Vehicle mass estimation method, device, vehicle, storage medium and product

Abstract

The application discloses a vehicle mass estimation method and device, a vehicle, a storage medium and a product, and relates to the technical field of vehicle mass estimation. The method comprises the following steps: in response to the vehicle switching from a driving state to a stationary state, acquiring each first tire pressure value of the vehicle; in response to a preset vehicle control signal, acquiring each second tire pressure value of the vehicle, wherein the vehicle control signal represents the intention of the vehicle to switch from the stationary state to the driving state; based on the first tire pressure value and the second tire pressure value, determining whether the vehicle has a mass mutation during the current parking; if the vehicle has a mass mutation during the current parking, amplifying the covariance matrix of the Kalman filter running on the vehicle, and estimating the mass of the vehicle based on the amplified covariance matrix to obtain a mass estimation value. The application can improve the mass estimation accuracy based on the Kalman filter under the working condition that the mass of the vehicle is mutated.

Description

Technical Field

[0001] This application relates to the field of vehicle quality estimation technology, and in particular to a vehicle quality estimation method, device, vehicle, storage medium, and product. Background Technology

[0002] With the continuous development of vehicle active safety control technology, the real-time and accurate estimation of vehicle mass, as a core parameter in dynamic control, has become an important foundation for the research of commercial vehicle electronic control systems.

[0003] Currently, the commonly used method for estimating vehicle mass is an online estimation algorithm based on Kalman filtering. Specifically, this involves establishing a longitudinal dynamics model of the vehicle, treating mass as the state variable to be estimated, and using sensor signals such as driving force and acceleration for recursive optimal estimation. However, after the Kalman filter has been running for a long time during stable vehicle operation, its error covariance matrix will continuously shrink with each filtering iteration, eventually approaching a minimum value. Correspondingly, since the error covariance matrix and Kalman gain are positively correlated in the Kalman gain calculation formula, when the error covariance matrix approaches 0, the Kalman gain also approaches 0. This means that the filter no longer relies on new sensor data when estimating vehicle mass. If a sudden change in mass occurs due to vehicle loading or unloading, the filter needs to re-track the new true mass value, during which driving safety cannot be guaranteed.

[0004] Therefore, improving the accuracy of Kalman filter-based quality estimation under conditions of sudden changes in vehicle quality is a problem that urgently needs to be solved. Summary of the Invention

[0005] The main objective of this application is to provide a vehicle quality estimation method, device, vehicle, storage medium, and product, which aims to improve the accuracy of Kalman filter-based quality estimation under conditions of sudden changes in vehicle quality.

[0006] To achieve the above objectives, this application provides a vehicle mass estimation method, the vehicle mass estimation method comprising: In response to the vehicle switching from a driving state to a stationary state, the first tire pressure values ​​of the vehicle are obtained; In response to a preset vehicle control signal, the second tire pressure values ​​of the vehicle are acquired, wherein the vehicle control signal indicates that the vehicle intends to switch from a stationary state to a driving state. Based on each of the first tire pressure values ​​and each of the second tire pressure values, determine whether the vehicle experiences a sudden change in quality during the current parking period; If the vehicle experiences a sudden change in quality during the current parking period, the covariance matrix of the Kalman filter running on the vehicle is amplified, and the overall vehicle quality is estimated based on the amplified covariance matrix to obtain a quality estimate.

[0007] In one embodiment, the step of determining whether the vehicle experiences a quality mutation during the current parking period based on each of the first tire pressure values ​​and each of the second tire pressure values ​​includes: Based on each of the first tire pressure values ​​and each of the second tire pressure values, determine the tire pressure change of each wheel of the vehicle during the current parking period; Based on the tire pressure changes, determine whether the vehicle experienced a sudden quality change during the current parking period.

[0008] In one embodiment, the step of determining the tire pressure change of each wheel of the vehicle during the current parking period based on each of the first tire pressure values ​​and each of the second tire pressure values ​​includes: Determine the current parking duration of the vehicle, and determine whether the current parking duration is greater than a preset duration; If the current parking duration is longer than the preset duration, then for each wheel of the vehicle, the first tire pressure value of the wheel is converted into a third tire pressure value at a preset reference temperature, and the second tire pressure value of the wheel is converted into a fourth tire pressure value at the preset reference temperature. The difference between the fourth tire pressure value and the third tire pressure value is taken as the tire pressure change of the wheel during the current parking period; If the current parking duration is less than or equal to the preset duration, then for each wheel of the vehicle, the difference between the second tire pressure value and the first tire pressure value of the wheel is taken as the tire pressure change of the wheel during the current parking period.

[0009] In one embodiment, the step of determining whether the vehicle experiences a sudden quality change during the current parking period based on the tire pressure changes includes: Based on the tire pressure change amounts, determine the direction of tire pressure change for each wheel; If the number of target wheels with the same direction of tire pressure change is greater than the preset number, then the average tire pressure change of each target wheel is determined based on the tire pressure change of each target wheel. If the absolute value of the average tire pressure change is greater than a first preset change, it is determined that the vehicle has a sudden quality change during the current parking period. If the number of target wheels with the same direction of tire pressure change is less than or equal to a preset number, or if the average tire pressure change is less than or equal to a preset change, then it is determined that the vehicle does not have a sudden change in quality during the current parking period.

[0010] In one embodiment, the step of amplifying the covariance matrix of the Kalman filter running on the vehicle includes: The intensity of the mass mutation of the vehicle is determined based on the absolute value of the average tire pressure change. Based on the intensity of the quality mutation, the covariance matrix of the Kalman filter running on the vehicle is amplified.

[0011] In one embodiment, the step of determining the intensity of the mass mutation of the vehicle based on the absolute value of the average tire pressure change includes: If the absolute value of the average tire pressure change is greater than the first preset change and less than or equal to the second preset change, the mass change intensity of the vehicle is determined to be the first intensity, wherein the second preset change is greater than the first preset change. If the absolute value of the average tire pressure change is greater than the second preset change amount, the intensity of the mass change of the vehicle is determined to be the second intensity.

[0012] In one embodiment, the step of amplifying the covariance matrix of the Kalman filter running on the vehicle based on the mass mutation intensity includes: When the intensity of the quality mutation is the first intensity, a first difference between the absolute value of the average tire pressure change and the first preset change is determined, and a second difference between the second preset change and the first preset change is determined. The amplification factor is determined based on the ratio of the first difference to the second difference, and the covariance matrix is ​​amplified based on the amplification factor to obtain the amplified covariance matrix. When the intensity of the quality mutation is the second intensity, the preset initial covariance matrix is ​​used as the amplified covariance matrix.

[0013] In one embodiment, the step of estimating the vehicle's overall mass based on the amplified covariance matrix to obtain a mass estimate includes: The vehicle's overall mass is estimated using the Kalman filter based on the amplified covariance matrix to obtain a first estimated mass; Obtain the second estimated mass output by the Kalman filter when it last estimated the total mass of the vehicle; The first estimated mass and the second estimated mass are weighted and summed to obtain the smoothed vehicle mass. The rationality of the smoothed vehicle mass is verified to obtain the estimated mass value.

[0014] Furthermore, to achieve the above objectives, this application also provides a vehicle mass estimation device, the vehicle mass estimation device comprising: The first acquisition module is used to acquire the first tire pressure values ​​of the vehicle in response to the vehicle switching from a driving state to a stationary state. The second acquisition module is used to acquire the second tire pressure values ​​of the vehicle in response to a preset vehicle control signal, wherein the vehicle control signal indicates that the vehicle has the intention to switch from a stationary state to a driving state. The judgment module is used to determine whether there is a sudden change in the quality of the vehicle during the current parking period based on each of the first tire pressure values ​​and each of the second tire pressure values; The amplification module is used to amplify the covariance matrix of the Kalman filter running on the vehicle if there is a sudden change in the quality of the vehicle during the current parking period, and estimate the overall vehicle quality based on the amplified covariance matrix to obtain a quality estimate.

[0015] In addition, to achieve the above objectives, this application also proposes an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle mass estimation method as described above.

[0016] In addition, to achieve the above objectives, this application also proposes a vehicle that includes the electronic equipment described above.

[0017] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, on which a program implementing a vehicle weight estimation method is stored, and the program implementing the vehicle weight estimation method is executed by a processor to implement the steps of the vehicle weight estimation method as described above.

[0018] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle mass estimation method as described above.

[0019] This application provides a vehicle mass estimation method. The method acquires a first tire pressure value when the vehicle switches from a driving state to a stationary state, and acquires a second tire pressure value when a preset vehicle control signal (which indicates that the vehicle intends to switch from a stationary state to a driving state) is triggered. The method determines whether a mass change caused by loading or unloading occurred during the parking period based on the comparison of the two tire pressure values. The method then amplifies the covariance matrix of the Kalman filter only when a mass change is determined to exist.

[0020] In summary, the tire pressure detection in this application is completed while the vehicle is stationary. At this time, there is no load transfer caused by acceleration, braking, or steering. Tire pressure changes directly reflect load changes, and the detection timing precisely covers the physical time period during loading or unloading operations, allowing mass changes to be captured before the vehicle restarts. After a mass change is confirmed, the Kalman filter's covariance matrix is ​​amplified, synchronously increasing the Kalman gain. This allows the filter to regain its responsiveness to new sensor data, thus quickly converging the mass estimate to the new true value during subsequent driving, shortening the transition time for the filter to re-track after a mass change. Therefore, this application solves the data saturation problem caused by the covariance matrix shrinking to a minimum and the Kalman gain approaching zero after long-term operation of the Kalman filter in traditional solutions, improving the accuracy of mass estimation and driving safety under load change conditions. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the first embodiment of the vehicle mass estimation method of this application; Figure 2 This is a schematic diagram of the mass mutation detection process involved in an embodiment of the vehicle mass estimation method of this application; Figure 3 This is a schematic diagram of the system architecture involved in an embodiment of the vehicle mass estimation method of this application; Figure 4 This is a schematic diagram of the Kalman filter estimation process involved in an embodiment of the vehicle mass estimation method of this application; Figure 5 This is a schematic diagram of the module structure of the vehicle mass estimation device of this application; Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the vehicle mass estimation method in this application embodiment.

[0024] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0026] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0027] Currently, the commonly used method for estimating vehicle mass is an online estimation algorithm based on Kalman filtering. Specifically, this involves establishing a longitudinal dynamics model of the vehicle, treating mass as the state variable to be estimated, and using sensor signals such as driving force and acceleration for recursive optimal estimation. However, after the Kalman filter has been running for a long time during stable vehicle operation, its error covariance matrix will continuously shrink with each filtering iteration, eventually approaching a minimum value. Correspondingly, since the error covariance matrix and Kalman gain are positively correlated in the Kalman gain calculation formula, when the error covariance matrix approaches 0, the Kalman gain also approaches 0. This means that the filter no longer relies on new sensor data when estimating vehicle mass. If a sudden change in mass occurs due to vehicle loading or unloading, the filter needs to re-track the new true mass value, during which driving safety cannot be guaranteed.

[0028] Therefore, improving the accuracy of Kalman filter-based quality estimation under conditions of sudden changes in vehicle quality is a problem that urgently needs to be solved.

[0029] The main solution of this application is as follows: in response to the vehicle switching from a driving state to a stationary state, the first tire pressure values ​​of the vehicle are obtained; in response to a preset vehicle control signal, the second tire pressure values ​​of the vehicle are obtained, wherein the vehicle control signal indicates that the vehicle has the intention to switch from a stationary state to a driving state; based on the first tire pressure values ​​and the second tire pressure values, it is determined whether there is a quality change in the vehicle during the current parking period; if there is a quality change in the vehicle during the current parking period, the covariance matrix of the Kalman filter running on the vehicle is amplified, and the overall vehicle mass is estimated based on the amplified covariance matrix to obtain a quality estimate.

[0030] The tire pressure detection in this application is completed while the vehicle is stationary. At this time, there is no load transfer caused by acceleration, braking, or steering. Tire pressure changes directly reflect load changes, and the detection timing precisely covers the physical time period during loading or unloading operations, allowing mass changes to be captured before the vehicle restarts. After a mass change is confirmed, the Kalman filter's covariance matrix is ​​amplified, synchronously increasing the Kalman gain. This allows the filter to regain its responsiveness to new sensor data, thus quickly converging the mass estimate to the new true value during subsequent driving, shortening the transition time for the filter to re-track after a mass change. In this way, this application solves the data saturation problem caused by the covariance matrix shrinking to a minimum and the Kalman gain approaching zero after long-term operation of the Kalman filter in traditional solutions, improving the accuracy of mass estimation and driving safety under load change conditions.

[0031] It should be noted that the execution subject of the vehicle quality estimation method in various embodiments of this application can be a vehicle control system, a computing service device with data processing, network communication, and program execution functions, or an electronic device capable of implementing the above functions, etc. This embodiment does not specifically limit it in this way. The following uses a vehicle control system as the execution subject as an example to describe this embodiment and the following embodiments.

[0032] Based on this, this application proposes a vehicle mass estimation method according to the first embodiment, please refer to... Figure 1 The vehicle mass estimation method includes steps S10 to S40: Step S101: In response to the vehicle switching from a driving state to a stationary state, the first tire pressure values ​​of the vehicle are obtained.

[0033] Driving state refers to the state in which the vehicle is in motion, determined by the vehicle speed signal; when the vehicle speed is greater than zero, it is in a driving state. Stationary state refers to the state in which the vehicle is stationary.

[0034] When the vehicle is detected to switch from a driving state to a stationary state, the tire pressure acquisition action is triggered; the tire pressure value at the current moment is collected by the tire pressure monitoring sensors installed on each wheel of the vehicle (hereinafter referred to as the first tire pressure value for distinction).

[0035] The vehicle control system includes a parking status detection module to determine whether the vehicle is stationary. This can be determined by a combination of factors, including vehicle speed signals (wheel speed sensors or CAN bus), ignition switch status, and parking brake signals. In one feasible implementation, the vehicle is determined to be stationary when any of the following conditions are met: the vehicle speed is zero for a duration exceeding a preset threshold (e.g., 5 seconds), the ignition switch is in the OFF position, or the parking brake is activated and the vehicle speed is zero.

[0036] The vehicle control system includes a tire pressure monitoring module, which comprises TPMS (Tire Pressure Monitoring System) sensors installed in each tire to collect tire pressure values ​​while the vehicle is stationary, with a sampling frequency of at least 1Hz. It can maintain low-power operation after the vehicle ignition switch is turned off, or it can be read immediately upon being activated by the vehicle start signal.

[0037] For example, when the vehicle is detected to be stationary, the tire pressure monitoring module reads the tire pressure values ​​of each tire as a reference value (i.e., the first tire pressure value), which can be expressed as: Where i = 1, 2, ..., n are tire numbers, and n is the total number of tires on the vehicle. This refers to the moment when the vehicle comes to a complete stop. Let be the tire pressure value of the i-th tire detected when the vehicle is stationary. Let be the first tire pressure value of the i-th tire when the vehicle is stationary.

[0038] Step S20: In response to a preset vehicle control signal, acquire each of the second tire pressure values ​​of the vehicle, wherein the vehicle control signal indicates that the vehicle intends to switch from a stationary state to a driving state.

[0039] The preset vehicle control signals represent the driver's intention to move the vehicle from a stationary state to a driving state. For example, when the driver performs operations such as turning on the ignition switch, unlocking the door, powering on the vehicle, releasing the handbrake, and shifting gears, these operations all indicate that the driver is preparing to start the vehicle. The electrical signals or bus messages corresponding to these operations constitute the vehicle control signals that represent the intention to start.

[0040] The vehicle control system pre-stores a list of preset signals, which records the identification information of a series of selected vehicle control signals. When the vehicle controller receives a vehicle control signal, it first compares the signal with the signal identifier in the preset signal list. If a match is found, the signal is identified as a preset vehicle control signal, and the tire pressure monitoring module is triggered to read the current tire pressure value as a secondary tire pressure value. Simultaneously, the vehicle control signal is also sent to the corresponding actuator to perform its original vehicle control function. Through this comparison and triggering mechanism, only specific control signals are responded to, avoiding redundant operations or false triggers caused by triggering acquisition for any control signal, while also enabling the reuse of existing vehicle control signals.

[0041] For example, when the system detects that the vehicle is about to restart (ignition switch on, door unlocked, vehicle powered on, handbrake released, gear engaged), the tire pressure monitoring module immediately reads the current tire pressure value of each tire, which can be specifically represented as: Where i = 1, 2, ..., n are tire numbers, and n is the total number of tires on the vehicle. The moment to prepare the vehicle for start-up. Let be the tire pressure value of the i-th detected tire at the moment the vehicle is about to start. Let be the second tire pressure value of the i-th tire at the moment the vehicle is about to start.

[0042] Step S30: Based on each of the first tire pressure values ​​and each of the second tire pressure values, determine whether the vehicle experiences a sudden change in quality during the current parking period.

[0043] The current parking period refers to the entire parking time from when the vehicle comes to a standstill (i.e., the moment the first tire pressure value is collected) to when the vehicle controller receives the preset vehicle control signal (i.e., the moment the second tire pressure value is collected). Mass mutation refers to a significant change in the vehicle's overall mass caused by loading or unloading during the current parking period. This change is reflected in tire pressure changes through altered tire loads. Since both the first and second tire pressure values ​​are collected when the vehicle is stationary, the tire pressure changes during the two collection periods are not affected by load transfer caused by driving conditions (acceleration, braking, steering). Therefore, tire pressure changes can be directly attributed to changes in vehicle load.

[0044] The core logic for determining whether a quality mutation exists is based on the first and second tire pressure values: loading will increase the load on each tire and the tire pressure will rise synchronously; unloading will reduce the load on each tire and the tire pressure will drop synchronously. By judging whether the tire pressure of each tire changes in the same direction and with sufficient magnitude, it can be confirmed whether a valid loading or unloading event has occurred.

[0045] The first tire pressure value of each wheel recorded when parking is compared wheel by wheel with the second tire pressure value of the corresponding wheel collected when the preset vehicle control signal is received. The tire pressure change of each wheel during parking is calculated. Based on whether the tire pressure change direction of each wheel is consistent and whether the change amplitude exceeds the preset threshold, a comprehensive judgment is made as to whether a valid loading or unloading event occurred during the parking period, thereby determining whether there is a quality mutation.

[0046] In this embodiment, step S30 may include: Step S301: Based on each of the first tire pressure values ​​and each of the second tire pressure values, determine the tire pressure change of each wheel of the vehicle during the current parking period.

[0047] Tire pressure change is used to quantify the degree of pressure change of each wheel during a parking period.

[0048] Based on the first and second tire pressure values ​​for each wheel, determine the tire pressure change of each wheel of the vehicle during the current parking period.

[0049] In one feasible implementation, for each wheel, the second tire pressure value of the wheel is subtracted from its first tire pressure value to obtain the tire pressure change of the wheel during the current parking period; the above calculation is performed on all wheels one by one to obtain the tire pressure change of each wheel, which serves as the basis data for subsequent quality change determination.

[0050] In another feasible embodiment, step S301 may include: Step S3011: Determine the current parking duration of the vehicle and determine whether the current parking duration is greater than a preset duration.

[0051] The parking duration refers to the time from when the vehicle comes to a standstill (i.e., the moment the first tire pressure value is collected) to when the vehicle controller receives the preset vehicle control signal (i.e., the moment the second tire pressure value is triggered). This duration represents the total time the vehicle remains stationary during this parking period. The preset duration is a pre-defined time threshold (e.g., several hours) used to determine whether the parking time is long enough to warrant consideration of temperature compensation. When the parking duration is short, the ambient temperature change is limited, and its impact on tire pressure is negligible. When the parking duration is long, the ambient temperature may change significantly, requiring temperature compensation before calculating the tire pressure change to eliminate the impact of temperature drift on detection accuracy.

[0052] Obtain the first timestamp when collecting the first tire pressure value and the second timestamp when collecting the second tire pressure value; calculate the time difference between the second timestamp and the first timestamp, and use this difference as the current parking duration; compare the calculated current parking duration with the preset duration, and select different tire pressure change calculation methods based on the comparison results.

[0053] In one feasible implementation, the system records the system clock value when collecting the first tire pressure value and the second tire pressure value, respectively, and calculates the difference between the two clock values ​​as the parking duration. The preset duration is 4 hours. If the parking duration is longer than 4 hours, it is determined that temperature compensation is required. If it is less than 4 hours, it is determined that the temperature effect is negligible, and the change is calculated directly using the measured value.

[0054] In another feasible implementation, a timer is started when the vehicle enters a stationary state. When a preset vehicle control signal is received, the cumulative value of the timer is read as the current parking duration. If the cumulative value is greater than the preset duration (e.g., 6 hours), the temperature compensation calculation process is started. If it is less than or equal to the preset duration, the measured value is used directly for calculation.

[0055] Step S3012: If the current parking time is longer than the preset time, then for each wheel of the vehicle, the first tire pressure value of the wheel is converted into a third tire pressure value at a preset reference temperature, and the second tire pressure value of the wheel is converted into a fourth tire pressure value at the preset reference temperature. Step S3013: The difference between the fourth tire pressure value and the third tire pressure value is taken as the tire pressure change of the wheel during the current parking period.

[0056] The preset reference temperature refers to a uniform baseline temperature value (e.g., 25℃, or 298.15K) set in advance to eliminate the influence of temperature on tire pressure. It is used to compare tire pressure measurements taken at different times under the same temperature conditions. The third tire pressure value refers to the equivalent tire pressure value at the preset reference temperature, calculated by converting the first tire pressure value (the measured value taken when the vehicle is parked) to the actual tire temperature at that time. The fourth tire pressure value refers to the equivalent tire pressure value at the same preset reference temperature, calculated by converting the second tire pressure value (the measured value taken before starting the vehicle) to the actual tire temperature at that time.

[0057] The basic principle of temperature compensation is based on the approximate relationship of the ideal gas law. Under the condition that the tire volume remains approximately constant, tire pressure is approximately proportional to thermodynamic temperature. Therefore, the measured tire pressure can be converted to a uniform reference temperature according to the temperature ratio. This conversion eliminates tire pressure drift caused by changes in ambient temperature during parking, ensuring that the calculated tire pressure change accurately reflects the tire load changes caused by variations in load.

[0058] When the parking duration is determined to be longer than a preset duration, the temperature compensation calculation process begins. For each wheel, the tire temperature value simultaneously measured by the tire pressure monitoring sensor when the first tire pressure value is collected, and the tire temperature value simultaneously measured when the second tire pressure value is collected, are obtained. According to the temperature compensation formula of the ideal gas law, the first tire pressure value and its corresponding temperature value of the wheel are substituted into the formula and converted to a preset reference temperature to obtain the third tire pressure value. The second tire pressure value and its corresponding temperature value of the wheel are substituted into the same formula and converted to a preset reference temperature to obtain the converted fourth tire pressure value. The fourth tire pressure value of the wheel is subtracted from the third tire pressure value, and the difference is the tire pressure change of the wheel after temperature compensation. The above temperature compensation and difference calculation are performed on all wheels one by one to obtain the tire pressure change of each wheel, which is used for subsequent determination of sudden quality changes.

[0059] For example, the temperature compensation formula can be expressed as:

[0060] in To standardize reference temperatures, Let be the measured temperature of the i-th tire at the time of data collection. This is the measured tire pressure value of the i-th tire, converted to the preset reference temperature. The compensated tire pressure change is: .

[0061] Step S3014: If the current parking duration is less than or equal to the preset duration, then for each wheel of the vehicle, the difference between the second tire pressure value and the first tire pressure value of the wheel is taken as the tire pressure change of the wheel during the current parking period.

[0062] When the parking duration is less than or equal to the preset duration, it indicates a short parking time. During this period, the ambient temperature changes are limited, and the resulting temperature drift in tire pressure is less than the allowable error range. Therefore, temperature compensation is not required. In this case, the difference between the two measured tire pressure values ​​can accurately reflect the tire pressure changes during parking.

[0063] When the current parking duration is determined to be less than or equal to the preset duration, a simplified calculation process is initiated, without the need to obtain tire temperature values ​​or perform temperature compensation calculations. For each wheel, the collected second tire pressure value is directly subtracted from the first tire pressure value, and the difference is taken as the tire pressure change of that wheel during the current parking period. The above calculation is performed on all wheels one by one to obtain the tire pressure change of each wheel.

[0064] For example, calculate the tire pressure change of each tire during parking: .

[0065] Step S302: Based on the tire pressure changes, determine whether there is a sudden change in the quality of the vehicle during the current parking period.

[0066] The core logic for determining whether a quality mutation exists based on the changes in tire pressure is as follows: loading will increase the load on each tire, and the tire pressure of each vehicle will rise synchronously; unloading will decrease the load on each tire, and the tire pressure of each vehicle will drop synchronously. That is, by judging whether the direction of tire pressure change of each wheel is consistent (changing in the same direction) and whether the change amplitude exceeds the preset threshold, it is confirmed whether a valid loading or unloading event has occurred.

[0067] The vehicle control system also includes a tire pressure change detection module, which can read the tire pressure values ​​of each tire while the vehicle is stationary, calculate the difference between the tire pressure reference value before parking (or the last recorded value), and detect whether a valid loading or unloading event has occurred through a multi-tire joint decision algorithm.

[0068] In this embodiment, step S302 may include: Step S3021: Determine the direction of tire pressure change for each wheel based on the tire pressure change amount; Step S3022: If the number of target wheels with the same direction of tire pressure change is greater than the preset number, then the average tire pressure change of each target wheel is determined based on the tire pressure change of each target wheel. Step S3023: If the absolute value of the average tire pressure change is greater than the first preset change, it is determined that the vehicle has a sudden quality change during the current parking period.

[0069] The direction of tire pressure change refers to the sign of the change in tire pressure of each wheel relative to zero. Loading or unloading a truck causes the tire pressure of all the load-bearing tires to change in the same direction: all tire pressures increase during loading and decrease during unloading. This is the core physical basis for judging sudden changes in quality.

[0070] The target wheels refer to those wheels whose tire pressure changes in the same direction and in the same direction. The preset number is a pre-set threshold number used to determine whether the number of wheels with tire pressure changing in the same direction is sufficient to support the identification of a qualitative change event (e.g., more than half of the wheels).

[0071] The average tire pressure change refers to the arithmetic mean of the tire pressure changes of all target wheels, used to characterize the magnitude of the overall load change. The first preset change is a pre-defined threshold value. Only when the absolute value of the average tire pressure change exceeds this threshold is the tire pressure change considered sufficiently large to constitute a valid quality mutation event, avoiding false triggering due to minor fluctuations. It should be noted that if the tire pressure change rate exceeds the physical limit (e.g., 100 kPa / s), it is considered a sensor malfunction and a reset is not triggered.

[0072] After obtaining the tire pressure change of each wheel, the direction of tire pressure change for each wheel is first determined. The tire pressure change of each wheel is compared with a preset dead zone threshold. Tire pressure changes greater than the dead zone threshold are determined to be in the direction of increasing tire pressure, tire pressure changes less than a negative dead zone threshold are determined to be in the direction of decreasing tire pressure, and tire pressure changes with absolute values ​​within the dead zone threshold range are determined to be in the direction of unchanged tire pressure. Then, the number of wheels in each direction is counted to determine if there is a number of wheels in a certain direction greater than a preset number (e.g., more than half of the wheels). If so, these wheels with the same tire pressure change direction are identified as target wheels. Subsequently, the average tire pressure change of all target wheels is calculated as the average tire pressure change. The absolute value of this average tire pressure change is compared with a first preset change value. If it is greater than the first preset change value, it is determined that there is a sudden change in vehicle quality during the current parking period. Among them, a positive average tire pressure change is determined as a loading event, and a negative average tire pressure change is determined as an unloading event.

[0073] In one feasible implementation, the sign of the tire pressure change direction for each tire is calculated: , where ign() is a standard symbolic function, when When >ε = +1 (Tire pressure rises, indicating loading); when When ≤ε, = 0 (tire pressure remains constant); when When <-ε, = -1 (Tire pressure drop, indicating unloading). ε is the dead zone threshold (e.g., 3~5 kPa), used to eliminate sensor noise and minor fluctuations. The direction is considered consistent when any of the following conditions are met:

[0074]

[0075] That is, the above formula indicates that more than half of the tires change in the same direction.

[0076] Furthermore, calculate the average change in tires changing in the same direction:

[0077] in This refers to a set of tires whose direction of change is consistent with the mainstream direction. This represents the number of tires in the set. When > When this occurs, it is considered a valid mutation event. This is the amplitude threshold (e.g., 15-20 kPa, corresponding to a load change of about 1-2 tons, which can be calibrated according to the vehicle model), also known as the first preset change amount.

[0078] For example, the dead zone threshold ε is set to 5 kPa, and the preset number is set to 2 (i.e., more than half – half of a four-wheeled vehicle has 2 wheels, and more than half has at least 3). The tire pressure changes of the four wheels are +25 kPa, +22 kPa, +28 kPa, and +20 kPa, respectively. Each change is greater than the +5 kPa dead zone threshold, so the tire pressure change direction of all four wheels is upward. The number of wheels in the upward direction is 4, which is greater than the preset number of 2. These 4 wheels are identified as target wheels; the average tire pressure change is calculated to be +23.75 kPa; the first preset change... If the average tire pressure change is set to 15 kPa, and the absolute value of the change is 23.75 kPa > 15 kPa, a loading event is determined to have occurred, meaning that there was a sudden change in quality during the parking period.

[0079] Step S3024: If the number of target wheels with the same direction of tire pressure change is less than or equal to a preset number, or the average tire pressure change is less than or equal to a preset change, then it is determined that the vehicle does not have a sudden change in quality during the current parking period.

[0080] After statistically analyzing the direction of tire pressure changes for each wheel, if the number of wheels in any direction does not exceed a preset number (i.e., the number of tires in each direction does not exceed the preset number), then it is directly determined that there is no quality mutation, and there is no need to calculate the average tire pressure change. Alternatively, even if the number of wheels in a certain direction exceeds the preset number and the average tire pressure change is calculated, if the absolute value of the average tire pressure change is less than or equal to the first preset change amount, then it is also determined that there is no quality mutation. This judgment logic ensures that a valid quality mutation event is only considered to have occurred when both the conditions of "most wheels changing in the same direction" and "the change magnitude being sufficiently large" are simultaneously met.

[0081] For example, such as Figure 2 The diagram illustrates the quality mutation detection process. When the vehicle switches from a driving state to a stationary state, the first tire pressure value at the current moment is recorded. The vehicle remains stationary, waiting to restart (i.e., waiting for a preset vehicle control signal). When the preset vehicle control signal is detected, the second tire pressure value at the current moment is recorded. It is determined whether the current parking time exceeds a preset time. If so, temperature compensation is applied to the first and second tire pressure values ​​of each wheel to obtain the third and fourth tire pressure values ​​at the same temperature. The difference between the fourth and third tire pressure values ​​of each tire is calculated as the tire pressure change. If not, the difference between the second and first tire pressure values ​​of each tire is calculated as the tire pressure change. It is determined whether each tire pressure change meets preset conditions (the number of tires changing in the same direction is greater than a preset number and the amplitude of the average tire pressure change is greater than the first preset change). If the preset conditions are met, a quality mutation is determined to exist; otherwise, no quality mutation is determined to exist.

[0082] Thus, this embodiment of the application, through a multi-tire joint decision mechanism, only confirms a quality mutation when more than a preset number of wheels change in the same direction and the average change exceeds a preset threshold. This effectively eliminates the interference of occasional fluctuations caused by factors such as single-wheel air leakage, abnormal temperature, or sensor noise on the judgment result, reduces the false judgment rate, and avoids unnecessary estimation fluctuations caused by the Kalman filter frequently amplifying the covariance matrix when no quality mutation occurs due to false triggering. This ensures the stability and reliability of the quality estimation algorithm under non-mutation conditions.

[0083] Step S40: If the vehicle experiences a sudden change in quality during the current parking period, the covariance matrix of the Kalman filter running on the vehicle is amplified, and the overall vehicle quality is estimated based on the amplified covariance matrix to obtain a quality estimate.

[0084] The Kalman filter is a recursive optimal estimation algorithm based on a state-space model. In this application, it operates within the vehicle's electronic control unit to perform online estimation of the vehicle's mass using driving force and longitudinal acceleration signals during vehicle operation. The covariance matrix, also known as the error covariance matrix, is a core parameter in the Kalman filter, characterizing the filter's uncertainty regarding the current state estimate. Specifically, a smaller covariance matrix indicates greater confidence in the estimate, a smaller Kalman gain, and a weaker response to new measurement data; conversely, a larger covariance matrix indicates greater uncertainty about the current estimate, a larger Kalman gain, and greater sensitivity to new sensor data.

[0085] Enlarging the covariance matrix means increasing the value of each element in the matrix. The purpose is to artificially increase the uncertainty of the filter, so that the filter can regain its ability to respond to new measurement data, and thus quickly correct the quality estimate to the new true value during subsequent driving.

[0086] When a quality mutation is determined to have occurred during parking based on a comparison of tire pressure data collected from two stationary states, a quality mutation flag is generated and stored in the vehicle's storage unit. After the vehicle enters a driving state, the Kalman filter quality estimation module (i.e., the Kalman filter) reads this quality mutation flag. If the flag is valid, a covariance matrix amplification operation is performed, increasing the Kalman gain of the filter during subsequent driving. Thereafter, in each recursive update, the filter uses newly acquired driving force and longitudinal acceleration signals to correct the quality state, causing the quality estimate to quickly converge to the new true quality value. Thus, this embodiment of the application achieves time-sharing coordination of detection during parking and rapid estimation during driving through the cooperation of the aforementioned static tire pressure detection and covariance reset.

[0087] For example, such as Figure 3The diagram shows the system architecture. The vehicle control system of this application includes a parking state detection module, a tire pressure monitoring module, a tire pressure surge detection module, a Kalman filter quality estimation module, a covariance reset control module, and a quality output module. Specifically, the parking state detection module determines whether the vehicle is stationary based on vehicle speed signals, ignition switch status signals, and parking brake signals; the tire pressure monitoring module collects tire pressure values ​​for each tire after the parking state detection module determines the vehicle is stationary, and collects tire pressure values ​​for each tire again upon receiving a preset vehicle control signal; the tire pressure surge detection module determines whether a quality surge has occurred based on the tire pressure values ​​collected twice by the tire pressure monitoring module; the covariance reset control module performs a graded reset of the covariance matrix when the tire pressure surge detection module determines a quality surge has occurred; the Kalman filter quality estimation module estimates the overall vehicle mass based on the vehicle's longitudinal dynamics model after the covariance matrix is ​​reset; and the quality output module outputs the estimated results after smoothing and verifying their reasonableness.

[0088] In this embodiment, tire pressure detection is performed while the vehicle is stationary. At this time, there is no load transfer caused by acceleration, braking, or steering. Tire pressure changes directly reflect load changes, and the detection timing precisely covers the physical time period during loading or unloading operations, allowing mass changes to be captured before the vehicle restarts. After a mass change is confirmed, the Kalman filter's covariance matrix is ​​amplified to synchronously increase the Kalman gain, enabling the filter to regain its responsiveness to new sensor data. This allows the mass estimate to quickly converge to the new true value during subsequent driving, shortening the transition time for the filter to re-track after a mass change. Thus, this embodiment solves the data saturation problem in traditional solutions caused by the covariance matrix shrinking to a minimum and the Kalman gain approaching zero after long-term operation of the Kalman filter, improving the accuracy of mass estimation and driving safety under load change conditions.

[0089] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, step S40 may include: Step A10: Determine the intensity of the mass mutation of the vehicle based on the absolute value of the average tire pressure change.

[0090] The intensity of quality mutation refers to a graded parameter used to characterize the severity of quality changes, based on the absolute value of the change in average tire pressure. This application classifies quality mutations into different intensity levels based on the relationship between the absolute value of the change in average tire pressure and a preset threshold, so as to facilitate the subsequent implementation of a graded covariance reset strategy. Different mutation intensities correspond to different covariance amplification methods, ensuring that the degree of covariance reset matches the actual degree of load change.

[0091] After confirming the existence of a quality mutation, obtain the calculated average tire pressure change; calculate the absolute value of the average tire pressure change; compare the absolute value with multiple preset amplitude thresholds, and determine the corresponding quality mutation intensity level according to the numerical range in which it is located; use the intensity level as the input parameter for the subsequent covariance amplification step.

[0092] In this embodiment, step A10 may include: Step A101: If the absolute value of the average tire pressure change is greater than the first preset change and less than or equal to the second preset change, the mass change intensity of the vehicle is determined to be the first intensity, wherein the second preset change is greater than the first preset change. Step A102: If the absolute value of the average tire pressure change is greater than the second preset change amount, the intensity of the mass change of the vehicle is determined to be the second intensity.

[0093] First preset change amount The minimum threshold for determining whether a quality mutation has occurred is defined above; a quality mutation is only confirmed when the absolute value of the change in average tire pressure exceeds this threshold. The second preset change amount... A threshold value (e.g., 50 kPa) above the first preset change is used to distinguish the severity of the quality change. The absolute value of the mean tire pressure change is... and When the value is between these values, it is determined to be of the first intensity (mild mutation); when it exceeds this value, it is determined to be of the second intensity (mild mutation). It was determined to be of the second intensity (severe mutation).

[0094] Obtain the absolute value of the average tire pressure change; compare the absolute value with the first preset change and the second preset change in sequence; if the absolute value is greater than the first preset change and less than or equal to the second preset change, determine the quality change intensity as the first intensity; if the absolute value is greater than the second preset change, determine the quality change intensity as the second intensity; output the determined intensity level to the covariance amplification step as the basis for selecting the corresponding amplification strategy.

[0095] For example, in When >δmag, it indicates that the mutation type of the current quality mutation is a loading event; When <-δmag, it indicates that the mutation type of the current quality mutation is an unloading event. The intensity level of the quality mutation is represented by the denoted level. If, then Level = 1; if If so, then Level = 2.

[0096] Step A20: Based on the intensity of the quality mutation, amplify the covariance matrix of the Kalman filter running on the vehicle.

[0097] A higher mass mutation intensity indicates a greater load change, requiring the filter to significantly increase uncertainty (i.e., a larger covariance matrix amplification factor) to track the new true mass value more quickly during subsequent driving. Covariance matrix amplification is achieved by multiplying the current covariance matrix by a coefficient greater than 1 (mild mutation), or by directly resetting the covariance matrix to a preset initial large value (severe mutation).

[0098] The vehicle control system also includes a covariance reset control module, which executes a graded covariance reset strategy based on the output of the tire pressure surge detection module during vehicle startup or driving. For lower intensity levels, a slight amplification is performed by multiplying the current covariance matrix by an adaptive amplification factor; for higher intensity levels, a complete reset is performed by directly setting the covariance matrix to a preset initial value. The amplified or reset covariance matrix is ​​used as the initial value for the subsequent recursive calculation of the covariance matrix by the Kalman filter, enabling the filter to respond quickly to new sensor data with an increased Kalman gain and converge to the new true quality value during subsequent driving.

[0099] In this embodiment, step A20 may include: Step A201: When the intensity of the quality mutation is the first intensity, determine the first difference between the absolute value of the average tire pressure change and the first preset change, and determine the second difference between the second preset change and the first preset change. Step A202: Determine the amplification factor based on the ratio of the first difference to the second difference, and amplify the covariance matrix based on the amplification factor to obtain the amplified covariance matrix; Step A203: When the intensity of the quality mutation is the second intensity, the preset initial covariance matrix is ​​used as the amplified covariance matrix.

[0100] The first difference refers to the difference between the absolute value of the change in average tire pressure and the first preset change, representing the degree to which the current mutation amplitude exceeds the minimum mutation threshold. The second difference refers to the difference between the second preset change and the first preset change, representing the total width of the first intensity range (mild mutation range). The amplification factor is a value greater than 1 and less than or equal to 2, determined based on the ratio of the first difference to the second difference, used to proportionally amplify the covariance matrix. When the mutation amplitude is close to... When the amplification factor is close to 1 (covariance is almost constant), when the amplification magnitude is close to The amplification factor is close to 2 (the covariance is amplified to about 2 times).

[0101] The preset initial covariance matrix refers to the initial value of the covariance matrix that is set when the Kalman filter is started. It is usually set to a large value (such as 10). 6 This indicates that the filter is completely uncertain about the initial state estimate and needs to converge gradually during the filtering process. Under the second intensity (severe abrupt change), the covariance matrix is ​​directly reset to... This indicates that the filter has completely lost confidence in the current quality estimate and needs to rely entirely on subsequent measurement data to rebuild the estimate.

[0102] After determining the intensity of the mass mutation, if it is the first intensity, calculate the first difference and the second difference; then calculate the ratio of the first difference to the second difference, and add this ratio to 1 to obtain the amplification factor. Multiply the current covariance matrix P by The amplified covariance matrix is ​​obtained; if it is the second intensity, the preset initial covariance matrix is ​​directly used. As an amplified covariance matrix, the amplified covariance matrix is ​​output to the recursive calculation of the Kalman filter. During subsequent driving, the increased Kalman gain quickly responds to new driving force and acceleration measurement data, enabling the mass estimate to converge rapidly to the new true value.

[0103] For example, for a small change in load (i.e., the first intensity), the amplified covariance matrix is ​​calculated: ,in, This is the magnified covariance matrix. This is the covariance matrix obtained in this iteration, where α is the amplification factor, which is adaptively adjusted according to the magnitude of tire pressure changes.

[0104] For large load variations (i.e., the second intensity), calculate the amplified covariance matrix: ,in, The initial covariance matrix is ​​preset, indicating that the filter is "completely uncertain" about the current quality estimate.

[0105] It should be noted that a preset minimum time interval (such as 60 seconds) is required between two covariance matrix resets to avoid frequent resets.

[0106] Thus, this embodiment determines the intensity of quality mutation based on the absolute value of the average tire pressure change, and then performs graded covariance amplification according to the intensity level. Specifically, for mild mutations, the amplification factor is adaptively determined based on the mutation amplitude to moderately improve the filter's sensitivity; for severe mutations, the covariance matrix is ​​directly reset to a preset initial value to completely rebuild the filter's confidence in the estimate. Based on this, the current quality estimate remains unchanged during covariance amplification, only the value of the covariance matrix is ​​changed. This allows the filter to retain existing estimation information while cooperating with the increased covariance matrix. During subsequent driving, the increased Kalman gain rapidly absorbs dynamic measurement data such as driving force and acceleration, thereby quickly converging the quality estimate to a new true value within a few sampling periods. This achieves a reasonable match between the covariance reset intensity and the degree of quality mutation, avoiding the problems of excessive amplification leading to estimation fluctuations during small mutations and insufficient amplification causing slow convergence during large mutations under a fixed reset method. This further improves the adaptability and convergence efficiency of the quality estimation algorithm under various load variation scenarios.

[0107] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, in this embodiment, step S40 may include: Step B10: Estimate the total vehicle mass of the vehicle based on the amplified covariance matrix using the Kalman filter to obtain the first estimated mass; Step B20: Obtain the second estimated mass output by the Kalman filter when it last estimated the total mass of the vehicle; Step B30: Perform a weighted summation on the first estimated mass and the second estimated mass to obtain the smoothed vehicle mass.

[0108] The first estimated quality refers to the vehicle mass estimate at the current moment, output by the Kalman filter after recursively optimizing the longitudinal dynamics model using the driving force and longitudinal acceleration signals acquired at the current moment, after the covariance matrix is ​​amplified. The second estimated quality refers to the vehicle mass estimate output by the Kalman filter at the previous sampling moment, i.e., the historical mass value finally output after the previous round of smoothing and rationality verification. The weighted summation process involves multiplying the first and second estimated qualities by their respective weighting coefficients and then summing them to suppress high-frequency fluctuations in the estimation results. Its core idea is to fuse the current estimated value (containing current measurement information but possibly containing noise) with the historical output value (smoothed and relatively stable), suppressing drastic jumps in the estimation results while maintaining the tracking response speed.

[0109] After the covariance matrix is ​​amplified, the Kalman filter enters the recursive estimation process: first, state prediction is performed based on the mass estimate value of the previous time step; then, the Kalman gain is calculated using the driving force and longitudinal acceleration collected at the current time step; the predicted value is corrected by combining the gain value to obtain the mass estimate value of the current time step as the first estimated mass; then, the mass estimate value of the final output of the previous time step is obtained as the second estimated mass; the first estimated mass and the second estimated mass are multiplied by the corresponding weight coefficients and then summed to obtain the smoothed vehicle mass, so as to suppress the high-frequency fluctuation of the estimated value caused by sensor noise or dynamic disturbance.

[0110] Specifically, the Kalman filter quality estimation module in the vehicle control system uses the following state-space model for quality estimation during driving: State vector:

[0111] Equation of state (mass is considered constant in a short time):

[0112] Observation equations (based on the longitudinal dynamics model):

[0113] in, As the driving force, For longitudinal acceleration, For process noise, For measuring noise.

[0114] Furthermore, the Kalman filtering recursive process includes: The prediction step includes state prediction and covariance prediction.

[0115] State prediction:

[0116] Covariance prediction:

[0117] The update steps include Kalman gain update, state update, and covariance update.

[0118] Kalman gain:

[0119] Status Update:

[0120] Covariance update:

[0121] For example, such as Figure 4 The diagram shows the Kalman filter estimation process. After the vehicle restarts, the Kalman filter performs a prediction step and checks whether there is a sudden change in quality during the current stop. If so, the covariance is reset in stages; otherwise, the current covariance is retained. Vehicle driving data (driving force and acceleration) is collected. The Kalman gain is calculated based on the vehicle driving data and the covariance matrix. Then, an update step is performed based on the Kalman gain to output an estimate of the vehicle quality (i.e., the first estimated quality).

[0122] In one feasible implementation, the vehicle mass estimated based on the amplified covariance matrix (i.e., the first estimated mass) is used as the final mass estimate.

[0123] In another feasible implementation, a first-order low-pass filter is used when smoothing the first and second estimated qualities obtained by Kalman filtering: ,in, This refers to the estimated vehicle mass at that time (i.e., the first estimated mass). This refers to the previously estimated vehicle mass (i.e., the second estimated mass). The smoothing coefficient β is dynamically adjusted based on whether a reset occurs. After a reset, β is larger (e.g., 0.3, fast response), while in steady state, β is smaller (e.g., 0.05, smoothing priority).

[0124] Step B40: Perform a rationality check on the smoothed vehicle mass to obtain the estimated mass value.

[0125] Reasonableness verification refers to performing a physical feasibility check on the smoothed vehicle mass to determine whether it falls within a preset reasonable range. The upper limit of this preset range is the maximum permissible gross vehicle weight, and the lower limit is the vehicle's unloaded weight. If the smoothed vehicle mass exceeds this range, it indicates that the current estimate is physically unrealizable, possibly due to sensor malfunction, model error, or extreme operating conditions, and requires correction; if it falls within this range, it indicates that the estimate is physically reasonable.

[0126] After obtaining the smoothed vehicle mass through weighted summation, this value is compared with a preset reasonable range: if the smoothed vehicle mass is within the preset range, it is directly output as the final mass estimate; if the smoothed vehicle mass exceeds the preset range, it is corrected to the corresponding boundary value (outputting the lower limit when it is below the lower limit, and outputting the upper limit when it is above the upper limit), and optionally, an estimation anomaly flag is issued; the final output mass estimate is used by vehicle dynamics control systems such as shift strategy, braking distance calculation, drive force distribution, and electronic stability program.

[0127] For example, the output value is physically validated; if it exceeds a preset range, […]. , If the unloaded mass reaches the maximum permissible total mass, the amplitude is limited to the boundary value, and an "estimated anomaly" flag is issued.

[0128] Thus, this embodiment of the application effectively suppresses high-frequency fluctuations in the quality estimate caused by sensor noise or dynamic disturbances by weighted summing of the first estimated quality estimated by the Kalman filter and the second estimated quality output at the previous moment, making the output quality estimate smoother and more stable. On this basis, the smoothed vehicle quality is further validated for reasonableness, and the estimated value that exceeds the reasonable range of vehicle physics is corrected to the boundary value. This avoids abnormal estimated values ​​caused by sensor failure, model error or extreme conditions being directly output to the vehicle dynamics control system, further ensuring the physical credibility and engineering usability of the output results, and improving the reliability and safety of the quality estimation results in real vehicle applications.

[0129] This application also provides a vehicle mass estimation device; please refer to... Figure 5 The vehicle mass estimation device includes: The first acquisition module 10 is used to acquire the first tire pressure values ​​of the vehicle in response to the vehicle switching from a driving state to a stationary state. The second acquisition module 20 is used to acquire each second tire pressure value of the vehicle in response to a preset vehicle control signal, wherein the vehicle control signal indicates that the vehicle has the intention to switch from a stationary state to a driving state. The judgment module 30 is used to determine whether there is a sudden change in the quality of the vehicle during the current parking period based on each of the first tire pressure values ​​and each of the second tire pressure values; The amplification module 40 is used to amplify the covariance matrix of the Kalman filter running on the vehicle if there is a sudden change in the quality of the vehicle during the current parking period, and estimate the overall vehicle quality based on the amplified covariance matrix to obtain a quality estimate.

[0130] Optionally, the determination module 30 is further configured to: Based on each of the first tire pressure values ​​and each of the second tire pressure values, determine the tire pressure change of each wheel of the vehicle during the current parking period; Based on the tire pressure changes, determine whether the vehicle experienced a sudden quality change during the current parking period.

[0131] Optionally, the determination module 30 is further configured to: Determine the current parking duration of the vehicle, and determine whether the current parking duration is greater than a preset duration; If the current parking duration is longer than the preset duration, then for each wheel of the vehicle, the first tire pressure value of the wheel is converted into a third tire pressure value at a preset reference temperature, and the second tire pressure value of the wheel is converted into a fourth tire pressure value at the preset reference temperature. The difference between the fourth tire pressure value and the third tire pressure value is taken as the tire pressure change of the wheel during the current parking period; If the current parking duration is less than or equal to the preset duration, then for each wheel of the vehicle, the difference between the second tire pressure value and the first tire pressure value of the wheel is taken as the tire pressure change of the wheel during the current parking period.

[0132] Optionally, the determination module 30 is further configured to: Based on the tire pressure change amounts, determine the direction of tire pressure change for each wheel; If the number of target wheels with the same direction of tire pressure change is greater than the preset number, then the average tire pressure change of each target wheel is determined based on the tire pressure change of each target wheel. If the absolute value of the average tire pressure change is greater than a first preset change, it is determined that the vehicle has a sudden quality change during the current parking period. If the number of target wheels with the same direction of tire pressure change is less than or equal to a preset number, or if the average tire pressure change is less than or equal to a preset change, then it is determined that the vehicle does not have a sudden change in quality during the current parking period.

[0133] Optionally, the amplification module 40 is further configured to: The intensity of the mass mutation of the vehicle is determined based on the absolute value of the average tire pressure change. Based on the intensity of the quality mutation, the covariance matrix of the Kalman filter running on the vehicle is amplified.

[0134] Optionally, the amplification module 40 is further configured to: If the absolute value of the average tire pressure change is greater than the first preset change and less than or equal to the second preset change, the mass change intensity of the vehicle is determined to be the first intensity, wherein the second preset change is greater than the first preset change. If the absolute value of the average tire pressure change is greater than the second preset change amount, the intensity of the mass change of the vehicle is determined to be the second intensity.

[0135] Optionally, the amplification module 40 is further configured to: When the intensity of the quality mutation is the first intensity, a first difference between the absolute value of the average tire pressure change and the first preset change is determined, and a second difference between the second preset change and the first preset change is determined. The amplification factor is determined based on the ratio of the first difference to the second difference, and the covariance matrix is ​​amplified based on the amplification factor to obtain the amplified covariance matrix. When the intensity of the quality mutation is the second intensity, the preset initial covariance matrix is ​​used as the amplified covariance matrix.

[0136] Optionally, the amplification module 40 is further configured to: The vehicle's overall mass is estimated using the Kalman filter based on the amplified covariance matrix to obtain a first estimated mass; Obtain the second estimated mass output by the Kalman filter when it last estimated the total mass of the vehicle; The first estimated mass and the second estimated mass are weighted and summed to obtain the smoothed vehicle mass. The rationality of the smoothed vehicle mass is verified to obtain the estimated mass value.

[0137] The vehicle quality estimation device provided in this application, employing the vehicle quality estimation method described in the above embodiments, can solve the technical problem of improving the accuracy of Kalman filter-based quality estimation under conditions of sudden changes in vehicle quality. Compared with the prior art, the beneficial effects of the vehicle quality estimation device provided in this application are the same as those of the vehicle quality estimation method provided in the above embodiments, and other technical features in the vehicle quality estimation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0138] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the vehicle mass estimation method in Embodiment 1 above.

[0139] The following is for reference. Figure 6 It shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of this application. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0140] like Figure 6As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0141] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0142] The electronic device provided in this application, employing the vehicle mass estimation method described in the above embodiments, can solve the technical problem of improving the accuracy of Kalman filter-based mass estimation under conditions of sudden changes in vehicle mass. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the vehicle mass estimation method provided in the above embodiments, and other technical features of this electronic device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0143] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0144] 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.

[0145] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the vehicle mass estimation method in the above embodiments.

[0146] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0147] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0148] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: acquire first tire pressure values ​​of the vehicle in response to the vehicle switching from a driving state to a stationary state; acquire second tire pressure values ​​of the vehicle in response to a preset vehicle control signal, wherein the vehicle control signal indicates that the vehicle intends to switch from a stationary state to a driving state; determine whether there is a quality mutation in the vehicle during the current parking period based on the first tire pressure values ​​and the second tire pressure values; if there is a quality mutation in the vehicle during the current parking period, amplify the covariance matrix of the Kalman filter running on the vehicle, and estimate the overall vehicle mass based on the amplified covariance matrix to obtain a quality estimate.

[0149] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0151] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0152] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described vehicle quality estimation method. This addresses the technical problem of improving the accuracy of Kalman filter-based quality estimation under conditions of sudden changes in vehicle quality. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the vehicle quality estimation method provided in the above embodiments, and will not be elaborated upon here.

[0153] This application provides a vehicle having the electronic equipment described above, the electronic equipment being used to perform the vehicle mass estimation method in the above embodiments.

[0154] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle mass estimation method described above.

[0155] The computer program product provided in this application can improve the accuracy of Kalman filter-based quality estimation under conditions of sudden changes in vehicle quality. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle quality estimation method provided in the above embodiments, and will not be repeated here.

[0156] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for estimating vehicle mass, characterized in that, The vehicle mass estimation method includes: In response to the vehicle switching from a driving state to a stationary state, the first tire pressure values ​​of the vehicle are obtained; In response to a preset vehicle control signal, the second tire pressure values ​​of the vehicle are acquired, wherein the vehicle control signal indicates that the vehicle intends to switch from a stationary state to a driving state. Based on each of the first tire pressure values ​​and each of the second tire pressure values, determine whether the vehicle experiences a sudden change in quality during the current parking period; If the vehicle experiences a sudden change in quality during the current parking period, the covariance matrix of the Kalman filter running on the vehicle is amplified, and the overall vehicle quality is estimated based on the amplified covariance matrix to obtain a quality estimate.

2. The vehicle mass estimation method as described in claim 1, characterized in that, The step of determining whether the vehicle experiences a sudden quality change during the current parking period based on each of the first tire pressure values ​​and each of the second tire pressure values ​​includes: Based on each of the first tire pressure values ​​and each of the second tire pressure values, determine the tire pressure change of each wheel of the vehicle during the current parking period; Based on the tire pressure changes, determine whether the vehicle experienced a sudden quality change during the current parking period.

3. The vehicle mass estimation method as described in claim 2, characterized in that, The step of determining the tire pressure change of each wheel of the vehicle during the current parking period based on each of the first tire pressure values ​​and each of the second tire pressure values ​​includes: Determine the current parking duration of the vehicle, and determine whether the current parking duration is greater than a preset duration; If the current parking duration is longer than the preset duration, then for each wheel of the vehicle, the first tire pressure value of the wheel is converted into a third tire pressure value at a preset reference temperature, and the second tire pressure value of the wheel is converted into a fourth tire pressure value at the preset reference temperature. The difference between the fourth tire pressure value and the third tire pressure value is taken as the tire pressure change of the wheel during the current parking period; If the current parking duration is less than or equal to the preset duration, then for each wheel of the vehicle, the difference between the second tire pressure value and the first tire pressure value of the wheel is taken as the tire pressure change of the wheel during the current parking period.

4. The vehicle mass estimation method as described in claim 2, characterized in that, The step of determining whether the vehicle experiences a sudden quality change during the current parking period based on the tire pressure changes includes: Based on the tire pressure change amounts, determine the direction of tire pressure change for each wheel; If the number of target wheels with the same direction of tire pressure change is greater than the preset number, then the average tire pressure change of each target wheel is determined based on the tire pressure change of each target wheel. If the absolute value of the average tire pressure change is greater than a first preset change, it is determined that the vehicle has a sudden quality change during the current parking period. If the number of target wheels with the same direction of tire pressure change is less than or equal to a preset number, or if the average tire pressure change is less than or equal to a preset change, then it is determined that the vehicle does not have a sudden change in quality during the current parking period.

5. The vehicle mass estimation method as described in claim 4, characterized in that, The step of amplifying the covariance matrix of the Kalman filter running on the vehicle includes: The intensity of the mass mutation of the vehicle is determined based on the absolute value of the average tire pressure change. Based on the intensity of the quality mutation, the covariance matrix of the Kalman filter running on the vehicle is amplified.

6. The vehicle mass estimation method as described in claim 5, characterized in that, The step of determining the intensity of the mass mutation of the vehicle based on the absolute value of the average tire pressure change includes: If the absolute value of the average tire pressure change is greater than the first preset change and less than or equal to the second preset change, the mass change intensity of the vehicle is determined to be the first intensity, wherein the second preset change is greater than the first preset change. If the absolute value of the average tire pressure change is greater than the second preset change amount, the intensity of the mass change of the vehicle is determined to be the second intensity.

7. The vehicle mass estimation method as described in claim 6, characterized in that, The step of amplifying the covariance matrix of the Kalman filter running on the vehicle based on the intensity of the quality mutation includes: When the intensity of the quality mutation is the first intensity, a first difference between the absolute value of the average tire pressure change and the first preset change is determined, and a second difference between the second preset change and the first preset change is determined. The amplification factor is determined based on the ratio of the first difference to the second difference, and the covariance matrix is ​​amplified based on the amplification factor to obtain the amplified covariance matrix. When the intensity of the quality mutation is the second intensity, the preset initial covariance matrix is ​​used as the amplified covariance matrix.

8. The vehicle mass estimation method according to any one of claims 1 to 7, characterized in that, The step of estimating the vehicle's overall mass based on the amplified covariance matrix to obtain a mass estimate includes: The vehicle's overall mass is estimated using the Kalman filter based on the amplified covariance matrix to obtain a first estimated mass; Obtain the second estimated mass output by the Kalman filter when it last estimated the total mass of the vehicle; The first estimated mass and the second estimated mass are weighted and summed to obtain the smoothed vehicle mass. The rationality of the smoothed vehicle mass is verified to obtain the estimated mass value.

9. An electronic device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle mass estimation method as claimed in any one of claims 1 to 8.

10. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 9.

11. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the vehicle mass estimation method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the vehicle mass estimation method as described in any one of claims 1 to 8.

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