A Static Compensation and Calibration Method for Acceleration Sensors Used in Vehicle Ramp Estimation

Abstract

The present invention provides an acceleration sensor static compensation and correction method for vehicle ramp estimation. By calculating within the acceleration sensor self-learning model, static correction and compensation are performed on the vehicle acceleration sensor to obtain more accurate vehicle longitudinal, lateral, and vertical acceleration values, avoiding output signal errors caused by azimuth deviations of the acceleration sensor due to production differences and manual installation reasons, and providing reliable data for subsequent vehicle state recognition. The present invention provides more reliable data for ramp load identification of hybrid commercial vehicles, thereby improving the accuracy of vehicle ramp and load identification. By obtaining the sum of the three-axis vectors of the acceleration sensor and the included angles between the three axes of the acceleration sensor and the sum of the three-axis signal vectors, the deviation angles between the three axes of the acceleration sensor and the three axes of vehicle motion and the static error compensation values are obtained, the original acceleration signal is corrected, and finally the accurate acceleration values in the three axes of vehicle motion are output.

Description

Technical Field

[0001] The present invention relates to the field of vehicle state recognition for hybrid commercial vehicles, and specifically to an acceleration sensor static compensation and correction method for vehicle ramp estimation. Background Art

[0002] Currently in the industry, the estimation of vehicle ramp load mainly relies on acceleration sensors. The difference is that the number of measurement axes and sensitivity of the sensors vary among different manufacturers, mainly divided into two-axis and three-axis acceleration sensors. In actual applications, due to the different installation orientations and tilts of the acceleration sensors, it is necessary to compensate and correct the output of the sensors to obtain the actual acceleration values in each direction of the vehicle. If the current installation orientation angle of the sensor is horizontal and vertical, then in the static state, the longitudinal acceleration and lateral acceleration output values of the acceleration sensor should be approximately equal to 0, while the vertical acceleration output value should be the gravitational acceleration at the current position of the vehicle, that is, approximately equal to 9.8 m / s 2 . However, due to the deviation of the installation orientation, the gravitational acceleration will generate acceleration components acting on the longitudinal and lateral directions of the acceleration sensor, resulting in errors in the output. Many projects have made simple treatments to reduce the impact of installation differences on the output on the premise of trying to ensure the horizontal and vertical installation of the sensor as much as possible. The problem caused by this is that without ensuring production consistency, it is necessary to calibrate and calibrate and compensate each sensor individually. In order to further improve the degree of automation and software versatility, this project proposes an acceleration sensor static compensation and correction method for vehicle ramp estimation. Summary of the Invention

[0003] Aiming at the problem of static calibration and compensation of three-axis acceleration sensors in the prior art, the present invention provides an acceleration sensor static compensation and correction method for vehicle ramp estimation, which compensates and corrects the output of each axis of the three-axis acceleration sensor through self-learning to obtain more accurate data.

[0004] The present invention is realized through the following technical solutions:

[0005] An acceleration sensor static compensation and correction method for vehicle ramp estimation, comprising the following steps:

[0006] Step 1, establish an acceleration sensor self-learning model for the parameters of the vehicle in a stationary horizontal state;

[0007] Step 2, collect the three-axis signal vectors of the vehicle acceleration sensor;

[0008] Step 3, calculate the compensation values of the three-axis signals of the acceleration sensor in the vehicle stationary horizontal state and the deviation angles between the three axes of the acceleration sensor and the three axes of vehicle movement through the acceleration sensor self-learning model;

[0009] Step 4: Obtain the actual acceleration values in the three vehicle motion axes based on the raw values and compensation values of the three-axis signals of the acceleration sensor, as well as the deviation angles between the three axes of the acceleration sensor and the three vehicle motion axes.

[0010] Preferably, the calculation steps after the acceleration sensor self-learning model receives the three-axis signal vector are as follows:

[0011] Sum the three-axis vectors to obtain the sum of the three-axis signal vectors, where the sum of the three-axis signal vectors corresponds to the gravitational acceleration at the current position of the vehicle.

[0012] Obtain the angles between the three axes of the acceleration sensor and the sum of the three-axis signal vectors, and obtain the deviation angles between the three axes of the acceleration sensor and the three vehicle motion axes based on the angles between the three axes of the acceleration sensor and the sum of the three-axis signal vectors.

[0013] Obtain the compensation values of the three-axis signals of the acceleration sensor based on the deviation angles between the three axes of the acceleration sensor and the three vehicle motion axes and the sum of the three-axis signal vectors.

[0014] During the vehicle motion, correct the raw signal values through the obtained deviation angles and compensation values of the three-axis signals of the acceleration sensor to obtain the actual acceleration values in the three vehicle motion axes.

[0015] Furthermore, in the acceleration sensor self-learning model, the sum of the three-axis signal vectors is obtained by the vector method.

[0016] Furthermore, the calculation formula for the sum of the three-axis signal vectors is as follows:

[0017]

[0018] Where, AccRaw_X is the raw signal value of the X-axis of the acceleration sensor; AccRaw_Y is the raw signal value of the Y-axis of the acceleration sensor; AccRaw_Z is the raw signal value of the Z-axis of the acceleration sensor; AccSumVct is the sum of the three-axis signal vectors calculated by the sum of squares formula.

[0019] Even further, the sum of the three-axis signal vectors AccSumVct corresponds to the gravitational acceleration at the current position of the vehicle.

[0020] Furthermore, obtain the deviation angles between the three axes of the acceleration sensor and the three vehicle motion axes through the trigonometric function relationships among the three axes of the acceleration sensor, the sum of the three-axis signal vectors, and the three vehicle motion axes:

[0021] AgAccXOrntn = arcsin(AccRaw_X ÷ AccSumVct);

[0022] AgAccYOrntn = arcsin(AccRaw_Y ÷ AccSumVct);

[0023] AgAccZOrntn = arccos(AccRaw_Z ÷ AccSumVct);

[0024] Wherein, AgAccXOrntn is the deviation angle between the X-axis of the acceleration sensor and the X-axis of the vehicle movement; AgAccYOrntn is the deviation angle between the Y-axis of the acceleration sensor and the Y-axis of the vehicle movement; AgAccZOrntn is the deviation angle between the Z-axis of the acceleration sensor and the Z-axis of the vehicle movement; AccRaw_X is the original signal value of the X-axis of the acceleration sensor; AccRaw_Y is the original signal value of the Y-axis of the acceleration sensor; AccRaw_Z is the original signal value of the Z-axis of the acceleration sensor; AccSumVct is the sum vector of the three-axis signals calculated by the sum of squares formula.

[0025] Furthermore, the calculation formula for the static compensation value of the three-axis signals of the acceleration sensor is as follows:

[0026] Cmps_X = -AccRaw_X * cos(AgAccXOrntn),

[0027] Cmps_Y = -AccRaw_Y * cos(AgAccYOrntn),

[0028] Cmps_Z = -AccRaw_Z * cos(AgAccZOrntn),

[0029] Wherein, Cmps_X is the static compensation value of the X-axis signal of the acceleration sensor; Cmps_Y is the static compensation value of the Y-axis signal of the acceleration sensor; Cmps_Z is the static compensation value of the X-axis signal of the acceleration sensor; AgAccXOrntn is the deviation angle between the X-axis of the acceleration sensor and the X-axis of the vehicle movement; AgAccYOrntn is the deviation angle between the Y-axis of the acceleration sensor and the Y-axis of the vehicle movement; AgAccZOrntn is the deviation angle between the Z-axis of the acceleration sensor and the Z-axis of the vehicle movement; AccRaw_X is the original signal value of the X-axis of the acceleration sensor; AccRaw_Y is the original signal value of the Y-axis of the acceleration sensor; AccRaw_Z is the original signal value of the Z-axis of the acceleration sensor.

[0030] Furthermore, the calculation formula for the actual acceleration value in the three-axis directions of the vehicle movement is as follows:

[0031] AccX_Cltd = AccRaw_X ÷ cos(AgAccXOrntn) + Cmps_X

[0032] AccY_Cltd = AccRaw_Y ÷ cos(AgAccYOrntn) + Cmps_Y

[0033] AccZ_Cltd = AccRaw_Z ÷ cos(AgAccZOrntn) + Cmps_Z

[0034] Wherein, AccX Cltd is the actual acceleration value in the X-axis direction of the vehicle movement; AccY Cltd is the actual acceleration value in the Y-axis direction of the vehicle movement; AccZ Cltd is the actual acceleration value in the Z-axis direction of the vehicle movement; AccRaw_X is the original signal value of the X-axis of the acceleration sensor; AccRaw_Y is the original signal value of the Y-axis of the acceleration sensor; AccRaw_Z is the original signal value of the Z-axis of the acceleration sensor; Cmps_X is the static compensation value of the X-axis signal of the acceleration sensor; Cmps_Y is the static compensation value of the Y-axis signal of the acceleration sensor; Cmps_Z is the static compensation value of the X-axis signal of the acceleration sensor; AgAccXOrntn is the deviation angle between the X-axis of the acceleration sensor and the X-axis of the vehicle movement; AgAccYOrntn is the deviation angle between the Y-axis of the acceleration sensor and the Y-axis of the vehicle movement; AgAccZOrntn is the deviation angle between the Z-axis of the acceleration sensor and the Z-axis of the vehicle movement.

[0035] Compared with the prior art, the present invention has the following beneficial technical effects:

[0036] The present invention provides a method for static compensation and correction of an acceleration sensor for vehicle ramp estimation. By calculating within the acceleration sensor self-learning model, static correction compensation is performed on the vehicle acceleration sensor to obtain more accurate vehicle longitudinal, lateral, and vertical acceleration values, avoiding output signal errors caused by azimuth deviations of the acceleration sensor due to production differences and manual installation reasons, and providing reliable data for subsequent vehicle state recognition. The present invention provides more reliable data for ramp load identification of hybrid commercial vehicles, thereby improving the accuracy of vehicle ramp and load identification. By obtaining the sum of the three-axis vectors of the acceleration sensor and the included angles between the three axes of the acceleration sensor and the sum of the three-axis signal vectors, the deviation angles between the three axes of the acceleration sensor and the three axes of the vehicle movement and the static error compensation value are obtained, and the original acceleration signal is corrected, and finally the accurate acceleration values in the three axes of the vehicle movement are output. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flowchart of the method for static compensation and correction of an acceleration sensor for vehicle ramp estimation in the present invention;

[0038] Figure 2Schematic diagram of the azimuth deviation of the acceleration sensor in the present invention;

[0039] Figure 3 Schematic diagram of the original signal of the acceleration sensor in the present invention;

[0040] Figure 4 Schematic diagram of the triaxial deviation angle of the acceleration sensor in the present invention;

[0041] Figure 5 Schematic diagram of the triaxial compensation value of the acceleration sensor in the present invention;

[0042] Figure 6 Schematic diagram of the output correction value of the acceleration sensor in the present invention. Detailed implementation manners

[0043] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0044] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings:

[0046] In one embodiment of the present invention, an acceleration sensor static compensation and correction method for vehicle ramp estimation is provided. Through self-learning, the outputs of each axis of the triaxial acceleration sensor are compensated to obtain more accurate data.

[0047] Specifically, as shown in Figure 1 the acceleration sensor static compensation and correction method includes the following steps:

[0048] The vehicle is in a stationary horizontal state with the engine shut down, and an acceleration sensor self-learning model for establishing vehicle stationary horizontal state parameters is established;

[0049] The acceleration sensor collects the three-axis signal vector of the vehicle acceleration sensor, where the three-axis signal vector includes AccRaw_X, AccRaw_Y, and AccRaw_Z;

[0050] The three-axis signal vector is input into the acceleration sensor self-learning model for calculation to obtain the compensation value of the three-axis signal of the acceleration sensor in the vehicle stationary horizontal state and the deviation angle between the three axes of the acceleration sensor and the three axes of vehicle movement;

[0051] The actual acceleration value in the three axes of vehicle movement is obtained through the original value and compensation value of the three-axis signal of the acceleration sensor and the deviation angle between the three axes of the acceleration sensor and the three axes of vehicle movement.

[0052] Specifically, the calculation steps of the acceleration sensor self-learning model after receiving the three-axis signal vector are as follows:

[0053] Sum AccRaw_X, AccRaw_Y, and AccRaw_Z to obtain the sum of the three-axis signal vectors, where the sum of the three-axis signal vectors corresponds to the gravitational acceleration at the current position of the vehicle;

[0054] Obtain the included angle between the three axes of the acceleration sensor and the sum of the three-axis signal vectors, and obtain the deviation angle between the three axes of the acceleration sensor and the three axes of vehicle movement through the included angle between the three axes of the acceleration sensor and the sum of the three-axis signal vectors;

[0055] Obtain the compensation value of the three-axis signal of the acceleration sensor through the deviation angle between the three axes of the acceleration sensor and the three axes of vehicle movement and the sum of the three-axis signal vectors;

[0056] During vehicle movement, the actual acceleration value in the three axes of vehicle movement is obtained by correcting the original signal value through the obtained deviation angle and compensation value of the three-axis signal of the acceleration sensor.

[0057] Specifically, in the acceleration sensor self-learning model, the sum of the three-axis signal vectors of AccRaw_X, AccRaw_Y, and AccRaw_Z is obtained by the vector method.

[0058] Among them, the calculation formula for the sum of the three-axis signal vectors is as follows:

[0059]

[0060] Wherein, AccRaw_X is the original signal value of the X-axis of the acceleration sensor; AccRaw_Y is the original signal value of the Y-axis of the acceleration sensor; AccRaw_Z is the original signal value of the Z-axis of the acceleration sensor; AccSumVct is the sum of the three-axis signal vectors calculated by the sum of squares formula; and the sum of the three-axis signal vectors AccSumVct corresponds to the gravitational acceleration at the current position of the vehicle.

[0061] Specifically, the deviation angles between the three axes of the acceleration sensor and the three axes of the vehicle motion are obtained through the trigonometric function relationships among the three axes of the acceleration sensor, the sum of the three-axis signal vectors, and the three axes of the vehicle motion:

[0062] AgAccXOrntn = arcsin(AccRaw_X ÷ AccSumVCt):

[0063] AgAccYOrntn = arcsin(AccRaw_Y ÷ AccSumVct);

[0064] AgAccZOrntn = arccos(AccRaw_Z ÷ AccSumVct);

[0065] Wherein, AgAccXOrntn is the deviation angle between the X-axis of the acceleration sensor and the X-axis of the vehicle motion; AgAccYOrntn is the deviation angle between the Y-axis of the acceleration sensor and the Y-axis of the vehicle motion; AgAccZOrntn is the deviation angle between the Z-axis of the acceleration sensor and the Z-axis of the vehicle motion; AccRaw_X is the original signal value of the X-axis of the acceleration sensor; AccRaw_Y is the original signal value of the Y-axis of the acceleration sensor; AccRaw_Z is the original signal value of the Z-axis of the acceleration sensor; and AccSumVct is the sum of the three-axis signal vectors calculated by the sum of squares formula.

[0066] Specifically, the calculation formulas for the compensation values of the three-axis signals of the acceleration sensor are as follows:

[0067] Cmps_X = -AccRaw_X * cos(AgAccXOrntn);

[0068] Cmps_Y = -AccRaw_Y * cos(AgAccYOrntn);

[0069] Cmps_Z = -AccRaw_Z * cos(AgAccZOrntn);

[0070] Where, Cmps_X is the static compensation value of the acceleration sensor X-axis signal; Cmps_Y is the static compensation value of the acceleration sensor Y-axis signal; Cmps_Z is the static compensation value of the acceleration sensor X-axis signal; AgAccXOrntn is the deviation angle between the acceleration sensor X-axis and the vehicle motion X-axis; AgAccYOrntn is the deviation angle between the acceleration sensor Y-axis and the vehicle motion Y-axis; AgAccZOrntn is the deviation angle between the acceleration sensor Z-axis and the vehicle motion Z-axis; AccRaw_X is the raw signal value of the acceleration sensor X-axis; AccRaw_Y is the raw signal value of the acceleration sensor Y-axis; AccRaw_Z is the raw signal value of the acceleration sensor Z-axis.

[0071] Specifically, the calculation formula for the actual acceleration values in the three-axis directions of vehicle motion is as follows:

[0072] AccX_Cltd = AccRaw_X ÷ cos(AgAccXOrntn) + Cmps_X;

[0073] AccY_Cltd = AccRaw_Y ÷ cos(AgAccYOrntn) + Cmps_Y;

[0074] AccZ_Cltd = AccRaw_Z ÷ cos(AgAccZOrntn) + Cmps_Z;

[0075] Where, AccX Cltd is the actual acceleration value in the vehicle motion X-axis direction; AccY Cltd is the actual acceleration value in the vehicle motion Y-axis direction; AccZ Cltd is the actual acceleration value in the vehicle motion Z-axis direction; AccRaw_X is the raw signal value of the acceleration sensor X-axis; AccRaw_Y is the raw signal value of the acceleration sensor Y-axis; AccRaw_Z is the raw signal value of the acceleration sensor Z-axis; Cmps_X is the static compensation value of the acceleration sensor X-axis signal; Cmps_Y is the static compensation value of the acceleration sensor Y-axis signal; Cmps_Z is the static compensation value of the acceleration sensor X-axis signal; AgAccXOrntn is the deviation angle between the acceleration sensor X-axis and the vehicle motion X-axis; AgAccYOrntn is the deviation angle between the acceleration sensor Y-axis and the vehicle motion Y-axis; AgAccZOrntn is the deviation angle between the acceleration sensor Z-axis and the vehicle motion Z-axis.

[0076] Embodiment

[0077] In this embodiment, the acceleration sensor static compensation and calibration method for vehicle ramp estimation is used to perform acceleration sensor static compensation and calibration on a hybrid commercial vehicle. The specific steps are as follows:

[0078] Step 1: Stop the hybrid commercial vehicle on a horizontal road surface with the engine turned off, and ensure that the acceleration sensor is normally powered.

[0079] Step 2: Collect the three-axis signals AccRaw_X, AccRaw_Y, and AccRaw_Z of the vehicle acceleration sensor. In this example, take AccRaw_X in the collected original signals, AccRaw_X≈-0.65, as Figure 3 shown.

[0080] Step 3: Obtain the vector sum AccSumVct of the original three-axis signals of the acceleration sensor through the vector method. Since the vehicle is in a stationary horizontal state, this vector sum should correspond to the gravitational acceleration at the current position of the vehicle, as Figure 2 shown.

[0081] Step 4: Through the trigonometric function relationship between the vector sum AccSumVct of the three-axis signals of the acceleration sensor and each axis signal, combined with the three-axis reference system of the vehicle movement direction, the sine relationship can be used to obtain the angles between the three axes of the acceleration sensor and the three axes of each movement direction of the actual vehicle AgAccXOrntn≈-0.069rad, AgAccYOrntn≈0.0033rad, AgAccZOrntn≈0.069rad, as Figure 4 shown.

[0082] Step 6: Multiply the known three-axis signals of the vehicle acceleration sensor by the cosine values of the above angles AgAccXOrntn, AgAccYOrntn, and AgAccZOrntn respectively to obtain the actual acceleration compensation values Cmps_X≈0.65, Cmps_Y≈0.031, Cmps_Z≈0.46 acting on each movement direction of the vehicle by the gravitational acceleration, as Figure 5 shown.

[0083] Step 7: During the vehicle movement, correct and compensate the collected values of the acceleration sensor. Obtain the projections of the acceleration sensor in each movement direction of the vehicle through the cosine relationship of the angles AgAccXOrntn, AgAccYOrntn, and AgAccZOrntn.

[0084] This value is only the value of the original acceleration value after azimuth deviation correction, not the actual acceleration value in each movement direction of the vehicle.

[0085] Step 8, superimpose the value after the azimuth deviation correction on the actual acceleration compensation values Cmps_X, Cmps_Y, and Cmps_Z in all directions of the vehicle movement under the action of the gravitational acceleration to obtain the actual acceleration values Cltd_X≈0.01, Cltd_Y≈-0.008, Cltd_Z≈9.79 in all directions of the vehicle movement, as Figure 6 shown.

[0086] In summary, the present invention provides an acceleration sensor static compensation and correction method for vehicle ramp estimation. By calculating within the acceleration sensor self-learning model, static correction and compensation are performed on the vehicle acceleration sensor to obtain more accurate vehicle longitudinal, lateral, and vertical acceleration values, avoiding output signal errors caused by azimuth deviation of the acceleration sensor due to production differences and manual installation reasons, and providing reliable data for subsequent vehicle state recognition.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A static compensation and calibration method for an acceleration sensor used in vehicle ramp estimation, characterized in that, It includes the following steps: Step 1, establish an acceleration sensor self-learning model for vehicle static horizontal state parameters; Step 2, collect the three-axis signal vectors of the vehicle acceleration sensor; Step 3, calculate the compensation values of the three-axis signals of the acceleration sensor in the vehicle static horizontal state and the deviation angles between the three axes of the acceleration sensor and the three axes of vehicle movement through the acceleration sensor self-learning model; Step 4, obtain the actual acceleration values in the three axes of vehicle movement through the original values and compensation values of the three-axis signals of the acceleration sensor and the deviation angles between the three axes of the acceleration sensor and the three axes of vehicle movement; The calculation steps of the acceleration sensor self-learning model after receiving the three-axis signal vectors are as follows: Sum the three-axis vectors to obtain the sum of the three-axis signal vectors, where the sum of the three-axis signal vectors corresponds to the gravitational acceleration at the current position of the vehicle; Obtain the angles between the three axes of the acceleration sensor and the sum of the three-axis signal vectors, and obtain the deviation angles between the three axes of the acceleration sensor and the three axes of vehicle movement through the angles between the three axes of the acceleration sensor and the sum of the three-axis signal vectors; Obtain the compensation values of the three-axis signals of the acceleration sensor through the deviation angles between the three axes of the acceleration sensor and the three axes of vehicle movement and the sum of the three-axis signal vectors; During vehicle movement, correct the original signal values through the obtained deviation angles and compensation values of the three-axis signals of the acceleration sensor to obtain the actual acceleration values in the three axes of vehicle movement; It is characterized in that, in the acceleration sensor self-learning model, the sum of the three-axis signal vectors is obtained by the vector method; Obtain the deviation angles between the three axes of the acceleration sensor and the three axes of vehicle movement through the trigonometric function relationships among the three axes of the acceleration sensor, the sum of the three-axis signal vectors, and the three axes of vehicle movement: ； ； ； Among them, AgAccXOrntn is the deviation angle between the X-axis of the acceleration sensor and the X-axis of vehicle movement; AgAccYOrntn is the deviation angle between the Y-axis of the acceleration sensor and the Y-axis of vehicle movement; AgAccZOrntn is the deviation angle between the Z-axis of the acceleration sensor and the Z-axis of vehicle movement; AccRaw_X is the original signal value of the X-axis of the acceleration sensor; AccRaw_Y is the original signal value of the Y-axis of the acceleration sensor; AccRaw_Z is the original signal value of the Z-axis of the acceleration sensor; AccSumVct is the sum of the three-axis signal vectors calculated by the sum of squares formula; The calculation formula for the static compensation value of the three-axis signals of the acceleration sensor is as follows: Among them, Cmps_X is the static compensation value of the acceleration sensor X-axis signal; Cmps_Y is the static compensation value of the acceleration sensor Y-axis signal; Cmps_Z is the static compensation value of the acceleration sensor X-axis signal; AgAccXOrntn is the deviation angle between the acceleration sensor X-axis and the vehicle motion X-axis; AgAccYOrntn is the deviation angle between the acceleration sensor Y-axis and the vehicle motion Y-axis; AgAccZOrntn is the deviation angle between the acceleration sensor Z-axis and the vehicle motion Z-axis; AccRaw_X is the raw signal value of the acceleration sensor X-axis; AccRaw_Y is the raw signal value of the acceleration sensor Y-axis; AccRaw_Z is the raw signal value of the acceleration sensor Z-axis; The calculation formula for the actual acceleration values in the three-axis directions of vehicle motion is as follows: Among them, AccXCltd is the actual acceleration value in the vehicle motion X-axis direction; AccYCltd is the actual acceleration value in the vehicle motion Y-axis direction; AccZCltd is the actual acceleration value in the vehicle motion Z-axis direction; AccRaw_X is the raw signal value of the acceleration sensor X-axis; AccRaw_Y is the raw signal value of the acceleration sensor Y-axis; AccRaw_Z is the raw signal value of the acceleration sensor Z-axis; Cmps_X is the static compensation value of the acceleration sensor X-axis signal; Cmps_Y is the static compensation value of the acceleration sensor Y-axis signal; Cmps_Z is the static compensation value of the acceleration sensor X-axis signal; AgAccXOrntn is the deviation angle between the acceleration sensor X-axis and the vehicle motion X-axis; AgAccYOrntn is the deviation angle between the acceleration sensor Y-axis and the vehicle motion Y-axis; AgAccZOrntn is the deviation angle between the acceleration sensor Z-axis and the vehicle motion Z-axis.

2. The static compensation and calibration method of an acceleration sensor for vehicle ramp estimation according to claim 1, characterized in that Among them, the calculation formula for the sum of the three-axis signal vectors is as follows: ； Among them, AccRaw_X is the raw signal value of the acceleration sensor X-axis; AccRaw_Y is the raw signal value of the acceleration sensor Y-axis; AccRaw_Z is the raw signal value of the acceleration sensor Z-axis; AccSumVct is the sum of the three-axis signal vectors calculated by the sum of squares formula.

3. A static compensation and calibration method for an acceleration sensor used in vehicle ramp estimation according to claim 2, characterized in that, The sum of the three-axis signal vectors AccSumVct corresponds to the gravitational acceleration at the current position of the vehicle.

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