationary and the speed must be [missing information]. The vehicle must be kept under standard load conditions, ensuring that no occupants occupy the interior space and that all four doors and two hoods are closed. This prevents suspension height deviations caused by load variations. The attitude angles output in real time by the onboard inertial measurement unit must fall within the tolerance range defined by the factory attitude reference. This condition is used to confirm that the vehicle's current attitude is consistent with the attitude at the time of factory calibration, thereby ensuring the accuracy of physical height reproduction.

[0039] In addition, the pressure-height linear mapping relationship requires at least the completion of data learning for the design height settings. When a complete mapping relationship has not yet been established, the pressure values ​​recorded during the factory delivery stage can be used. As an alternative input, where This indicates the standard pressure value corresponding to each air spring under factory calibration conditions. These conditions collectively define the calibration execution environment, ensuring that subsequent calculations can be performed under a uniform and controllable condition.

[0040] After meeting the above conditions, the altitude state reproduction process based on pressure data is first performed. The actual factory-designed altitude value stored in non-volatile memory is read. This parameter represents the target physical height of each wheel under standard conditions and serves as the reference benchmark for the entire calibration process. Subsequently, based on the pressure-height linear mapping relationship established in the second stage, the target pressure value corresponding to this design height is obtained through table lookup or interpolation. ,in This indicates the air spring pressure required to achieve the target height under current vehicle conditions.

[0041] By individually inflating or deflating each air spring, the internal pressure of the airbag is gradually brought closer to and stabilized at a certain level. This allows for a high degree of physical replication of the suspension system. When the mapping relationship has not yet been established, the pressure values ​​recorded at the factory are used directly. While this control method lacks self-learning correction, it still provides a basic level of reproducibility. After pressure adjustment is completed and stabilized, the vehicle should enter a static equilibrium state, at which point the mechanical relationships within the suspension structure tend to stabilize, providing a reliable basis for subsequent calculations.

[0042] After completing the pressure closed-loop adjustment, it is necessary to verify the vehicle's current attitude consistency. The current roll and pitch angles output by the inertial measurement unit are read and compared with the attitude reference recorded at the factory. When the real-time attitude angles fall within the preset tolerance range, it can be determined that the vehicle's current load state and ground conditions are consistent with the factory calibration. At this point, it can be considered that the suspension system has successfully reproduced the target physical height. The core of this judgment process lies in using attitude angles as an indirect observation, and verifying the accuracy of height reproduction by judging whether the vehicle body remains level.

[0043] When the attitude angle exceeds the allowable range, it indicates that the vehicle has additional loads or uneven ground. In this case, environmental adjustments are required, such as unloading additional cargo or moving the vehicle to a more level ground condition. After the attitude constraints are met, the above process is repeated to ensure that the calibration calculation is based on the correct physical premises.

[0044] After confirming successful physical altitude reproduction, the calculation of the new altitude sensor bias values ​​begins. The raw output signals of the four new altitude sensors are read under the current stable condition. ,in This represents the unprocessed output value of the new sensor, which can be in the form of a voltage value, duty cycle, or angle value, depending on the sensor's implementation. Based on this raw output signal, the corresponding uncompensated height value is calculated using the mapping function of the new altitude sensor, as follows: in, This represents the original height value without offset compensation. The mapping function representing the new altitude sensor, This represents the raw output signal of the new sensor. The calculated result reflects the height directly derived from the raw signal under the current sensor characteristics; however, due to the lack of bias correction, its value deviates from the true physical height. Based on this, the true physical height... The difference between the new sensor's bias value and the uncompensated height value is used to calculate the new sensor's bias value, and the relationship is as follows: in, This represents the bias value of the new altitude sensor, used to compensate for systematic errors between the mapping function and the actual physical altitude; This indicates the actual physical height recorded at the factory. This represents the uncompensated height value calculated from the original output signal of the new sensor. This calculation process establishes a consistency between the sensor output and the actual physical height, thus completing the calibration parameter solution for the new sensor. Subsequently, the calculated... The data is written into non-volatile memory for subsequent height calculations, thereby enabling the effective replacement of the new sensor in the control system.

[0045] After writing the offset value, the reliability of the calibration results needs to be further verified through multi-level calibration. By controlling the suspension system to switch to multiple different height levels sequentially, after each level stabilizes, the current physical height is calculated using the new sensor output signal and the calculated offset value, and compared with the target design height corresponding to that level. When the deviation of each level is within the allowable range, the calibration results can be considered to have consistency and stability; if there is an excessive deviation, it indicates an anomaly in the calibration process, and the relevant data or execution conditions need to be rechecked.

[0046] Based on this, mechanical limit position verification is also required. This is done by fully venting the air springs, allowing the suspension structure to drop to the mechanical limit position, and then reading the original output signal of the new sensor in this state. The current physical height is calculated using a mapping function and offset value, and compared with the mechanical limit height recorded at the factory. When the deviation is within the allowable range, it indicates that the calibration result is also valid under absolute physical reference, thus completing the entire online calibration process. Through the above series of calculation and verification steps, accurate calibration of the new height sensor is achieved without the need for external equipment, ensuring the consistency and reliability of the calibration results under various physical constraints.

[0047] After calculating and storing the new altitude sensor bias value, further verification is required at multiple altitude settings to ensure consistency and reliability of the calibration results under different operating conditions. This process is based on the multi-level pressure mapping relationship formed during the daily self-learning phase, and checks the lateral consistency of the calibration results by reproducing different altitude states one by one.

[0048] During execution, the control logic, following a predetermined sequence, gradually adjusts the suspension system from the current design height to other self-learned height settings besides the design height, such as any combination of HL2, HL1, NRH, LL1, and LL2. Each shift requires waiting for the system to reach a stable state before data acquisition and calculation. The determination of a stable state is typically based on the air spring pressure changing smoothly and the vehicle body posture no longer changing significantly, thus ensuring that the collected data accurately reflects the current physical height.

[0049] At each altitude setting reached, the raw output signal from the new altitude sensor is read and combined with the already calculated bias value. The current actual height is calculated and denoted as . ,in This represents the actual physical height calculated by the new sensor in the current gear. This value already includes the results of the mapping function transformation and offset compensation, directly reflecting the true height state of the suspension system in this gear. Subsequently, this calculation result is compared with the target design height corresponding to this gear. The target design height is a standard height value predefined during the system's design phase, corresponding one-to-one with different gears. When the difference between the two is within a preset allowable range, it indicates that the calibration results in this gear have good accuracy and consistency; if the deviation exceeds this range, it indicates a calibration error in the current gear, requiring further analysis of the cause.

[0050] By performing the above verification on each of the self-learned height settings, the stability of the calibration results can be confirmed from multiple dimensions. When all settings meet the accuracy requirements, it indicates that the calibration results are reliable throughout the entire working range, thus completing the consistency confirmation of multiple settings. If any setting deviates beyond the limit, a calibration anomaly should be indicated, and manual inspection or re-execution of the relevant procedures should be initiated. This multi-setting verification process is not executed if the pressure mapping relationship has not yet been established, thereby avoiding misjudgments due to insufficient data.

[0051] After completing the multi-gear consistency verification, mechanical limit position verification is required to further confirm the correctness of the calibration results from the perspective of absolute physical reference. This process is based on the mechanical limiting characteristics of the air suspension structure. That is, when the gas inside the air spring is completely emptied, the suspension structure will naturally fall to the position of the mechanical limit block. This position is determined by the structural design and geometry, and has high stability and immutability. It is not affected by factors such as sensor characteristics, temperature changes or material aging, and therefore can be used as an absolute height reference point.

[0052] During this calibration, the air spring exhaust valve is kept continuously open, allowing gas to gradually escape from the airbag until the pressure approaches atmospheric pressure, thus confirming the completion of the exhaust process. After exhaustion, the suspension system enters a mechanical limit state and remains stationary to eliminate residual dynamic effects. The raw output signal from the new height sensor in this state is then read and recorded as follows. ,in This represents the initial output value of the new sensor when it is in the mechanical limit position. This value is related to the specific sensor type and can be expressed as voltage, duty cycle, or angle signal.

[0053] Based on the original output signal, the height is calculated using a new sensor mapping function and compensated for by the bias value, thus obtaining the lower limit physical height of the current measurement. The calculation relationship is as follows: in: This represents the physical height calculated by the new sensor system in the mechanically limited position; The mapping function for the new altitude sensor is used to convert the raw output signal into an altitude value; This represents the original output signal of the new sensor under mechanical limit conditions. This represents the bias value of the new sensor, which is used to correct for systematic errors between the mapping function and the true height.

[0054] This formula fully describes the calculation process from the raw signal to the final physical height under the new sensor system, and is an important basis for verifying the entire calibration result. (In obtaining...) Then, compare it with the mechanical limit reference height recorded during the factory manufacturing stage. Comparison, among which This represents the true physical height of the mechanical position obtained through external equipment calibration under the old sensor system. This value remains unchanged during system operation and is the absolute reference value in the entire calibration system.

[0055] By calculating the difference between the two, we can obtain the magnitude of the error of the current calibration result under the absolute reference, which is expressed as follows: This expression represents the absolute deviation between the measurement result of the new sensor system and the reference height. The symbol || indicates absolute value operation, ignoring the error direction and focusing only on the magnitude of the error. If the deviation falls within a preset allowable range (e.g., no more than 5mm), it indicates that the calibration result has high accuracy at the absolute physical reference point of the mechanical limit, thus further confirming the correctness of the overall calibration result. If the deviation exceeds this range, it indicates a large measurement error at the extreme position, possibly caused by insufficient accuracy of the mapping function, offset calculation error, or aging of mechanical components. In this case, it is necessary to determine that there is an abnormality in the calibration process and prompt manual inspection. It should be noted that near the extreme position, due to factors such as the nonlinear characteristics of the sensor or structural gaps, the accuracy of the mapping function may decrease to some extent. Therefore, a relatively loose but still acceptable error range is allowed to balance actual engineering conditions and accuracy requirements.

[0056] By combining multi-position consistency verification with absolute mechanical limit position verification, the reliability of calibration results can be comprehensively evaluated from both relative and absolute accuracy perspectives. Multi-position verification focuses on relative consistency across different working ranges, ensuring good accuracy across all commonly used height ranges. Mechanical limit position verification, on the other hand, provides an absolute reference independent of any sensor model, ultimately confirming whether the calibration results conform to physical reality. These two methods complement each other, giving the entire calibration process both dynamic adaptability and static absolute verification capability, thus forming a complete verification closed loop.

[0057] If all the above verifications pass, it can be determined that the calibration process has been completed and the new height sensor can accurately reflect the physical height of the suspension under various working conditions. If any abnormality occurs in any link, it is necessary to deal with it according to the specific problem, such as re-collecting data, adjusting attitude conditions, or checking the hardware status, to ensure that the final result meets the accuracy and reliability requirements.

[0058] This invention achieves fully software-based calibration, completely eliminating reliance on external measuring equipment and dedicated calibration stations. During the factory phase, key data such as altitude sensor offset values, actual physical altitude, air spring pressure, and attitude references are collected and stored, providing a complete data foundation for subsequent online calibration. During daily vehicle operation, a self-learning mechanism gradually establishes the mapping relationship between pressure and altitude, enabling the system to continuously optimize the data model under real-world conditions. After a model change in the altitude sensor, the calibration process is entirely automated by the algorithm within the onboard control unit, eliminating the need for factory returns or external diagnostic equipment. This supports remote software upgrades for calibration after hardware changes, significantly reducing maintenance costs and greatly improving operational efficiency.

[0059] This invention achieves high calibration accuracy by constructing a multi-layered physical benchmark system to constrain and verify the calibration results at each level. First, the attitude angles acquired by the onboard inertial measurement unit are used as a consistency criterion, ensuring that data acquisition and calibration are conducted in a mechanical environment consistent with the factory specifications, thus guaranteeing data validity from the outset. Second, the pressure-altitude mapping relationship accumulated during daily operation enables the model to accurately reflect the dynamic characteristics of the vehicle in its current state. Third, the mechanical limit position, a physical hard point that does not change over time, is used as an absolute reference to finally verify the calibration results, thus forming a complete guarantee chain from relative consistency to absolute accuracy, significantly improving the overall calibration accuracy.

[0060] This invention, while performing calibration, also possesses online diagnostic capabilities for system health. During routine self-learning data acquisition, a multi-layered verification mechanism is introduced to screen data validity. This includes judging the reasonableness of the deviation between the altitude sensor output and the target altitude, and continuously monitoring the trend of air spring pressure changes. This allows for early identification of potential problems such as sensor malfunctions or airbag leaks. When anomalies are detected, timely marking or alarms are issued to prevent erroneous data from entering the model and affecting calibration results. Furthermore, it provides a basis for subsequent maintenance, contributing to improved safety, reliability, and operational stability of the entire vehicle system.

[0061] The method of this invention is simple in structure, low in implementation cost, and has good engineering feasibility. The entire solution relies only on the existing height sensor, pressure sensor, and inertial measurement unit in the vehicle's air suspension system, without requiring additional hardware, thus avoiding increased system complexity. In terms of data processing, a linear mapping table is used to store and query the relationship between pressure and height. Compared with complex algorithm models, this method has advantages such as low computational load, high real-time performance, and simple implementation, and can run stably in resource-constrained embedded control units. Furthermore, this method has low software architecture requirements, is easy to integrate into existing control systems, and has high engineering application value and promising prospects for widespread adoption.

[0062] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A self-learning calibration method after switching altitude sensors, characterized in that, Includes the following steps: During the vehicle manufacturing process, the height sensor is calibrated using an external height measurement device. This process acquires the old height sensor offset value, actual physical height, and air spring pressure data for each wheel. It also records the vehicle body attitude angle data when the vehicle is in a standard level state. Simultaneously, after the air springs are fully vented to the mechanical limit position, the original output signal of the old height sensor at that position is collected, and the corresponding physical height is calculated. The offset value, actual physical height, air spring pressure data, vehicle body attitude angle data, and physical height at the mechanical limit position are stored together. During the daily operation of the vehicle, when the vehicle is stationary, the body posture angle data falls within the factory record range, and the suspension is in a stable height gear, the air spring pressure data of each gear is collected. When the deviation between the sensor output and the target height and the change in air spring pressure meet the preset conditions, the air spring pressure data corresponding to each height gear is recorded as pressure-height correspondence data, forming a multi-gear pressure mapping relationship. After completing the height sensor model switch and obtaining the new height sensor mapping relationship, the actual physical height recorded at the factory is read. The corresponding target air spring pressure is obtained through the pressure mapping relationship and the air spring is adjusted to reach the pressure state. When the vehicle body attitude angle data meets the factory recorded range, the original output signal of the new height sensor is collected and the uncompensated height value is calculated. The new offset value is calculated based on the actual physical height and the uncompensated height value. Then, the height deviation is checked at multiple height positions. After the air spring is fully vented to the mechanical limit position, the current physical height is calculated and compared with the physical height of the mechanical limit position recorded at the factory.

2. The self-learning calibration method after switching altitude sensors according to claim 1, characterized in that, The process of recording vehicle attitude parameters, highlighting the logic of acquiring and determining vehicle attitude reference data, includes the following steps: The inertial measurement unit acquires multiple consecutive frames of roll and pitch angle data, performs time-series buffering on each frame of data, and removes abrupt values ​​and abnormal jump signals that occur during the sampling process. The cached data is averaged using a sliding window method, and the results from different time windows are weighted and fused to generate stable attitude angle reference values ​​and store them. Construct upper and lower limit ranges for attitude angles, set independent threshold ranges for roll and pitch angles respectively, and establish a bivariate joint judgment rule; The real-time attitude sampling data is input into the judgment logic, and the deviations of the roll angle and pitch angle are compared item by item, and the attitude matching status indicator is output.

3. The self-learning calibration method after switching altitude sensors according to claim 1, characterized in that, Based on the characteristics of the mechanical structure, the process of acquiring extreme position data and establishing a height reference is described, and the steps are as follows: Execute the exhaust control command to keep the air spring exhaust valve open and collect airbag pressure data in real time until the detected value reaches the preset atmospheric pressure judgment threshold. After the air pressure stabilizes, the suspension is kept in a mechanically limited state, and structural vibration data is continuously collected within a set time to confirm that the system is in a static state. The height sensor output signal is read multiple times, and the reading results are subjected to consistency verification and discrete value filtering. The processed raw signal is input into the mapping function for height calculation, and the offset correction value is superimposed to generate the extreme position height data and complete the storage.

4. The self-learning calibration method after switching altitude sensors according to claim 1, characterized in that, For the data collection condition filtering process under normal operation, the consistency judgment logic of the trigger scenario is emphasized, and the steps are as follows: Collect vehicle operating status signals, including vehicle speed signals, wheel speed signals, and position change information, and confirm that the vehicle is completely stationary through multi-signal cross-verification; Read the door opening / closing status signal and the seat occupancy detection signal, and perform consistency verification on the status of multiple sensors; Acquire real-time attitude angle data and perform filtering processing, then compare the processing results with the attitude reference interval dimension by dimension; Extract the status of the suspension control signal, determine whether there is a height adjustment execution command, and confirm that the height setting is in a stable range.

5. The self-learning calibration method after switching altitude sensors according to claim 1, characterized in that, Starting from the operational status, the process of data validity judgment and anomaly identification and handling is described in the following steps: The current height value is calculated by the sensor output signal, and the standard height parameter corresponding to the target gear is read simultaneously to construct real-time height difference data; The height difference data is continuously sampled, the trend of deviation change is calculated, and the frequency and duration of exceeding the limit are recorded. The air spring pressure signal is sampled at high frequency, and the pressure change rate and change curve characteristics within the time window are calculated. The pressure change rate is matched with a preset change model for analysis, and anomaly identification signals and corresponding fault status records are output.

6. The self-learning calibration method after switching altitude sensors according to claim 1, characterized in that, Based on data from multiple operating conditions, the following steps illustrate how to organize and record data related to the relationship between pressure and altitude: When the acquisition conditions are met, the air spring pressure signals of each wheel are acquired, and multi-cycle continuous sampling is performed to form pressure sequence data; The pressure sequence is filtered, outliers are removed, and stability analysis is performed to extract representative pressure values ​​within the stable interval. The pressure values ​​are categorized according to the height gear label, and the data for different wheel positions are stored independently. The pressure data for multiple gears is structured and organized to form a mapping table structure that includes gear information and pressure data for multiple gears.

7. The self-learning calibration method after switching altitude sensors according to claim 6, characterized in that, During the generation of the multi-level pressure mapping table, a pressure change consistency judgment and data update mechanism is introduced for the pressure data corresponding to each height level. The pressure sequence formed by continuous sampling is compared with the interval stability. The data writing operation is only performed when the pressure change trend meets the preset stability judgment condition. The historical pressure records under the same level are replaced or updated, thereby forming a pressure and height mapping relationship data structure that is dynamically corrected according to the working conditions.

8. The self-learning calibration method after switching altitude sensors according to claim 1, characterized in that, For the pressure acquisition stage, the numerical processing method for cases where data is missing at different gear levels is described, and the steps are as follows: Access the mapping table structure and read the pressure data item corresponding to the target gear, while checking the data integrity flag; When no pressure data is recorded for the target gear, retrieve the data range of the adjacent recorded gears and determine the upper and lower boundary gears; The pressure data of adjacent gears are calculated to find the difference, and a linear interpolation function is constructed by combining the gear spacing parameter. Substitute the target gear position into the interpolation function to calculate the corresponding pressure value and output it for control input.

9. The self-learning calibration method after switching altitude sensors according to claim 1, characterized in that, Based on the verification process after calibration, the method for verifying results under multiple operating conditions and extreme states is explained, and the steps are as follows: The system controls the suspension to execute multi-gear shifting commands and triggers data acquisition after maintaining a stable state in each gear. Read the output signal of the new height sensor and combine it with the offset value to calculate the height and form the current gear height data; The calculated height is compared with the target height gear by gear, and the deviation data and changing trend of each gear are recorded. Under mechanical limit conditions, the sensor signal is collected again and the limit height is calculated. The difference between the limit height and the reference height is calculated and the verification result data is output.

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