Fault diagnosis method and device of vehicle sensor, electronic equipment and storage medium

Abstract

The invention discloses a fault diagnosis method and device for a vehicle sensor, electronic equipment and a storage medium. The fault diagnosis method comprises the steps of determining a first fault calibration result between a signal observation value of a sensor and a signal output value of the sensor based on power data of a vehicle; determining a second fault calibration result between the signal observation value of the sensor and the signal output value of the sensor based on the motion data of the vehicle; determining a third fault calibration result between the signal observation value of the sensor and the signal output value of the sensor based on the rotation data of the vehicle; and obtaining a signal fault result of the sensor based on the first fault calibration result, the second fault calibration result and the third fault calibration result. According to the method, the accuracy of sensor fault diagnosis is improved.

Description

Technical Field

[0001] This application relates to the technical fields of vehicles and sensors, and in particular to a fault diagnosis method, device, electronic device and storage medium for vehicle sensors. Background Technology

[0002] With the development of science and technology and the rapid increase in the number of vehicles, enhancing the safety of vehicle systems has become particularly important. Fault diagnosis of vehicle sensors has received increasing attention in practical applications. To diagnose vehicle sensor faults, it is typically necessary to use relevant sensors to obtain vehicle state parameters (such as yaw and lateral motion) and employ relevant dynamic models to achieve the purpose of fault diagnosis.

[0003] In related technologies, sensor fault diagnosis methods rely heavily on the two-degree-of-freedom model of vehicle handling and stability, focusing only on the vehicle's motion characteristics within the linear region and failing to consider the influence of nonlinear characteristics (such as crosswinds, side slopes, etc.) and external disturbances, which affects the fault diagnosis results. Furthermore, fault diagnosis is usually performed unilaterally through dynamics or kinematics, resulting in low accuracy of fault diagnosis. Summary of the Invention

[0004] In view of the above problems, this application provides a method, apparatus, electronic device and storage medium for diagnosing vehicle sensors, which can effectively solve the problem of low accuracy in diagnosing vehicle sensor faults.

[0005] In a first aspect, this application provides a fault diagnosis method for a vehicle sensor, the method comprising: determining a first fault calibration result between the sensor's observed signal value and the sensor's output signal value based on the vehicle's dynamic data; determining a second fault calibration result between the sensor's observed signal value and the sensor's output signal value based on the vehicle's motion data; determining a third fault calibration result between the sensor's observed signal value and the sensor's output signal value based on the vehicle's rotation data; and obtaining a sensor signal fault result based on the first fault calibration result, the second fault calibration result, and the third fault calibration result.

[0006] In the technical solution of this application embodiment, an engineering approach is adopted from three aspects: vehicle kinematics, dynamics, and vehicle rotational characteristics (including vehicle suspension or steering characteristics). The fault calibration result is determined based on sensor observations and signal output values. After obtaining the three fault calibration results, a "three-party voting" mechanism can be used for fault diagnosis, greatly improving the accuracy of fault diagnosis and increasing the speed of fault exit.

[0007] In some embodiments, determining a first fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's power data includes: calculating a first signal observation value of the sensor based on the vehicle's power data; obtaining a first estimated residual value based on the first signal observation value of the sensor and the sensor's signal output value; and obtaining a first fault calibration result based on the first estimated residual value and a first residual value threshold.

[0008] In some embodiments, determining a second fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's motion data includes: calculating the sensor's second signal observation value based on the vehicle's motion data; obtaining a second estimated residual value based on the sensor's second signal observation value and the sensor's signal output value; and obtaining a second fault calibration result based on the second estimated residual value and a second residual value threshold.

[0009] In some embodiments, determining a third fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's rotation data includes: calculating a third signal observation value of the sensor based on the vehicle's rotation data; obtaining a third estimated residual value based on the sensor's third signal observation value and the sensor's signal output value; and obtaining a third fault calibration result based on the third estimated residual value and a third residual value threshold.

[0010] In the technical solution of this application embodiment, residual values ​​are calculated based on parameter information such as sensor power data, motion data, and rotation data, combined with the sensor output values. The results are then compared with threshold values ​​to determine the fault calibration result. This method simplifies the calculation process, classifies and analyzes the data, improves processing efficiency, and makes it easier to manage.

[0011] In some embodiments, the sensor signal includes a longitudinal acceleration signal; based on the vehicle's power data, the first signal observation value of the sensor is calculated, including: based on the vehicle's total motor torque, total transmission ratio, transmission efficiency of the transmission system, wheel rolling radius, vehicle mass, longitudinal slope of the road on which the vehicle is traveling, air resistance coefficient, vehicle frontal area, longitudinal vehicle speed, road rolling resistance coefficient, and rotational mass conversion coefficient, the first longitudinal acceleration observation value of the sensor is calculated.

[0012] In the technical solution of this application embodiment, the vehicle's power data includes the total torque of the motor, the total transmission ratio, and the transmission efficiency of the transmission system, etc., and the first longitudinal acceleration observation value is calculated based at least on the vehicle's power data. Specifically, the first longitudinal acceleration observation value can be calculated by combining the vehicle's power data with other types of data to improve the calculation accuracy.

[0013] In some embodiments, the sensor signal includes a longitudinal acceleration signal; based on the vehicle's motion data, a second signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, a second longitudinal acceleration observation value of the sensor is calculated.

[0014] In the technical solution of this application embodiment, the vehicle motion data includes longitudinal vehicle speed, and a second longitudinal acceleration observation value is calculated based on the longitudinal vehicle speed, thereby reducing computational complexity and improving the accuracy of the calculation results.

[0015] In some embodiments, the sensor signal includes a longitudinal acceleration signal; based on the vehicle's rotation data, a third signal observation value of the sensor is calculated, including: based on the vehicle's body pitch angle and body pitch gradient, a third longitudinal acceleration observation value of the sensor is calculated.

[0016] In the technical solution of this application embodiment, the vehicle rotation data includes the vehicle pitch angle and the vehicle pitch gradient. The third longitudinal acceleration observation value is calculated based on the vehicle pitch angle and the vehicle pitch gradient, which reduces the computational complexity and improves the accuracy of the calculation results.

[0017] In some embodiments, the sensor signal includes a lateral acceleration signal; based on the vehicle's dynamic data, a first signal observation value of the sensor is calculated, including: calculating the vehicle yaw rate based on the vehicle wheelbase, longitudinal vehicle speed, and stability factor; and calculating the first lateral acceleration observation value of the sensor based on the vehicle's yaw rate according to the vehicle's lateral dynamics model.

[0018] In the technical solution of this application embodiment, the vehicle's dynamic data includes at least the vehicle wheelbase, longitudinal speed, and stability factor. When calculating the first lateral acceleration observation value, the vehicle's yaw rate is first calculated using the aforementioned dynamic data. Based on this, the first lateral acceleration is calculated according to the lateral dynamics model, which improves the accuracy of the calculation results.

[0019] In some embodiments, based on the vehicle lateral dynamics model, the first lateral acceleration observation value of the sensor is calculated according to the vehicle yaw rate, including: the first lateral acceleration observation value of the sensor is calculated based on the vehicle yaw rate, the front axle lateral stiffness, the rear axle lateral stiffness, the center of gravity sideslip angle, the distance from the front axle to the center of gravity, the distance from the rear axle to the center of gravity, the front wheel steering angle, and the vehicle mass.

[0020] In the technical solution of this application embodiment, when calculating the first lateral acceleration observation value, the yaw rate is first calculated with the help of relevant dynamic data. Then, based on the yaw rate and data such as the vehicle's front axle lateral stiffness, rear axle lateral stiffness, center of gravity sideslip angle, distance from the front axle to the center of gravity, distance from the rear axle to the center of gravity, front wheel steering angle, and mass, the calculation is performed based on the lateral dynamics model, which improves the accuracy of the calculation results.

[0021] In some embodiments, the sensor signal includes a lateral acceleration signal; based on the vehicle's motion data, a second signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, center of gravity sideslip angle, front wheel rotation and wheelbase, the second lateral acceleration observation value of the sensor is calculated.

[0022] In the technical solution of this application embodiment, the vehicle's motion data includes longitudinal vehicle speed, center of gravity sideslip angle, front wheel rotation, and wheelbase. The second lateral acceleration observation value is calculated based on the vehicle's longitudinal vehicle speed, center of gravity sideslip angle, front wheel rotation, and wheelbase, thereby improving the accuracy of the calculation results and reducing the complexity of the calculation.

[0023] In some embodiments, the sensor signal includes a lateral acceleration signal; based on the vehicle's rotation data, a third signal observation value of the sensor is calculated, including: based on the vehicle's body roll angle and body roll gradient, a third lateral acceleration observation value of the sensor is calculated.

[0024] In the technical solution of this application embodiment, the vehicle rotation data includes the vehicle body roll angle and the vehicle body roll gradient. The third lateral acceleration observation value is calculated based on the vehicle body roll angle and the vehicle body roll gradient, which reduces the complexity of the calculation and improves the accuracy of the calculation results.

[0025] In some embodiments, the sensor signal includes a yaw rate signal; based on the vehicle's dynamic data, a first signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, wheelbase, and stability factor, the first yaw rate observation value of the sensor is calculated.

[0026] In the technical solution of this application embodiment, the vehicle's power data includes longitudinal vehicle speed, wheelbase, and stability factor. The first yaw rate observation value is calculated based on the longitudinal vehicle speed, wheelbase, and stability factor, which reduces the computational complexity and improves the accuracy of the calculation results.

[0027] In some embodiments, the sensor signal includes a yaw rate signal; based on the vehicle's motion data, a second signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, front wheel steering angle, and wheelbase, the second yaw rate observation value of the sensor is calculated.

[0028] In the technical solution of this application embodiment, the vehicle's motion data includes longitudinal vehicle speed, front wheel steering angle, and wheelbase. The second yaw rate observation value is calculated based on the longitudinal vehicle speed, front wheel steering angle, and wheelbase, which reduces the computational complexity and improves the accuracy of the calculation results.

[0029] In some embodiments, the sensor signal includes a yaw rate signal; based on the vehicle's rotation data, a third signal observation value of the sensor is calculated, including: based on the vehicle's body pitch angle, body pitch gradient, longitudinal vehicle speed, and center of gravity sideslip angle, the third yaw rate observation value of the sensor is calculated.

[0030] In the technical solution of this application embodiment, the vehicle rotation data includes vehicle pitch angle, vehicle pitch gradient, longitudinal vehicle speed and center of gravity sideslip angle. The third yaw rate observation value is calculated based on the vehicle pitch angle, vehicle pitch gradient, longitudinal vehicle speed and center of gravity sideslip angle, which reduces the computational complexity and improves the accuracy of the calculation results.

[0031] In the technical solution of this application embodiment, the longitudinal acceleration signal, the lateral acceleration signal, and the yaw rate signal can be used for fault calculation separately. The three signals are decoupled to avoid mutual interference between the signals and affect the accuracy of fault diagnosis.

[0032] In some embodiments, obtaining a sensor signal fault result based on a first fault calibration result, a second fault calibration result, and a third fault calibration result includes: determining that the sensor signal has a fault if at least two of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicate a signal fault.

[0033] In the technical solution of this application embodiment, after obtaining the first fault calibration result, the second fault calibration result, and the third fault calibration result, the sensor fault is finally determined through a "three-party voting" mechanism, thereby improving the accuracy of fault diagnosis.

[0034] In some embodiments, the method further includes: if at most one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates a signal fault, exiting fault diagnosis after a preset time period.

[0035] In some embodiments, when the first fault calibration result, the second fault calibration result, and the third fault calibration result all indicate that the signal is not faulty, the preset time period is the first time period; when one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates that the signal is faulty, the preset time period is the second time period, wherein the second time period is longer than the first time period.

[0036] In the technical solution of this application embodiment, when at most one of the three fault calibration results indicates a signal fault, it is necessary to exit fault diagnosis. To avoid exiting fault diagnosis too early, it can be exited after a preset time period has elapsed since at most one of the three fault calibration results indicates a signal fault. The preset time period length can be set according to the number of the three fault calibration results. The larger the number, the higher the probability that a fault still exists. To avoid premature exit from the fault diagnosis mechanism due to misjudgment, the larger the number, the longer the preset time period, so as to leave enough buffer time.

[0037] In some embodiments, the method further includes determining that the sensor signal is faulty when the absolute value of the sensor's signal output value is greater than a road surface threshold for the signal.

[0038] The first fault calibration result, the second fault calibration result, and the third fault calibration result in this application are independent calibration results, which are calibration results obtained from independent events.

[0039] In the technical solution of this application embodiment, based on the actual situation, when the absolute value of the sensor's signal output value is greater than the road surface threshold of the signal, it is directly determined that the sensor signal has a fault, thus improving the fault diagnosis speed. When the absolute value of the sensor's signal output value is less than or equal to the road surface threshold of the signal, it is necessary to further determine whether a fault exists, thereby improving accuracy. Further determining whether a fault exists includes calculating a first fault calibration result, a second fault calibration result, and a third fault calibration result, and performing a three-way vote to determine whether a fault exists.

[0040] On the other hand, this application provides a fault diagnosis device for a vehicle sensor, the device comprising: a first determining module, configured to determine a first fault calibration result between the sensor's observed signal value and the sensor's output signal value based on the vehicle's power data; a second determining module, configured to determine a second fault calibration result between the sensor's observed signal value and the sensor's output signal value based on the vehicle's motion data; a third determining module, configured to determine a third fault calibration result between the sensor's observed signal value and the sensor's output signal value based on the vehicle's rotation data; and an obtaining module, configured to obtain a sensor signal fault result based on the first fault calibration result, the second fault calibration result, and the third fault calibration result.

[0041] On the other hand, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method of any of the above embodiments.

[0042] On the other hand, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0043] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0045] Figure 1 A schematic diagram of a fault diagnosis method is shown;

[0046] Figure 2 A flowchart of a vehicle sensor fault diagnosis method according to an embodiment of this application is shown;

[0047] Figure 3 A schematic diagram of the longitudinal acceleration diagnostic process according to an embodiment of this application is shown;

[0048] Figure 4 A schematic diagram of the lateral acceleration diagnostic process according to an embodiment of this application is shown;

[0049] Figure 5 A schematic diagram of the yaw rate diagnostic process according to an embodiment of this application is shown;

[0050] Figure 6 A block diagram of a vehicle sensor fault diagnosis device according to an embodiment of this application is shown. Detailed Implementation

[0051] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0053] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0054] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0055] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0056] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0057] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0058] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0059] With the development of science and technology and the rapid increase in the number of vehicles, enhancing the safety of vehicle systems has become particularly important. Fault diagnosis of vehicle sensors has received increasing attention in practical applications. To diagnose vehicle sensor faults, it is typically necessary to use relevant sensors to obtain vehicle state parameters (such as yaw and lateral motion) and employ relevant dynamic models to achieve the purpose of fault diagnosis.

[0060] In some examples, sensor fault diagnosis methods heavily rely on a two-degree-of-freedom model of vehicle handling and stability, neglecting the coupling effects between signals (such as lateral acceleration and yaw rate), focusing only on vehicle motion characteristics within the linear region, and failing to consider the influence of nonlinear characteristics (such as crosswinds, side slopes, etc.) and external disturbances, thus affecting the fault diagnosis results. Furthermore, fault diagnosis is often performed solely through dynamics or kinematics, resulting in low accuracy.

[0061] In view of this, this application provides a fault diagnosis method for vehicle sensors. This method, based on vehicle power data, motion data, and rotation data, determines the fault calibration result between the sensor's observed signal value and the sensor's output signal value. The fault diagnosis is then performed using a "three-way voting" mechanism based on these three fault calibration results, ultimately yielding the sensor's signal fault result. This significantly improves the accuracy of fault diagnosis and increases the speed of fault exit. Furthermore, this method can support dynamic control systems such as vehicle stability control and torque vector control, enabling timely and accurate diagnosis of inertial measurement unit (IMU) faults, preventing control system malfunctions; it also allows the aforementioned dynamic control systems to quickly restore control after fault elimination, ensuring vehicle handling stability. The inertial measurement unit (IMU) includes the sensor described herein.

[0062] This application takes into account the influence of nonlinear environment, and greatly improves the accuracy of fault diagnosis by considering factors such as the longitudinal slope of the road where the vehicle is traveling, air resistance coefficient, vehicle frontal area, longitudinal speed, road rolling resistance coefficient, and rotational mass conversion factor.

[0063] Figure 1 A schematic diagram of a fault diagnosis method is shown.

[0064] like Figure 1As shown, this method acquires vehicle state parameters using relevant sensors and then uses a Kalman filter algorithm and direct integration method to fuse and estimate the vehicle's center of gravity sideslip angle. Based on the fusion estimation result of the center of gravity sideslip angle, a sensor redundancy observer is established in conjunction with the vehicle state parameters, ultimately establishing a fault diagnosis and fault diagnosis compensation mechanism for different vehicle driving states. This scheme, relying on a two-degree-of-freedom model, can only analyze yaw rate and lateral acceleration sensor faults under ideal vehicle conditions. It cannot be applied to nonlinear environments or environments with external interference (such as vehicles encountering crosswinds or side slopes). Moreover, the diagnosis of lateral acceleration and yaw rate uses coupling methods (e.g., both rely on signal β), which means that a misdiagnosis of one signal will seriously affect the diagnostic effect of the other signal.

[0065] Figure 2 A flowchart of a vehicle sensor fault diagnosis method according to an embodiment of this application is shown.

[0066] like Figure 2 As shown, the vehicle sensor fault diagnosis method 200 provided in this application includes steps S210 to S240.

[0067] Step S210: Based on the vehicle's power data, determine the first fault calibration result between the sensor's signal observation value and the sensor's signal output value.

[0068] For example, the vehicle's power data is obtained, and the sensor's signal observation value is calculated based on the power data. The signal observation value is an estimated value, and the sensor's signal output value is collected. The signal output value is the true value. The first fault calibration result is obtained by comparing and analyzing the signal observation value and the signal output value.

[0069] Step S220: Based on the vehicle's motion data, determine a second fault calibration result between the sensor's signal observation value and the sensor's signal output value.

[0070] For example, vehicle motion data is obtained, and sensor signal observation values ​​are calculated based on the motion data. The signal observation values ​​are estimated values, and sensor signal output values ​​are collected. The signal output values ​​are real values. A second fault calibration result is obtained by comparing and analyzing the signal observation values ​​and the signal output values.

[0071] Step S230: Based on the vehicle's rotation data, determine a third fault calibration result between the sensor's observed signal value and the sensor's output signal value.

[0072] For example, vehicle rotation data is obtained, and sensor signal observation values ​​are calculated based on the rotation data. These observation values ​​are estimated values, and sensor signal output values ​​are collected. These output values ​​are the true values. A third fault calibration result is obtained by comparing and analyzing the observation values ​​and the output values. Rotation data, for example, represents the vehicle's suspension or steering characteristics.

[0073] Step S240: Based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, the signal fault result of the sensor is obtained.

[0074] For example, based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, a "three-party voting" mechanism is used for fault diagnosis to finally obtain the sensor signal fault result. In this application, the first fault calibration result, the second fault calibration result, and the third fault calibration result are independent calibration results, belonging to calibration results obtained from independent events.

[0075] In the technical solution of this application embodiment, an engineering approach is adopted from three aspects: vehicle kinematics, dynamics, and suspension or steering characteristics. The fault calibration result is determined based on sensor observations and signal output values. This three-pronged approach to analyzing the fault calibration result decouples the three signals, avoids mutual interference, greatly improves the accuracy of fault diagnosis, and increases the persuasiveness and reliability of the results.

[0076] In another example, the sensor signals include longitudinal acceleration, lateral acceleration, and yaw rate. Fault diagnosis can be performed separately for each of the longitudinal acceleration, lateral acceleration, and yaw rate signals. The procedure for fault diagnosis for each signal can be as follows: Figure 2 As shown in the example.

[0077] In the technical solution of this application embodiment, fault diagnosis is performed on the longitudinal acceleration signal, lateral acceleration signal, and yaw rate signal separately. During the separate fault diagnosis, an engineering approach is adopted from three aspects: vehicle kinematics, dynamics, and suspension or steering characteristics. The fault calibration result is determined based on the sensor observations and signal output values. Performing fault diagnosis on different signals separately decouples the three signals, avoids mutual interference between them, greatly improves the accuracy of fault diagnosis, and increases the persuasiveness and reliability of the results.

[0078] In one example, the sensor's signal includes a longitudinal acceleration signal.

[0079] Figure 3 A schematic diagram of the longitudinal acceleration diagnostic process according to an embodiment of this application is shown.

[0080] like Figure 3 As shown, when diagnosing longitudinal acceleration faults, the road adhesion coefficient is first obtained, and the maximum longitudinal acceleration that the vehicle can achieve under the current road conditions is calculated. The maximum longitudinal acceleration is a. x,mue =μg, the maximum longitudinal acceleration is compared with the sensor's signal output value (original signal a) X,raw The comparison is performed when the absolute value of the sensor's signal output is greater than the road surface threshold (the road surface threshold is, for example, the maximum longitudinal acceleration a). x,mue In the case of |a), it is determined that the sensor signal is faulty. That is, if |a x,raw |>a x,mue If the absolute value of the sensor's signal output is less than or equal to the road surface threshold, it initially indicates that the longitudinal acceleration signal is normal, and further fault diagnosis procedures are required.

[0081] In the technical solution of this application embodiment, based on the actual situation, when the absolute value of the sensor's signal output value is greater than the road surface threshold, it is directly determined that the sensor signal is faulty, thus improving the fault diagnosis speed. When the absolute value of the sensor's signal output value is less than or equal to the road surface threshold, it is necessary to further determine whether a fault exists, thereby improving accuracy.

[0082] In the subsequent diagnostic process, it is necessary to determine the first fault calibration result, the second fault calibration result, and the third fault calibration result for the longitudinal acceleration signal.

[0083] Regarding the first fault calibration result, based on the vehicle's power data, the first fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's first observed signal value based on the vehicle's power data; obtaining a first estimated residual value based on the sensor's first observed signal value and the sensor's output signal value; and obtaining the first fault calibration result based on the first estimated residual value and a first residual value threshold. The specific process is described below.

[0084] First, based on the vehicle's power data, the first signal observation value of the sensor is calculated, including: based on the vehicle's total motor torque, total transmission ratio, transmission efficiency of the transmission system, wheel rolling radius, vehicle mass, longitudinal slope of the road on which the vehicle is traveling, air resistance coefficient, vehicle frontal area, longitudinal vehicle speed, road rolling resistance coefficient, and rotational mass conversion coefficient, the first longitudinal acceleration observation value of the sensor is calculated.

[0085] For example, vehicle dynamic data includes total motor torque, total transmission ratio, transmission efficiency of the transmission system, etc. Other types of vehicle data may include wheel rolling radius, vehicle mass, longitudinal slope of the road on which the vehicle is traveling, air resistance coefficient, vehicle frontal area, longitudinal speed, road rolling resistance coefficient, and rotational mass conversion factor. Based on the longitudinal dynamic equation, the first sensor signal observation value is calculated, and the acceleration observation value a is calculated according to the following formula (1). x,1 :

[0086]

[0087] Among them, T mot Let R be the total torque of the motor, i be the total transmission ratio, η be the transmission efficiency of the transmission system, and R be the total torque of the motor. w Let m be the wheel rolling radius, m be the vehicle mass, θ be the road longitudinal slope, and C be the radius of rotation. D Here, A is the air resistance coefficient, u is the longitudinal vehicle speed, f is the road rolling resistance coefficient, and δ is the frontal area. m This is the rotational mass conversion factor.

[0088] The first longitudinal acceleration observation can be calculated by combining vehicle dynamics data and other types of data, thereby improving the accuracy of the calculation.

[0089] Then, based on the first signal observation value and the signal output value of the sensor, the first estimated residual value is obtained.

[0090] Next, based on the first estimated residual value and the first residual value threshold, the first fault calibration result is obtained.

[0091] For example, a first estimated residual value is calculated based on the first signal observation value and the signal output value. The first estimated residual value is then compared with the calibration residual value threshold (first residual value threshold) to obtain the first fault calibration result.

[0092] For example, based on the acceleration observation value a x,1 Compared with the original value of sensor acceleration a x,raw The first estimated residual value ρ is calculated. x,1 The calculation formula (2) is as follows, and the calculation result can also be subjected to first-order low-pass filtering:

[0093] ρ x,1 =|a x,1 |-|a x,raw | (2)

[0094] Based on engineering experience, the first residual threshold is ρ. xthd,1 The first fault calibration result (flag) is obtained through comparison. x1 As shown in formula (3):

[0095]

[0096] Regarding the second fault calibration result, based on the vehicle's motion data, the second fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's second observed signal value based on the vehicle's motion data; obtaining a second estimated residual value based on the sensor's second observed signal value and the sensor's output signal value; and obtaining the second fault calibration result based on the second estimated residual value and the second residual value threshold. The specific process is described below.

[0097] First, based on the vehicle's motion data, the second signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, the second longitudinal acceleration observation value of the sensor is calculated.

[0098] For example, the vehicle's motion data includes longitudinal vehicle speed. Based on the kinematic equations, the second sensor signal observation is calculated, and the acceleration observation a is calculated according to the following formula (4). x,2 :

[0099]

[0100] Where u(n) is the longitudinal vehicle speed in the current cycle ( Figure 3 The figure shows the wheel speed, from which the longitudinal speed can be obtained, t m The scheduling period is, for example, the time interval between discrete data points when u(n) includes discrete data points. After calculation, the calculation result can also be subjected to first-order low-pass filtering.

[0101] In the technical solution of this application embodiment, the vehicle motion data includes longitudinal vehicle speed, and a second longitudinal acceleration observation value is calculated based on the longitudinal vehicle speed, thereby reducing computational complexity and improving the accuracy of the calculation results.

[0102] Then, based on the second signal observation value and the signal output value of the sensor, the second estimated residual value is obtained.

[0103] Next, based on the second estimated residual value and the second residual value threshold, the second fault calibration result is obtained.

[0104] For example, a second estimated residual value is calculated based on the second signal observation value and the signal output value. The second estimated residual value is then compared with the calibration residual value threshold (second residual value threshold) to obtain the second fault calibration result.

[0105] For example, based on the acceleration observation value a x,2 Compared with the original value of sensor acceleration a x,raw The second estimated residual value ρ is calculated. x,2The calculation formula (5) is as follows:

[0106] ρ x,2 =|a x,2 |-|a x,raw | (5)

[0107] Based on engineering experience, the second residual threshold is ρ. xthd,2， The second fault calibration result flag is obtained through comparison. x2 As shown in formula (6):

[0108]

[0109] Regarding the third fault calibration result, based on the vehicle's rotation data, the third fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's third signal observed value based on the vehicle's rotation data; obtaining the third estimated residual value based on the sensor's third signal observed value and the sensor's output signal value; and obtaining the third fault calibration result based on the third estimated residual value and the third residual value threshold. The specific process is described below.

[0110] First, based on the vehicle's rotation data, the third signal observation value of the sensor is calculated, including: based on the vehicle's body pitch angle and body pitch gradient, the third longitudinal acceleration observation value of the sensor is calculated.

[0111] For example, the vehicle's rotation data includes vehicle pitch (suspension pitch) data, specifically including vehicle pitch angle and vehicle pitch gradient. Based on the rotation equation, the sensor's third signal observation value is calculated, and the acceleration observation value a is calculated according to the following formula (7). x,3 :

[0112]

[0113] Where, θ pltch κ is the vehicle body pitch angle. pitch The vehicle body pitch gradient is obtained from the calibration of the actual vehicle.

[0114] The vehicle's rotation data includes the vehicle pitch angle and the vehicle pitch gradient. The third longitudinal acceleration observation is calculated based on the vehicle pitch angle and the vehicle pitch gradient, which reduces the computational complexity and improves the accuracy of the calculation results.

[0115] Then, based on the third signal observation value and the signal output value of the sensor, the third estimated residual value is obtained.

[0116] Next, based on the third estimated residual value and the third residual value threshold, the third fault calibration result is obtained.

[0117] For example, based on the third signal observation value and the signal output value, the third estimated residual value is calculated, and the third estimated residual value is compared with the calibration residual value threshold (the third residual value threshold) to obtain the fault calibration result.

[0118] For example, based on the acceleration observation value a x,3 Compared with the original value of sensor acceleration a x,raw The third estimated residual value ρ was calculated. x,3 The calculation formula (8) is as follows, and the calculation result can also be subjected to first-order low-pass filtering: ρ x,3 =|a x,3 |-|a x,raw |(8)

[0119] Based on engineering experience, the threshold for the third residual value is ρ. xthd,3， The third fault calibration result flag is obtained through comparison. x3 As shown in formula (9):

[0120]

[0121] In the technical solution of this application embodiment, residual values ​​are calculated based on parameter information such as sensor power data, motion data, and rotation data, combined with the sensor output values. The results are then compared with threshold values ​​to determine the fault calibration result. This method simplifies the calculation process, classifies and analyzes the data, improves processing efficiency, and makes it easier to manage.

[0122] Based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, a signal fault result of the sensor is obtained, including: if at least two of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicate a signal fault, it is determined that the sensor signal has a fault. If at most one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates a signal fault, the fault diagnosis is terminated after a preset time period.

[0123] For example, based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, a three-way vote is conducted to determine the fault state of the longitudinal acceleration signal as shown in formula (10):

[0124]

[0125] When two or three fault calibration results indicate a fault, the longitudinal acceleration signal of the sensor is considered faulty. When no fault calibration results or only one fault result indicates a fault, the longitudinal acceleration signal is considered normal. This is the longitudinal acceleration fault status flag. x When true, it indicates a fault in the IMU longitudinal acceleration signal; flagx A false result indicates that the IMU longitudinal acceleration signal is normal. When a fault occurs in the longitudinal acceleration signal, fault monitoring can continue until at most one fault calibration result indicates a fault in the longitudinal acceleration signal. At this point, the longitudinal acceleration fault is cleared, and fault diagnosis exits after a set time period.

[0126] The aforementioned preset time period refers to the following: when all three fault calibration results (first, second, and third) indicate that the signal is not faulty, the preset time period is the first time period; when one of the three fault calibration results indicates that the signal is faulty, the preset time period is the second time period, wherein the second time period is longer than the first time period.

[0127] For example, after the longitudinal acceleration fault is cleared, the delay time constant t is determined based on the number of fault marker locations. c The fault exit strategy is as shown in formula (11):

[0128]

[0129] Among them, t d t is the preset fault exit delay time constant. c This is a preset time period.

[0130] When the number of fault flag bits is 1, the preset time period is the second time period, lasting t. c =t d After a certain time, the longitudinal acceleration fault diagnosis process exits, and the longitudinal acceleration signal returns to normal. When the number of fault flag bits is 0, the preset time period is the first time period, lasting t. c =0.5t d After a certain time, the longitudinal acceleration fault diagnosis process is exited, and the longitudinal acceleration signal returns to normal. In formula (11), the second time period is longer than the first time period, and the coefficient of the first time period (e.g., coefficient 0.5) is any positive number less than 1.

[0131] In the technical solution of this application embodiment, when at most one of the three fault calibration results indicates a signal fault, it is necessary to exit fault diagnosis. To avoid exiting fault diagnosis too early, it can be exited after a preset time period has elapsed since at most one of the three fault calibration results indicates a signal fault. The preset time period length can be set according to the number of the three fault calibration results. The larger the number, the higher the probability that a fault still exists. To avoid premature exit from the fault diagnosis mechanism due to misjudgment, the larger the number, the longer the preset time period, so as to leave enough buffer time.

[0132] In one example, the sensor's signal includes a lateral acceleration signal.

[0133] Figure 4 A schematic diagram of the lateral acceleration diagnostic process according to an embodiment of this application is shown.

[0134] like Figure 4 As shown, when diagnosing lateral acceleration faults, the road adhesion coefficient is first obtained, and the maximum lateral acceleration that the vehicle can achieve under the current road conditions is calculated. The maximum lateral acceleration is a. y,mue =μg, the maximum lateral acceleration is compared with the sensor's signal output value (original signal a). y,raw The comparison is performed when the absolute value of the sensor's signal output is greater than the road surface threshold (the road surface threshold is, for example, the maximum lateral acceleration a). y,mue In the case of |a), it is determined that the sensor signal is faulty. That is, if |a y,raw |>a y,mue If the absolute value of the sensor's signal output is less than or equal to the road surface threshold, it initially indicates that the lateral acceleration signal is normal, and further fault diagnosis procedures are required.

[0135] In the technical solution of this application embodiment, based on the actual situation, when the absolute value of the sensor's signal output value is greater than the road surface threshold, it is directly determined that the sensor signal is faulty, thus improving the fault diagnosis speed. When the absolute value of the sensor's signal output value is less than or equal to the road surface threshold, it is necessary to further determine whether a fault exists, thereby improving accuracy.

[0136] In the subsequent diagnostic process, it is necessary to determine the first fault calibration result, the second fault calibration result, and the third fault calibration result for the lateral acceleration signal. Regarding the first fault calibration result, based on the vehicle's dynamic data, the first fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's first observed signal value based on the vehicle's dynamic data; obtaining the first estimated residual value based on the sensor's first observed signal value and the sensor's output signal value; and obtaining the first fault calibration result based on the first estimated residual value and the first residual value threshold. The specific process is described below.

[0137] First, based on the vehicle's dynamic data, the first signal observation value of the sensor is calculated, including: calculating the vehicle's yaw rate based on the vehicle's wheelbase, longitudinal speed, and stability factor; and calculating the first lateral acceleration observation value of the sensor based on the vehicle's yaw rate according to the vehicle's lateral dynamics model.

[0138] In the technical solution of this application embodiment, the vehicle's dynamic data includes at least the vehicle wheelbase, longitudinal speed, and stability factor. When calculating the first lateral acceleration observation value, the vehicle's yaw rate is first calculated using the aforementioned dynamic data. Based on this, the first lateral acceleration is calculated according to the lateral dynamics model, which improves the accuracy of the calculation results.

[0139] Specifically, based on the vehicle lateral dynamics model, the first lateral acceleration observation value of the sensor is calculated according to the vehicle yaw rate, including: the vehicle yaw rate, the front axle lateral stiffness, the rear axle lateral stiffness, the center of gravity lateral slip angle, the distance from the front axle to the center of gravity, the distance from the rear axle to the center of gravity, the front wheel rotation angle, and the vehicle mass.

[0140] In the technical solution of this application embodiment, when calculating the first lateral acceleration observation value, the yaw rate is first calculated with the help of relevant dynamic data. Then, based on the yaw rate and data such as the vehicle's front axle lateral stiffness, rear axle lateral stiffness, center of gravity sideslip angle, distance from the front axle to the center of gravity, distance from the rear axle to the center of gravity, front wheel steering angle, and mass, the calculation is performed based on the lateral dynamics model, which improves the accuracy of the calculation results.

[0141] For example, the vehicle data acquired by the sensor is used to calculate the first sensor signal observation value based on the two-degree-of-freedom maneuvering model, and the acceleration observation value a is calculated according to the following formula (12). y,1 :

[0142]

[0143] Where k1 is the front axle lateral stiffness, k2 is the rear axle lateral stiffness, β is the center-of-gravity sideslip angle, a is the distance from the front axle to the center of gravity, b is the distance from the rear axle to the center of gravity, L is the wheelbase, K is the stability factor, u is the longitudinal vehicle speed, δ is the front wheel steering angle, and m is the vehicle mass. K, the stability factor, is obtained from low-speed circular tests in engineering.

[0144] For the calculation process of formula (12), the steady-state two-degree-of-freedom model can be used to calculate the vehicle yaw rate L(1+Ku) 2 Then, the vehicle yaw rate is substituted into the vehicle lateral dynamics formula (the lateral dynamics formula is, for example, part of the two-degree-of-freedom maneuvering model, and the lateral dynamics formula is the whole in formula (12).

[0145] To improve diagnostic accuracy in the nonlinear region of vehicle motion, the lateral stiffness is improved by introducing the influence of wheel load variation on the lateral stiffness, so that the relationship between lateral force and tire slip angle is more consistent with the actual vehicle, as shown in formulas (13) and (14):

[0146]

[0147] Where C1 and C2 are the coefficients in the tire lateral stiffness fitting multivariate, obtained through tire testing. ld For four-wheel load (subscript RR indicates the right rear wheel, RL indicates the left rear wheel, FL indicates the left front wheel, and FR indicates the right front wheel).

[0148] In the technical solution of this application embodiment, when calculating the first lateral acceleration observation value, the vehicle yaw rate is first calculated using the aforementioned dynamic data. Based on this, the first lateral acceleration is calculated according to the lateral dynamics model, improving the accuracy of the calculation results. Next, based on the yaw rate and data such as the vehicle's front axle lateral stiffness, rear axle lateral stiffness, center of gravity sideslip angle, distance from the front axle to the center of gravity, distance from the rear axle to the center of gravity, front wheel steering angle, and mass, calculations are performed according to the lateral dynamics model, further improving the accuracy of the calculation results.

[0149] Then, based on the first signal observation value and the signal output value of the sensor, the first estimated residual value is obtained.

[0150] Next, based on the first estimated residual value and the first residual value threshold, the first fault calibration result is obtained.

[0151] For example, a first estimated residual value is calculated based on the first signal observation value and the signal output value, and then compared with the calibration residual value threshold (first residual value threshold) to obtain a first fault calibration result.

[0152] For example, based on the acceleration observation value a y,1 Compared with the original value of sensor acceleration a y,raw The first estimated residual value ρ is calculated. y,1 The calculation formula (15) is as follows:

[0153] ρ y,1 =|a y,1 |-|a y,raw | (15)

[0154] Based on engineering experience, the first residual threshold is ρ. ythd,1， The first fault calibration result (flag) is obtained through comparison. y1 As shown in formula (16):

[0155]

[0156] Regarding the second fault calibration result, based on the vehicle's motion data, the second fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's second observed signal value based on the vehicle's motion data; obtaining the second estimated residual value based on the sensor's second observed signal value and the sensor's output signal value; and obtaining the second fault calibration result based on the second estimated residual value and the second residual value threshold. The specific process is described below.

[0157] First, based on the vehicle's motion data, the second signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, center of gravity sideslip angle, front wheel rotation and wheelbase, the second lateral acceleration observation value of the sensor is calculated.

[0158] For example, the vehicle data acquired by the sensor is used to calculate the second sensor signal observation value based on the kinematic equation (Ackermann's kinematic principle), and the acceleration observation value a is calculated according to the following formula (17). y,2 :

[0159]

[0160] Where u is the longitudinal vehicle speed, β is the sideslip angle, δ is the front wheel steering angle, L is the wheelbase, and the superscript "." indicates the derivative.

[0161] In the technical solution of this application embodiment, the vehicle's motion data includes longitudinal vehicle speed, center of gravity sideslip angle, front wheel rotation, and wheelbase. The second lateral acceleration observation value is calculated based on the vehicle's longitudinal vehicle speed, center of gravity sideslip angle, front wheel rotation, and wheelbase, thereby improving the accuracy of the calculation results and reducing the complexity of the calculation.

[0162] Then, based on the second signal observation value and the signal output value of the sensor, the second estimated residual value is obtained.

[0163] Next, based on the second estimated residual value and the second residual value threshold, the second fault calibration result is obtained.

[0164] For example, based on the second signal observation value and the signal output value, a second estimated residual value is calculated, and it is compared with the calibration residual value threshold (second residual value threshold) to obtain a second fault calibration result.

[0165] For example, based on the acceleration observation value a y,2 Compared with the original value of sensor acceleration a y,raw The second estimated residual value ρ is calculated. y,2 The calculation formula (18) is as follows, and the calculation result can also be subjected to first-order low-pass filtering:

[0166] ρ y,2 =|a y,2 |-|ay,raw | (18)

[0167] Based on engineering experience, the second residual threshold is ρ. ythd,2， The second fault calibration result flag is obtained through comparison. y2 As shown in formula (19):

[0168]

[0169] Regarding the third fault calibration result, based on the vehicle's rotation data, the third fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's third signal observed value based on the vehicle's rotation data; obtaining the third estimated residual value based on the sensor's third signal observed value and the sensor's output signal value; and obtaining the third fault calibration result based on the third estimated residual value and the third residual value threshold. The specific process is described below.

[0170] First, based on the vehicle's rotation data, the third signal observation value of the sensor is calculated, including: based on the vehicle's body roll angle and body roll gradient, the third lateral acceleration observation value of the sensor is calculated.

[0171] For example, the vehicle's rotation data includes vehicle roll (steering characteristics) data, specifically including vehicle roll angle and vehicle roll gradient. Based on the rotation equation, the sensor's third signal observation value is calculated, and the acceleration observation value a is calculated according to the following formula (20). y,3 :

[0172]

[0173] Where, θ roll κ is the body roll angle. roll The vehicle body roll gradient is obtained from actual vehicle calibration.

[0174] In the technical solution of this application embodiment, the vehicle rotation data includes the vehicle body roll angle and the vehicle body roll gradient. The third lateral acceleration observation value is calculated based on the vehicle body roll angle and the vehicle body roll gradient, which reduces the complexity of the calculation and improves the accuracy of the calculation results.

[0175] Then, based on the third signal observation value and the signal output value of the sensor, the third estimated residual value is obtained.

[0176] Next, based on the third estimated residual value and the third residual value threshold, the third fault calibration result is obtained.

[0177] For example, based on the third signal observation value and the signal output value, the third estimated residual value is calculated, and the third estimated residual value is compared with the calibration residual value threshold (the third residual value threshold) to obtain the third fault calibration result.

[0178] For example, based on the acceleration observation value a y,3 Compared with the original value of sensor acceleration a y,raw The estimated residual value ρ is calculated. y,3 The calculation formula (21) is as follows, and the calculation result can also be subjected to first-order low-pass filtering:

[0179] ρ y,3 =|a y,3 |-|a y,raw |(21)

[0180] Based on engineering experience, the threshold for the third residual value is ρ. ythd,3， The third fault calibration result flag is obtained through comparison. y3 As shown in formula (22):

[0181]

[0182] In the technical solution of this application embodiment, residual values ​​are calculated based on parameter information such as sensor power data, motion data, and rotation data, combined with the sensor output values. The results are then compared with threshold values ​​to determine the fault calibration result. This method simplifies the calculation process, classifies and analyzes the data, improves processing efficiency, and makes it easier to manage.

[0183] Based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, a signal fault result of the sensor is obtained, including: if at least two of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicate a signal fault, it is determined that the sensor signal has a fault. If at most one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates a signal fault, the fault diagnosis is terminated after a preset time period.

[0184] For example, based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, a three-way vote is conducted to determine the fault state of the lateral acceleration signal as shown in formula (23):

[0185]

[0186] When two or three fault calibration results indicate a fault, the sensor's lateral acceleration signal is considered faulty. When no fault calibration results or only one fault result indicates a fault, the lateral acceleration signal is considered normal. This is the lateral acceleration fault status flag.y When true, it indicates a fault in the IMU lateral acceleration signal; flag y A false result indicates that the IMU lateral acceleration signal is normal. When a lateral acceleration signal malfunctions, fault monitoring can continue until at most one fault calibration result indicates a lateral acceleration signal malfunction. At this point, the lateral acceleration fault is cleared, and fault diagnosis exits after a set time period.

[0187] The aforementioned preset time period refers to the following: when all three fault calibration results (first, second, and third) indicate that the signal is not faulty, the preset time period is the first time period; when one of the three fault calibration results indicates that the signal is faulty, the preset time period is the second time period, wherein the second time period is longer than the first time period.

[0188] For example, after the lateral acceleration fault is cleared, the delay time constant t is determined based on the number of fault marker locations. c The fault exit strategy is as shown in formula (24):

[0189]

[0190] Among them, t d t is the preset fault exit delay time constant. c This is a preset time period.

[0191] When the number of fault flag bits is 1, the preset time period is the second time period, lasting t. c =t d After a certain time, the lateral acceleration fault diagnosis process exits, and the lateral acceleration signal returns to normal. When the number of fault flag bits is 0, the preset time period is the first time period, lasting t. c =0.5t d After a certain time, the diagnostic process for lateral acceleration faults resumes, and the lateral acceleration signal returns to normal. In formula (11), the second time period is longer than the first time period, and the coefficient of the first time period (e.g., coefficient 0.5) is any positive number less than 1. The preset time period corresponding to the lateral acceleration signal can be different from the preset time period corresponding to the longitudinal acceleration signal.

[0192] In the technical solution of this application embodiment, when at most one of the three fault calibration results indicates a signal fault, it is necessary to exit fault diagnosis. To avoid exiting fault diagnosis too early, it can be exited after a preset time period has elapsed since at most one of the three fault calibration results indicates a signal fault. The preset time period length can be set according to the number of the three fault calibration results. The larger the number, the higher the probability that a fault still exists. To avoid premature exit from the fault diagnosis mechanism due to misjudgment, the larger the number, the longer the preset time period, so as to leave enough buffer time.

[0193] In another example, the sensor signal includes a yaw rate signal.

[0194] Figure 5 A schematic diagram of the yaw rate diagnostic process according to an embodiment of this application is shown.

[0195] like Figure 5 As shown, when diagnosing yaw rate faults, the road adhesion coefficient is first obtained, and the maximum yaw rate that the vehicle can achieve under the current road conditions is calculated. The maximum yaw rate is ω. r,mue =0.85μg, and compared with the sensor's signal output value (original signal ω) r,raw The comparison is performed when the absolute value of the sensor's signal output is greater than the road surface threshold (the road surface threshold is, for example, the maximum yaw rate ω). r,mue In the case of |ω), it is determined that the sensor signal is faulty. That is, if |ω r,raw |>ω r,mue If the absolute value of the sensor's signal output is less than or equal to the road surface threshold, it initially indicates that the yaw rate signal is normal, and further fault diagnosis procedures are required.

[0196] In the technical solution of this application embodiment, based on the actual situation, when the absolute value of the sensor's signal output value is greater than the road surface threshold, it is directly determined that the sensor signal is faulty, thus improving the fault diagnosis speed. When the absolute value of the sensor's signal output value is less than or equal to the road surface threshold, it is necessary to further determine whether a fault exists, thereby improving accuracy.

[0197] In the subsequent diagnostic process, it is necessary to determine the first fault calibration result, the second fault calibration result, and the third fault calibration result for the longitudinal acceleration signal.

[0198] Regarding the first fault calibration result, based on the vehicle's power data, the first fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's first observed signal value based on the vehicle's power data; obtaining a first estimated residual value based on the sensor's first observed signal value and the sensor's output signal value; and obtaining the first fault calibration result based on the first estimated residual value and the first residual value threshold. The specific process is described below.

[0199] First, based on the vehicle's dynamic data, the first signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, wheelbase, and stability factor, the first yaw rate observation value of the sensor is calculated.

[0200] For example, the vehicle data acquired by the sensor is used to calculate the first sensor signal observation value based on the two-degree-of-freedom model of handling and stability (which is also part of the two-degree-of-freedom model of maneuvering). The angular velocity observation value ω is then calculated according to the following formula (25). r,1 :

[0201]

[0202] Where K is the stability factor, obtained from low-speed circular tests in engineering. u is the longitudinal vehicle speed, and L is the wheelbase.

[0203] To improve diagnostic accuracy in the nonlinear region of vehicle motion, the lateral stiffness is improved by introducing the influence of wheel load variation on the lateral stiffness, so that the relationship between lateral force and tire slip angle is more consistent with the actual vehicle, as shown in formulas (26) and (27):

[0204]

[0205] Where C1 and C2 are the coefficients in the tire lateral stiffness fitting multivariate, obtained through tire testing. ld For four-wheel load (subscript RR indicates the right rear wheel, RL indicates the left rear wheel, FL indicates the left front wheel, and FR indicates the right front wheel).

[0206] In the technical solution of this application embodiment, the vehicle's power data includes longitudinal vehicle speed, wheelbase, and stability factor. The first yaw rate observation value is calculated based on the longitudinal vehicle speed, wheelbase, and stability factor, which reduces the computational complexity and improves the accuracy of the calculation results.

[0207] Then, based on the first signal observation value and the signal output value of the sensor, the first estimated residual value is obtained.

[0208] Next, based on the first estimated residual value and the first residual value threshold, the first fault calibration result is obtained.

[0209] For example, a first estimated residual value is calculated based on the first signal observation value and the signal output value. The first estimated residual value is then compared with the calibration residual value threshold (first residual value threshold) to obtain the first fault calibration result.

[0210] For example, based on the observed angular velocity ω r,1 Compared with the original value of sensor acceleration ω r,raw The first estimated residual value ρ is calculated. r,1 The calculation formula (28) is as follows, and the calculation result can also be subjected to first-order low-pass filtering:

[0211] ρ r,1 =|ω r,1 |-|ω r,raw | (28)

[0212] Based on engineering experience, the first residual threshold is ρ. rthd,1， The first fault calibration result (flag) is obtained through comparison. r1 As shown in formula (29):

[0213]

[0214] Regarding the second fault calibration result, based on the vehicle's motion data, the second fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's second observed signal value based on the vehicle's motion data; obtaining the second estimated residual value based on the sensor's second observed signal value and the sensor's output signal value; and obtaining the second fault calibration result based on the second estimated residual value and the second residual value threshold. The specific process is described below.

[0215] First, based on the vehicle's motion data, the second signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, front wheel steering angle and wheelbase, the second yaw rate observation value of the sensor is calculated.

[0216] For example, the second sensor signal observation value is calculated from the vehicle's motion data, including longitudinal vehicle speed, front wheel steering angle, and wheelbase, based on the kinematic equations (Ackermann principle), and the angular velocity observation value ω is calculated according to the following formula (30). r,2 :

[0217]

[0218] Where u is the longitudinal vehicle speed, δ is the front wheel steering angle, and L is the wheelbase.

[0219] In the technical solution of this application embodiment, the vehicle's motion data includes longitudinal vehicle speed, front wheel steering angle, and wheelbase. The second yaw rate observation value is calculated based on the longitudinal vehicle speed, front wheel steering angle, and wheelbase, which reduces the computational complexity and improves the accuracy of the calculation results.

[0220] Then, based on the second signal observation value and the signal output value of the sensor, the second estimated residual value is obtained.

[0221] Next, based on the second estimated residual value and the second residual value threshold, the second fault calibration result is obtained. For example, the second estimated residual value is calculated based on the second signal observation value and the signal output value, and the second estimated residual value is compared with the calibration residual value threshold (second residual value threshold) to obtain the second fault calibration result.

[0222] For example, based on the observed angular velocity ω r,2 Compared with the original value of sensor angular velocity ω r,raw The second estimated residual value ρ is calculated. r,2 The calculation formula (31) is as follows, and the calculation result can also be subjected to first-order low-pass filtering:

[0223] ρ r,2 =|ω r,2 |-|ω r,raw | (31)

[0224] Based on engineering experience, the second residual threshold is ρ. rthd,2， The second fault calibration result flag is obtained through comparison. r2 As shown in formula (32):

[0225]

[0226] Regarding the third fault calibration result, based on the vehicle's rotation data, the third fault calibration result between the sensor's observed signal value and the sensor's output signal value is determined, including: calculating the sensor's third signal observed value based on the vehicle's rotation data; obtaining the third estimated residual value based on the sensor's third signal observed value and the sensor's output signal value; and obtaining the third fault calibration result based on the third estimated residual value and the third residual value threshold. The specific process is described below.

[0227] First, based on the vehicle's rotation data, the third signal observation value of the sensor is calculated, including: based on the vehicle's body pitch angle, body pitch gradient, longitudinal vehicle speed and center of gravity sideslip angle, the third yaw rate observation value of the sensor is calculated.

[0228] For example, the vehicle's rotational data includes vehicle pitch (suspension pitch) data, specifically including vehicle pitch angle, vehicle pitch gradient, longitudinal vehicle speed, and center of gravity sideslip angle. Based on the rotational equation, the sensor's third signal observation value is calculated, and the angular velocity observation value ω is calculated according to the following formula (33). r,3 :

[0229]

[0230] Where, θ pitch κ is the vehicle body pitch angle. pitch The vehicle pitch gradient is obtained from the actual vehicle calibration, u is the longitudinal vehicle speed, and β is the center of gravity sideslip angle.

[0231] In the technical solution of this application embodiment, the vehicle rotation data includes vehicle pitch angle, vehicle pitch gradient, longitudinal vehicle speed and center of gravity sideslip angle. The third yaw rate observation value is calculated based on the vehicle pitch angle, vehicle pitch gradient, longitudinal vehicle speed and center of gravity sideslip angle, which reduces the computational complexity and improves the accuracy of the calculation results.

[0232] Then, based on the third signal observation value and the signal output value of the sensor, the third estimated residual value is obtained.

[0233] Next, based on the third estimated residual value and the third residual value threshold, the third fault calibration result is obtained. For example, the third estimated residual value is calculated based on the third signal observation value and the signal output value, and the third estimated residual value is compared with the calibration residual value threshold (the third residual value threshold) to obtain the fault calibration result.

[0234] For example, based on the observed angular velocity ω r,3 Compared with the original value of sensor angular velocity ω r,raw The third estimated residual value ρ was calculated. r,3 The calculation formula (34) is as follows:

[0235] ρ r,3 =|ω r,3 |-|ω r,raw | (34)

[0236] Based on engineering experience, the threshold for the third residual value is ρ. rthd,3， The third fault calibration result flag is obtained through comparison. r3 As shown in formula (35):

[0237]

[0238] In the technical solution of this application embodiment, residual values ​​are calculated based on parameter information such as sensor power data, motion data, and rotation data, combined with the sensor output values. The results are then compared with threshold values ​​to determine the fault calibration result. This method simplifies the calculation process, classifies and analyzes the data, improves processing efficiency, and makes it easier to manage.

[0239] Based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, a signal fault result of the sensor is obtained, including: if at least two of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicate a signal fault, it is determined that the sensor signal has a fault. If at most one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates a signal fault, the fault diagnosis is terminated after a preset time period.

[0240] For example, based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, a three-way vote is conducted to determine the fault state of the yaw rate signal as shown in formula (36):

[0241]

[0242] If two or three results in the fault calibration list indicate a fault, the yaw rate signal of the sensor is considered faulty. If no results or only one result indicates a fault, the yaw rate signal is considered normal. This is the yaw rate fault status flag. x When true, it indicates a fault in the IMU yaw rate signal; flag x A false value indicates that the IMU yaw rate signal is normal.

[0243] The aforementioned preset time period refers to the following: when all three fault calibration results (first, second, and third) indicate that the signal is not faulty, the preset time period is the first time period; when one of the three fault calibration results indicates that the signal is faulty, the preset time period is the second time period, wherein the second time period is longer than the first time period.

[0244] For example, after the yaw rate fault is cleared, the delay time constant is determined based on the number of fault marker locations. tc The fault exit strategy is as shown in formula (37):

[0245]

[0246] Among them, t d t is the preset fault exit delay time constant. c This is a preset time period.

[0247] When the number of fault flag bits is 1, the preset time period is the second time period, lasting t. c =t d After a certain time, the yaw rate fault diagnosis process exits, and the yaw rate signal returns to normal. When the number of fault flag bits is 0, the preset time period is the first time period, lasting t. c =0.5t d After a certain time, the yaw rate fault diagnosis process is exited, and the yaw rate signal returns to normal. In formula (37), the second time period is longer than the first time period, and the coefficient of the first time period (e.g., coefficient 0.5) is any positive number less than 1. The preset time period corresponding to the yaw rate signal can be different from the preset time period corresponding to the longitudinal acceleration signal and the lateral acceleration signal.

[0248] In the technical solution of this application embodiment, when at most one of the three fault calibration results indicates a signal fault, it is necessary to exit fault diagnosis. To avoid exiting fault diagnosis too early, it can be exited after a preset time period has elapsed since at most one of the three fault calibration results indicates a signal fault. The preset time period length can be set according to the number of the three fault calibration results. The larger the number, the higher the probability that a fault still exists. To avoid premature exit from the fault diagnosis mechanism due to misjudgment, the larger the number, the longer the preset time period, so as to leave enough buffer time.

[0249] Figure 6 A block diagram of a vehicle sensor fault diagnosis device according to an embodiment of this application is shown.

[0250] This application provides a fault diagnosis device 600 for vehicle sensors. Please refer to [link to relevant documentation]. Figure 6 The vehicle sensor fault diagnosis device 600 includes:

[0251] The first determining module 610 is used to determine a first fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's power data.

[0252] The second determining module 620 is used to determine a second fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's motion data.

[0253] The third determination module 630 is used to determine a third fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's rotation data.

[0254] The acquisition module 640 is used to obtain the sensor signal fault result based on the first fault calibration result, the second fault calibration result and the third fault calibration result.

[0255] It is understood that for a detailed description of the vehicle sensor fault diagnosis device 600, please refer to the description of the vehicle sensor fault diagnosis method above.

[0256] For example, the first determining module 610 is further configured to: calculate a first signal observation value of the sensor based on the vehicle's power data; obtain a first estimated residual value based on the first signal observation value of the sensor and the signal output value of the sensor; and obtain the first fault calibration result based on the first estimated residual value and the first residual value threshold.

[0257] For example, the sensor signal includes a longitudinal acceleration signal; based on the vehicle's power data, the sensor's first signal observation value is calculated, including: based on the vehicle's total motor torque, total transmission ratio, transmission efficiency of the transmission system, wheel rolling radius, vehicle mass, longitudinal slope of the road where the vehicle is traveling, air resistance coefficient, vehicle frontal area, longitudinal vehicle speed, road rolling resistance coefficient, and rotational mass conversion factor, the sensor's first longitudinal acceleration observation value is calculated.

[0258] For example, the sensor signal includes a lateral acceleration signal; the calculation of the first signal observation value of the sensor based on the vehicle's dynamic data includes: calculating the vehicle yaw rate based on the vehicle wheelbase, longitudinal speed and stability factor; and calculating the first lateral acceleration observation value of the sensor based on the vehicle's yaw rate according to the vehicle's lateral dynamics model.

[0259] For example, based on the vehicle lateral dynamics model, the first lateral acceleration observation value of the sensor is calculated according to the vehicle yaw rate, including: the first lateral acceleration observation value of the sensor is calculated based on the vehicle yaw rate, the front axle lateral stiffness, the rear axle lateral stiffness, the center of gravity sideslip angle, the distance from the front axle to the center of gravity, the distance from the rear axle to the center of gravity, the front wheel rotation angle, and the vehicle mass.

[0260] For example, based on the vehicle lateral dynamics model, the first lateral acceleration observation value of the sensor is calculated according to the vehicle yaw rate, including: the first lateral acceleration observation value of the sensor is calculated based on the vehicle yaw rate, the front axle lateral stiffness, the rear axle lateral stiffness, the center of gravity sideslip angle, the distance from the front axle to the center of gravity, the distance from the rear axle to the center of gravity, the front wheel rotation angle, and the vehicle mass.

[0261] For example, the sensor signal includes a yaw rate signal; the calculation of the first signal observation value of the sensor based on the vehicle's dynamic data includes: calculating the first yaw rate observation value of the sensor based on the longitudinal vehicle speed, wheelbase, and stability factor.

[0262] For example, the obtaining module 640 is further configured to: determine that the sensor signal is faulty if at least two of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicate a signal fault.

[0263] For example, the vehicle sensor fault diagnosis device 600 further includes an exit module, which is used to exit fault diagnosis after a preset time period if at most one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates a signal fault.

[0264] For example, when the first fault calibration result, the second fault calibration result, and the third fault calibration result all indicate that the signal is not faulty, the preset time period is the first time period; when one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates that the signal is faulty, the preset time period is the second time period, wherein the second time period is longer than the first time period.

[0265] For example, the second determining module 620 is further configured to: calculate a second signal observation value of the sensor based on the vehicle's motion data; obtain a second estimated residual value based on the second signal observation value of the sensor and the signal output value of the sensor; and obtain the second fault calibration result based on the second estimated residual value and the second residual value threshold.

[0266] For example, the sensor signal includes a longitudinal acceleration signal; based on the vehicle's motion data, a second signal observation value of the sensor is calculated, including: based on the longitudinal vehicle speed, a second longitudinal acceleration observation value of the sensor is calculated.

[0267] For example, the sensor signal includes a lateral acceleration signal; the calculation of the second signal observation value of the sensor based on the vehicle's motion data includes: calculating the second lateral acceleration observation value of the sensor based on the longitudinal vehicle speed, center of gravity sideslip angle, front wheel rotation and wheelbase.

[0268] For example, the sensor signal includes a yaw rate signal; the calculation of the second signal observation value of the sensor based on the vehicle's motion data includes: calculating the second yaw rate observation value of the sensor based on the longitudinal vehicle speed, front wheel steering angle, and wheelbase.

[0269] For example, the third determining module 630 is further configured to: calculate the third signal observation value of the sensor based on the vehicle's rotation data; obtain the third estimated residual value based on the third signal observation value of the sensor and the signal output value of the sensor; and obtain the third fault calibration result based on the third estimated residual value and the third residual value threshold.

[0270] For example, the sensor signal includes a longitudinal acceleration signal; based on the vehicle's rotation data, a third signal observation value of the sensor is calculated, including: based on the vehicle's body pitch angle and body pitch gradient, a third longitudinal acceleration observation value of the sensor is calculated.

[0271] For example, the sensor signal includes a lateral acceleration signal; the calculation of the third signal observation value of the sensor based on the vehicle's rotation data includes: calculating the third lateral acceleration observation value of the sensor based on the vehicle's body roll angle and body roll gradient.

[0272] For example, the sensor signal includes a yaw rate signal; the calculation of the third signal observation value of the sensor based on the vehicle's rotation data includes: calculating the third yaw rate observation value of the sensor based on the vehicle's body pitch angle, body pitch gradient, longitudinal vehicle speed and center of gravity sideslip angle.

[0273] For example, the vehicle sensor fault diagnosis device 600 further includes: a fourth determination module, used to determine that the sensor signal is faulty when the absolute value of the sensor signal output value is greater than the road surface threshold of the signal.

[0274] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0275] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0276] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this application, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0277] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0278] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for diagnosing faults in vehicle sensors, characterized in that, The method includes: Based on the vehicle's power data, determine the first fault calibration result between the sensor's observed signal values ​​and the sensor's output signal values; Based on the vehicle's motion data, a second fault calibration result is determined between the sensor's observed signal values ​​and the sensor's output signal values. Based on the vehicle's rotation data, a third fault calibration result is determined between the sensor's observed signal value and the sensor's output signal value; Based on the first fault calibration result, the second fault calibration result, and the third fault calibration result, the signal fault result of the sensor is obtained.

2. The method according to claim 1, characterized in that, The determination of the first fault calibration result based on vehicle power data, between the sensor signal observations and the sensor signal outputs, includes: Based on the vehicle's power data, the first signal observation value of the sensor is calculated; Based on the first observed signal value and the signal output value of the sensor, the first estimated residual value is obtained; The first fault calibration result is obtained based on the first estimated residual value and the first residual value threshold.

3. The method according to claim 1 or 2, characterized in that, The second fault calibration result, determined based on vehicle motion data, between the sensor's observed signal values ​​and the sensor's output signal values, includes: Based on the vehicle's motion data, the second signal observation value of the sensor is calculated; Based on the second signal observation value and the signal output value of the sensor, the second estimated residual value is obtained; The second fault calibration result is obtained based on the second estimated residual value and the second residual value threshold.

4. The method according to any one of claims 1-3, characterized in that, The third fault calibration result, determined based on vehicle rotation data, between the sensor's observed signal values ​​and the sensor's output signal values, includes: Based on the vehicle's rotation data, the third signal observation value of the sensor is calculated; The third estimated residual value is obtained based on the third signal observation value and the sensor signal output value; The third fault calibration result is obtained based on the third estimated residual value and the third residual value threshold.

5. The method according to any one of claims 2-4, characterized in that, The sensor signals include longitudinal acceleration signals; the first sensor signal observation value, calculated based on the vehicle's dynamic data, includes: Based on the vehicle's total motor torque, total transmission ratio, transmission efficiency of the transmission system, wheel rolling radius, vehicle mass, longitudinal slope of the road on which the vehicle is traveling, air resistance coefficient, vehicle frontal area, longitudinal speed, road rolling resistance coefficient, and rotational mass conversion factor, the first longitudinal acceleration observation value of the sensor is calculated.

6. The method according to any one of claims 3-5, characterized in that, The sensor signals include longitudinal acceleration signals; the second signal observation value of the sensor, calculated based on the vehicle's motion data, includes: Based on the longitudinal vehicle speed, the second longitudinal acceleration observation value of the sensor is calculated.

7. The method according to any one of claims 4-6, characterized in that, The sensor signals include longitudinal acceleration signals; the third signal observation value of the sensor, calculated based on the vehicle's rotation data, includes: Based on the vehicle's pitch angle and pitch gradient, the third longitudinal acceleration observation value of the sensor is calculated.

8. The method according to any one of claims 2-4, characterized in that, The sensor signals include lateral acceleration signals; the first sensor signal observation value, calculated based on the vehicle's dynamic data, includes: Calculate the vehicle's yaw rate based on the vehicle's wheelbase, longitudinal speed, and stability factor; Based on the vehicle lateral dynamics model, the first lateral acceleration observation value of the sensor is calculated according to the vehicle yaw rate.

9. The method according to claim 8, characterized in that, Based on the vehicle lateral dynamics model, the first lateral acceleration observation value from the sensor is calculated according to the vehicle yaw rate, including: Based on the vehicle's yaw rate, front axle lateral stiffness, rear axle lateral stiffness, center of gravity sideslip angle, distance from the front axle to the center of gravity, distance from the rear axle to the center of gravity, front wheel rotation angle, and vehicle mass, the first lateral acceleration observation value of the sensor is calculated.

10. The method according to any one of claims 3-5, characterized in that, The sensor signals include lateral acceleration signals; the second signal observation value of the sensor, calculated based on the vehicle's motion data, includes: The second lateral acceleration observation value of the sensor is calculated based on the longitudinal vehicle speed, the center of gravity sideslip angle, the front wheel rotation, and the wheelbase.

11. The method according to any one of claims 4-6, characterized in that, The sensor signals include lateral acceleration signals; the third signal observation value of the sensor, calculated based on the vehicle's rotation data, includes: Based on the vehicle's roll angle and roll gradient, the third lateral acceleration observation value of the sensor is calculated.

12. The method according to any one of claims 2-4, characterized in that, The sensor signals include yaw rate signals; the first sensor signal observation value, calculated based on the vehicle's dynamic data, includes: The first yaw rate observation value of the sensor is calculated based on the longitudinal vehicle speed, wheelbase, and stability factor.

13. The method according to any one of claims 3-5, characterized in that, The sensor signals include yaw rate signals; the second signal observation value of the sensor, calculated based on the vehicle's motion data, includes: The second yaw rate observation value of the sensor is calculated based on the longitudinal vehicle speed, front wheel steering angle, and wheelbase.

14. The method according to any one of claims 4-6, characterized in that, The sensor signals include yaw rate signals; the third signal observation value of the sensor, calculated based on the vehicle's rotation data, includes: Based on the vehicle's body pitch angle, body pitch gradient, longitudinal vehicle speed, and center of gravity sideslip angle, the sensor's third yaw rate observation value is calculated.

15. The method according to any one of claims 1-14, characterized in that, The process of obtaining the sensor's signal fault result based on the first fault calibration result, the second fault calibration result, and the third fault calibration result includes: If at least two of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicate a signal fault, it is determined that the sensor signal is faulty.

16. The method according to claim 15, characterized in that, The method further includes: If at most one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates a signal fault, the fault diagnosis will be terminated after a preset time period.

17. The method according to claim 16, characterized in that: When the first fault calibration result, the second fault calibration result, and the third fault calibration result all indicate that the signal is not faulty, the preset time period is the first time period; If any one of the first fault calibration result, the second fault calibration result, and the third fault calibration result indicates a signal fault, then the preset time period is the second time period. The second time period is longer than the first time period.

18. The method according to any one of claims 1-17, characterized in that, The method further includes: If the absolute value of the sensor's signal output is greater than the road surface threshold, it is determined that the sensor's signal is faulty.

19. A fault diagnosis device for a vehicle sensor, characterized in that, The device includes: The first determining module is used to determine a first fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's power data. The second determination module is used to determine a second fault calibration result between the sensor's signal observation value and the sensor's signal output value based on the vehicle's motion data. The third determination module is used to determine the third fault calibration result between the sensor signal observation value and the sensor signal output value based on the vehicle's rotation data. The acquisition module is used to obtain the sensor's signal fault result based on the first fault calibration result, the second fault calibration result, and the third fault calibration result.

20. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-18.

21. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-18.

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