CN113566777A - Vehicle pitch angle estimation method and system, computer device and storage medium - Google Patents

Vehicle pitch angle estimation method and system, computer device and storage medium Download PDF

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CN113566777A
CN113566777A CN202010356681.0A CN202010356681A CN113566777A CN 113566777 A CN113566777 A CN 113566777A CN 202010356681 A CN202010356681 A CN 202010356681A CN 113566777 A CN113566777 A CN 113566777A
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pitch angle
longitudinal acceleration
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CN113566777B (en
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梅兴泰
邓成
马传帅
林长青
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Guangzhou Automobile Group Co Ltd
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Abstract

The invention relates to a vehicle pitch angle estimation method and a system, a computer device and a storage medium thereof, wherein the method comprises the following steps: acquiring the current speed of a vehicle, and acquiring a first longitudinal acceleration according to the current speed; acquiring a current slope angle and a second longitudinal acceleration detected by a vehicle acceleration sensor currently; obtaining a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration; and performing Kalman filtering according to the measured pitch angle to obtain a pitch angle estimated value of the vehicle. The system corresponds to the method, and the computer device and the storage medium include a program capable of executing the method. The method can solve the problem of low estimation precision in the prior vehicle pitch angle estimation according to other vehicle sensor signals under the condition of not increasing the vehicle hardware cost.

Description

Vehicle pitch angle estimation method and system, computer device and storage medium
Technical Field
The invention relates to the technical field of vehicle motion state estimation, in particular to a vehicle pitch angle estimation method and system, computer equipment and storage media.
Background
Vehicle pitch is an important state of a vehicle and is closely related to ride comfort, and one goal of dynamic suspension control is to control vehicle pitch. In the field of intelligent assisted driving, it is also desirable to obtain the vehicle pitch angle to more accurately perceive the environment. There are also vehicles in which the headlamp height is automatically adjusted by the pitch angle of the vehicle. Therefore, it is important to obtain a more accurate pitch angle of the vehicle. At present, an ESP (electronic stability program) is generally installed on a passenger car to control the stability of the car under the limit working condition, and the passenger car has the functions of measuring longitudinal acceleration, lateral acceleration and the like by an inertial sensor but does not have the function of measuring a pitch angle. If a gyroscope is installed to measure the pitch angle, hardware cost is increased, and for the problem, vehicle pitch angle estimation is mainly carried out according to signals of other existing vehicle sensors at present, specifically, the vehicle pitch angle is mainly obtained according to the deviation of the actual acceleration of a vehicle and the longitudinal acceleration measured by an inertial sensor, and Kalman filtering is introduced to reduce fluctuation of an estimation result; however, the method does not consider the influence of the ramp on the estimation of the pitch angle, and the actual longitudinal acceleration of the vehicle is obtained only by vehicle speed derivation, so that high-frequency noise is introduced; moreover, the model equation in the Kalman filtering does not consider the dynamic factor of the pitch angle change, and only can give an approximate estimation based on the measurement signal through the Kalman filtering, so that the problem of low estimation precision exists in the vehicle pitch angle estimation according to the signals of other vehicle sensors at present.
Disclosure of Invention
The invention aims to provide a vehicle pitch angle estimation method and a system, computer equipment and a storage medium thereof, so as to solve the problem of low estimation precision in the prior vehicle pitch angle estimation according to other vehicle sensor signals under the condition of not increasing the hardware cost of a vehicle.
In a first aspect, an embodiment of the present invention provides a vehicle pitch angle estimation method, including:
acquiring the current speed of a vehicle, and acquiring a first longitudinal acceleration according to the current speed;
acquiring a current slope angle and a second longitudinal acceleration detected by a vehicle acceleration sensor currently;
obtaining a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration;
and taking the measured pitch angle as an observation variable, and performing Kalman filtering to obtain a pitch angle estimation value of the vehicle.
In an optional manner, the measured pitch angle is obtained according to the first longitudinal acceleration, the current slope angle, and the second longitudinal acceleration, and is specifically represented by the following formula:
Figure BDA0002473700060000021
wherein, thetasTo measure the pitch angle, axsIs a second longitudinal acceleration, axIs the first longitudinal acceleration, alpha is the slope angle, and g is the acceleration of gravity.
In an optional manner, if a current time k is set, and an observation variable at the time k is y (k), the performing kalman filtering to obtain the estimated value of the pitch angle of the vehicle includes:
performing one-step state prediction to obtain state prediction quantity at k moment
Figure BDA0002473700060000022
Wherein the content of the first and second substances,
Figure BDA0002473700060000023
Figure BDA0002473700060000024
Figure BDA0002473700060000025
is the system state quantity at the time k-1, u (k-1) is the value of u at the time k-1, FxIs vehicle longitudinal force, h is vehicle center of mass height, M is vehicle sprung mass, M is vehicle total mass, I'yFor equivalent moment of inertia of the vehicle, MyThe pitch resisting moment of the vehicle is shown, and T is a step length;
acquiring process noise Q (k-1) at a time k-1, and performing one-step prediction on variance according to the process noise Q (k-1) to obtain a variance prediction quantity P (k, k-1) at the time k, wherein P (k, k-1) ═ GP (k-1) G' + Q (k-1), and P (k-1) is the variance at the time k-1;
obtaining observation noise R (k) at the time k, and predicting quantity according to the state at the time k
Figure BDA0002473700060000031
Estimating a system state quantity at the k moment by using a variance prediction quantity P (k, k-1), an observation variable y (k) and observation noise R (k)
Figure BDA0002473700060000032
Wherein the content of the first and second substances,
Figure BDA0002473700060000033
C＝[1 0]，K(k)＝P(k,k-1)C/[C·P(k,k-1)·C′+R(k)]；
according to the system state quantity of the k time
Figure BDA0002473700060000034
And obtaining a pitching angle estimated value of the vehicle at the moment k.
In an optional manner, the obtaining of the observation noise r (k) at the time k includes:
determining observation noise R (k) at the moment k according to the first longitudinal acceleration at the moment k and the corresponding relation between the first longitudinal acceleration and the observation noise;
or determining the observation noise R (k) at the time k according to the second longitudinal acceleration at the time k and the corresponding relation between the second longitudinal acceleration and the observation noise.
In a second aspect, an embodiment of the present invention provides a vehicle pitch angle estimation system, including:
the first signal acquisition unit is used for acquiring the current speed of the vehicle and acquiring a first longitudinal acceleration according to the current speed;
the second signal acquisition unit is used for acquiring the current slope angle and the second longitudinal acceleration detected by the vehicle acceleration sensor;
the observation variable acquisition unit is used for acquiring a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration; and
and the Kalman filtering unit is used for taking the measured pitch angle as an observation variable and carrying out Kalman filtering to obtain a pitch angle estimated value of the vehicle.
In an alternative, the observation variable obtaining unit is specifically configured to obtain the measured pitch angle according to a formula,
Figure BDA0002473700060000035
wherein, thetasTo measure the pitch angle, axsIs a second longitudinal acceleration, axIs the first longitudinal acceleration, alpha is the slope angle, and g is the acceleration of gravity.
In an optional manner, the kalman filtering unit specifically includes:
a one-step state prediction unit for performing one-step state prediction to obtain a state prediction quantity at the k time
Figure BDA0002473700060000041
Wherein, the k time represents the current time,
Figure BDA0002473700060000042
Figure BDA0002473700060000043
Figure BDA0002473700060000044
is the system state quantity at the time k-1, u (k-1) is the value of u at the time k-1, FxIs vehicle longitudinal force, h is vehicle center of mass height, M is vehicle sprung mass, M is vehicle total mass, I'yFor equivalent moment of inertia of the vehicle, MyThe pitch resisting moment of the vehicle is shown, and T is a step length;
the one-step variance prediction unit is used for acquiring process noise Q (k-1) at the moment k-1 and performing one-step prediction variance according to the process noise Q (k-1) to obtain a k-moment variance prediction quantity P (k, k-1), wherein P (k, k-1) ═ GP (k-1) G' + Q (k-1), and P (k-1) is k-1 moment variance;
a system state quantity estimation unit for acquiring observation noise R (k) at the time k and predicting quantity according to the state at the time k
Figure BDA0002473700060000045
Estimating a system state quantity at the k moment by using a variance prediction quantity P (k, k-1), an observation variable y (k) and observation noise R (k)
Figure BDA0002473700060000046
Wherein the content of the first and second substances,
Figure BDA0002473700060000047
C＝[1 0]，K(k)＝P(k,k-1)C/[C·P(k,k-1)·C′+R(k)](ii) a And
a pitch angle estimation unit for estimating the system state quantity according to the k time
Figure BDA0002473700060000048
And obtaining a pitching angle estimated value of the vehicle at the moment k.
In an optional manner, the system state quantity estimation unit is further specifically configured to:
determining observation noise R (k) at the moment k according to the first longitudinal acceleration at the moment k and the corresponding relation between the first longitudinal acceleration and the observation noise;
or determining the observation noise R (k) at the time k according to the second longitudinal acceleration at the time k and the corresponding relation between the second longitudinal acceleration and the observation noise.
In a third aspect, an embodiment of the present invention provides a computer device, including: the vehicle pitch angle estimation system according to the embodiment of the second aspect; or a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the vehicle pitch angle estimation method according to an embodiment of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the vehicle pitch angle estimation method of the first aspect.
The above embodiment scheme has at least the following beneficial effects:
when estimating the pitch angle of the vehicle, fully and comprehensively considering a first longitudinal acceleration obtained by deriving the current vehicle speed, a slope angle of the current road and a second longitudinal acceleration obtained by current detection of a vehicle acceleration sensor, obtaining a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration, and performing Kalman filtering to obtain a pitch angle estimated value of the vehicle by taking the measured pitch angle as an observation variable. Therefore, the problem that the estimation precision is not high when the vehicle pitch angle estimation is carried out according to signals of other vehicle sensors at present is solved under the condition that the hardware cost of the vehicle is not increased.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a vehicle pitch angle estimation method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a vehicle pitch angle estimation system according to another embodiment of the present invention.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
In addition, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known means have not been described in detail so as not to obscure the present invention.
As shown in fig. 1, an embodiment of the present invention provides a vehicle pitch angle estimation method, which includes steps S10 to S40:
step S10, acquiring the current speed of the vehicle, and acquiring a first longitudinal acceleration according to the current speed;
specifically, in step S10, the longitudinal component of the current vehicle speed is differentiated to obtain a first longitudinal acceleration, and the current vehicle speed of the vehicle may be acquired through a CAN bus.
Step S20, acquiring a current slope angle and a second longitudinal acceleration detected by the vehicle acceleration sensor;
specifically, the current slope angle may be acquired from a map through GPS navigation information, and the map data includes slope angle information of each road, so that the slope angle corresponding to the road on which the vehicle is located may be acquired according to the vehicle position information. Of course, other methods for estimating the slope angle may also be used for real-time estimation, and the obtaining manner of the slope angle is not specifically limited in this embodiment, which is within the protection scope of the present invention.
In general, the vehicle is provided with a vehicle acceleration sensor (for example, an inertial sensor in the ESP) for detecting a longitudinal acceleration of the vehicle, and the longitudinal acceleration detected by the vehicle acceleration sensor is defined as a second longitudinal acceleration in the present embodiment.
Step S30, obtaining a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration;
specifically, changes in vehicle pitch angle are primarily due to changes in front and rear axle loads, resulting in changes in suspension deformation, which causes the vehicle to pitch or tilt forward. Thus, the vehicle pitch angle can be estimated by estimating the front-rear axle load, and incorporating the suspension characteristics. Because the vertical rigidity of the tire is obviously higher than that of the suspension, the attitude of the unsprung mass of the vehicle is assumed to be unchanged during running, and the pitching one-dimensional kinetic equation of the vehicle is shown as a formula (1).
Figure BDA0002473700060000071
Wherein theta is a pitch angle,
Figure BDA0002473700060000072
for pitch angular acceleration, FxIs longitudinal force, h is centroid height, M is sprung mass, M is total vehicle mass, I'yTo equivalent moment of inertia, MyIs pitch drag torque.
I′y＝Iy+mh2 (2)
Wherein, IyThe moment of inertia for the vehicle to rotate about the y-axis can be obtained by advance measurements.
Fx＝m·ax (3)
Wherein, axThe longitudinal acceleration of the vehicle can be obtained by longitudinal vehicle speed derivation.
Figure BDA0002473700060000073
Wherein l1Is the distance of the front axis from the center of mass in the X direction, l2Is the distance of the rear axis from the center of mass in the X direction, K1、K2Spring rates, R, for front and rear axle suspension respectively1、R2Respectively front and rear shock absorber characteristics.
Generally speaking, the gravity center height, the mass center X axial position and the rotation inertia around the Y axis under the vehicle design state can be obtained through a KC bench test. By adopting relevant parameters of the vehicle at half load and combining the characteristics of the suspension and the longitudinal acceleration of the vehicle, the pitch angle of the vehicle can be estimated. However, the actual load of the vehicle is often changed, the position of the center of gravity is changed correspondingly, and the formula (1) has certain uncertainty, so that the pitch angle is estimated by the deviation between the longitudinal acceleration measured by the longitudinal sensor and the actual longitudinal acceleration of the vehicle in the embodiment, and the judgment accuracy is improved.
The X direction of an inertial sensor installed in an ESP of a vehicle is superposed with the X axis of the vehicle, and the change of the pitch angle of the vehicle can cause a first longitudinal acceleration axAnd a second longitudinal acceleration axsWith a deviation ofxAnd axsThe relationship of (1) is:
Figure BDA0002473700060000081
wherein lcThe lateral offset of the sensor and the rotation center during turning,
Figure BDA0002473700060000082
is yaw angular acceleration, r is yaw angular velocity, VyIs the Y-direction velocity, g is the gravity acceleration and alpha is the slope angle. Neglecting the yaw motion effect, there are:
axs＝axcosθ+g sin(α+θ) (6)
and (4) obtaining the measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration through a formula (6).
And step S40, taking the measured pitch angle as an observation variable, and performing Kalman filtering to obtain a pitch angle estimation value of the vehicle.
Based on the above contents, in the method of the embodiment, when estimating the pitch angle of the vehicle, a first longitudinal acceleration obtained by deriving a current vehicle speed, a current road slope angle and a second longitudinal acceleration obtained by current detection of a vehicle acceleration sensor are fully and comprehensively considered, a measured pitch angle is obtained according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration, and kalman filtering is performed to obtain a pitch angle estimated value of the vehicle by using the measured pitch angle as an observation variable. Therefore, the problem that the estimation precision is not high when the vehicle pitch angle estimation is carried out according to signals of other vehicle sensors at present is solved under the condition that the hardware cost of the vehicle is not increased.
In a preferred embodiment, the measured pitch angle is obtained according to the first longitudinal acceleration, the current pitch angle and the second longitudinal acceleration, and the following formula is specifically shown:
Figure BDA0002473700060000091
wherein, thetasTo measure the pitch angle, axsIs a second longitudinal acceleration, axIs the first longitudinal acceleration, alpha is the slope angle, and g is the acceleration of gravity.
Specifically, based on the above equation (6), the measured pitch angle observed by the sensor is as shown in equation (7) considering that the vehicle pitch angle is lower than 3 ° and cos θ ≈ 1 in most operating conditions.
In a preferred embodiment, Kalman filtering is used to estimate the vehicle pitch angle, and system equation modeling is performed based on equation (1) with the system state vector as
Figure BDA0002473700060000092
Figure BDA0002473700060000093
The observation variable y is the pitch angle thetasThen the system state space equation is:
Figure BDA0002473700060000094
wherein the content of the first and second substances,
Figure BDA0002473700060000095
C＝[1 0]。
in the matrix a, the matrix b is,
Figure BDA0002473700060000101
discretizing equation (8) yields:
Figure BDA0002473700060000102
wherein the content of the first and second substances,
Figure BDA0002473700060000103
and T is the step length, namely the time interval between the current cycle and the last cycle of the pitch angle estimation, which is a preset parameter value.
Based on the above kalman filter model considering the vehicle model, if the current time k is set, and the observation variable at the time k is y (k), the step S40 specifically includes:
step S401, performing one-step state prediction to obtain a state prediction quantity at the k time
Figure BDA0002473700060000104
Wherein the content of the first and second substances,
Figure BDA0002473700060000105
Figure BDA0002473700060000106
is the system state quantity at the moment k-1, and u (k-1) is the value of u at the moment k-1;
step S402, acquiring process noise Q (k-1) at the time of k-1, and performing one-step prediction on variance according to the process noise Q (k-1) to obtain a variance prediction quantity P (k, k-1) at the time of k, wherein P (k, k-1) ═ GP (k-1) G' + Q (k-1), and P (k-1) is the variance at the time of k-1;
step S403, acquiring observation noise R (k) at the time k, and predicting quantity according to the state at the time k
Figure BDA0002473700060000107
Estimating a system state quantity at the k moment by using a variance prediction quantity P (k, k-1), an observation variable y (k) and observation noise R (k)
Figure BDA0002473700060000108
Wherein the content of the first and second substances,
Figure BDA0002473700060000109
k (k) is a filter gain matrix, k (k) ═ P (k, k-1) C/[ C · P (k, k-1) · C' + r (k)]；
Step S404, updating the variance according to the predicted variance P (k, k-1) at time k to obtain and store a time k variance P (k), where P (k) is [ I-k (k) · C ] · P (k, k-1), and I is a unit matrix; wherein, the k-time variance p (k) is used for the subsequent k + 1-time pitch angle estimation;
step S405, according to the system state quantity of the k time
Figure BDA0002473700060000111
And obtaining a pitching angle estimated value of the vehicle at the moment k.
Specifically, in step S405, the system state quantity at time CX and k is obtained from the observation matrix y
Figure BDA0002473700060000112
Namely, the estimated value of the pitch angle of the vehicle at the moment k is obtained.
Specifically, compared with the existing pitch angle kalman filtering model, the kalman filtering model in the embodiment considers the dynamic factor of the pitch angle change, improves the conventional kalman filtering model, and combines the measured pitch angle estimated by the sensor measurement data with the estimated pitch angle of the one-dimensional dynamic model, so that the variance parameter in the kalman filtering changes along with the working condition, thereby further improving the precision of the pitch angle estimation value.
In a preferred embodiment, the obtaining of the observation noise r (k) at the time k includes:
determining observation noise R (k) at the moment k according to the first longitudinal acceleration at the moment k and the corresponding relation between the first longitudinal acceleration and the observation noise;
or determining the observation noise R (k) at the time k according to the second longitudinal acceleration at the time k and the corresponding relation between the second longitudinal acceleration and the observation noise.
Specifically, there is a certain correspondence between the longitudinal acceleration of the vehicle and the observed noise, which can be roughly described as: when the vehicle does not change greatly in the longitudinal acceleration during linear motion, a value with smaller measurement noise variance is given; when the lateral acceleration of the vehicle is large and the longitudinal acceleration changes violently, a value with large variance of the measured noise is given.
It should be noted that the corresponding relationship is calibrated in advance according to the real vehicle measurement data, and the corresponding relationship is different for different vehicle types. In the embodiment, the first longitudinal acceleration and the second longitudinal acceleration are comprehensively considered, so that in specific application, the corresponding relationship between the first longitudinal acceleration and the observation noise or the corresponding relationship between the second longitudinal acceleration and the observation noise may be considered separately to determine the observation noise r (k) at the time k, or the corresponding relationship between the first longitudinal acceleration, the second longitudinal acceleration and the observation noise may be considered comprehensively, and the specifically considered factors are used for calibrating real vehicle measurement data to obtain the corresponding relationship, so as to obtain the observation noise r (k).
As shown in fig. 2, an embodiment of the present invention provides a vehicle pitch angle estimation system, including:
the system comprises a first signal acquisition unit 1, a second signal acquisition unit and a control unit, wherein the first signal acquisition unit is used for acquiring the current speed of a vehicle and acquiring a first longitudinal acceleration according to the current speed;
the second signal acquisition unit 2 is used for acquiring a current slope angle and a second longitudinal acceleration detected by the vehicle acceleration sensor currently;
the observation variable acquiring unit 3 acquires a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration; and
and the Kalman filtering unit 4 is used for taking the measured pitch angle as an observation variable and carrying out Kalman filtering to obtain a pitch angle estimated value of the vehicle.
In a preferred embodiment, the observation variable obtaining unit 3 is specifically configured to obtain the measured pitch angle according to a formula,
Figure BDA0002473700060000121
wherein，θsTo measure the pitch angle, axsIs a second longitudinal acceleration, axIs the first longitudinal acceleration, alpha is the slope angle, and g is the acceleration of gravity.
In a preferred embodiment, the kalman filtering unit 4 specifically includes:
a one-step state prediction unit 41 for performing one-step state prediction to obtain a state prediction quantity at the time k
Figure BDA0002473700060000122
Wherein, the k time represents the current time,
Figure BDA0002473700060000123
Figure BDA0002473700060000124
Figure BDA0002473700060000125
is the system state quantity at the time k-1, u (k-1) is the value of u at the time k-1, FxIs vehicle longitudinal force, h is vehicle center of mass height, M is vehicle sprung mass, M is vehicle total mass, I'yFor equivalent moment of inertia of the vehicle, MyThe pitch resisting moment of the vehicle is taken as T, and T is a step length which is a time interval between the current cycle and the previous cycle of the program;
a one-step variance prediction unit 42, configured to obtain a process noise Q (k-1) at time k-1, and perform one-step prediction variance according to the process noise Q (k-1) to obtain a k-time variance prediction quantity P (k, k-1), where P (k, k-1) ═ GP (k-1) G' + Q (k-1), and P (k-1) is a k-1 time variance;
a system state quantity estimating unit 43 for acquiring observation noise R (k) at time k and predicting quantity according to state at time k
Figure BDA0002473700060000131
Estimating a system state quantity at the k moment by using a variance prediction quantity P (k, k-1), an observation variable y (k) and observation noise R (k)
Figure BDA0002473700060000132
Wherein the content of the first and second substances,
Figure BDA0002473700060000133
C＝[1 0]，K(k)＝P(k,k-1)C/[C·P(k,k-1)·C′+R(k)]；
a variance estimation unit 44, configured to perform variance update according to the variance prediction amount P (k, k-1) at time k to obtain a time k variance P (k), where P (k) is [ I-k (k) · C ] · P (k, k-1), and I is a unit matrix; and
a pitch angle estimation unit 45 for estimating the system state quantity according to the k time
Figure BDA0002473700060000134
And the variance P (k) obtains the estimated pitch angle value of the vehicle at the moment k.
In a preferred embodiment, the system state quantity estimation unit 43 is further configured to:
determining observation noise R (k) at the moment k according to the first longitudinal acceleration at the moment k and the corresponding relation between the first longitudinal acceleration and the observation noise;
or determining the observation noise R (k) at the time k according to the second longitudinal acceleration at the time k and the corresponding relation between the second longitudinal acceleration and the observation noise.
Or determining the observation noise R (k) at the moment k according to the first longitudinal acceleration and the second longitudinal acceleration at the moment k and the corresponding relation between the first longitudinal acceleration and the observation noise and the second longitudinal acceleration.
The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
It should be noted that the system described in the foregoing embodiment corresponds to the method described in the foregoing embodiment, and therefore, portions of the system described in the foregoing embodiment that are not described in detail can be obtained by referring to the content of the method described in the foregoing embodiment, and details are not described here.
Also, the vehicle pitch angle estimation system according to the above embodiment, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer-readable storage medium.
Another embodiment of the present invention further provides a computer device, including: the vehicle pitch angle estimation system according to the above embodiment; or a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the vehicle pitch angle estimation method according to the above embodiment.
Of course, the computer device may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input/output, and the computer device may also include other components for implementing the functions of the device, which are not described herein again.
Illustratively, the computer program may be divided into one or more units, which are stored in the memory and executed by the processor to accomplish the present invention. The one or more units may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the computer device.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center for the computer device and connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used for storing the computer program and/or unit, and the processor may implement various functions of the computer device by executing or executing the computer program and/or unit stored in the memory and calling data stored in the memory. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Another embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the vehicle pitch angle estimation method according to the above-mentioned embodiment.
Specifically, the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. A vehicle pitch angle estimation method, characterized by comprising:
acquiring the current speed of a vehicle, and acquiring a first longitudinal acceleration according to the current speed;
acquiring a current slope angle and a second longitudinal acceleration detected by a vehicle acceleration sensor currently;
obtaining a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration;
and taking the measured pitch angle as an observation variable, and performing Kalman filtering to obtain a pitch angle estimation value of the vehicle.
2. The vehicle pitch angle estimation method according to claim 1, wherein the measured pitch angle is obtained from the first longitudinal acceleration, the current pitch angle, and the second longitudinal acceleration, as shown in the following formula:
Figure FDA0002473700050000011
wherein, thetasTo measure the pitch angle, axsIs a second longitudinal acceleration, axIs the first longitudinal acceleration, alpha is the slope angle, and g is the acceleration of gravity.
3. The vehicle pitch angle estimation method according to claim 1, wherein if a current time k is set, and an observation variable at the time k is y (k), the performing kalman filtering to obtain the vehicle pitch angle estimation value includes:
performing one-step state prediction to obtain state prediction quantity at k moment
Figure FDA0002473700050000012
Wherein the content of the first and second substances,
Figure FDA0002473700050000013
Figure FDA0002473700050000014
Figure FDA0002473700050000015
is the system state quantity at the time k-1, u (k-1) is the value of u at the time k-1, FxIs vehicle longitudinal force, h is vehicle center of mass height, M is vehicle sprung mass, M is vehicle total mass, I'yFor equivalent moment of inertia of the vehicle, MyThe pitch resisting moment of the vehicle is shown, and T is a step length;
acquiring process noise Q (k-1) at a time k-1, and performing one-step prediction on variance according to the process noise Q (k-1) to obtain a variance prediction quantity P (k, k-1) at the time k, wherein P (k, k-1) ═ GP (k-1) G' + Q (k-1), and P (k-1) is the variance at the time k-1;
obtaining observation noise R (k) at the time k, and predicting quantity according to the state at the time k
Figure FDA0002473700050000021
Estimating a system state quantity at the k moment by using a variance prediction quantity P (k, k-1), an observation variable y (k) and observation noise R (k)
Figure FDA0002473700050000022
Wherein the content of the first and second substances,
Figure FDA0002473700050000023
C＝[1 0]，K(k)＝P(k,k-1)C/[C·P(k,k-1)·C′+R(k)]；
according to the system state quantity of the k time
Figure FDA0002473700050000024
And obtaining a pitching angle estimated value of the vehicle at the moment k.
4. The vehicle pitch angle estimation method of claim 3, wherein said obtaining observed noise R (k) at time k comprises:
determining observation noise R (k) at the moment k according to the first longitudinal acceleration at the moment k and the corresponding relation between the first longitudinal acceleration and the observation noise;
or determining the observation noise R (k) at the time k according to the second longitudinal acceleration at the time k and the corresponding relation between the second longitudinal acceleration and the observation noise.
5. A vehicle pitch angle estimation system, comprising:
the first signal acquisition unit is used for acquiring the current speed of the vehicle and acquiring a first longitudinal acceleration according to the current speed;
the second signal acquisition unit is used for acquiring the current slope angle and the second longitudinal acceleration detected by the vehicle acceleration sensor;
the observation variable acquisition unit is used for acquiring a measured pitch angle according to the first longitudinal acceleration, the current slope angle and the second longitudinal acceleration; and
and the Kalman filtering unit is used for taking the measured pitch angle as an observation variable and carrying out Kalman filtering to obtain a pitch angle estimated value of the vehicle.
6. The vehicle pitch angle estimation system of claim 5, wherein the observation variable acquisition unit is specifically configured to obtain the measured pitch angle according to a formula,
Figure FDA0002473700050000031
wherein, thetasTo measure the pitch angle, axsIs a second longitudinal acceleration, axIs the first longitudinal acceleration, alpha is the slope angle, and g is the acceleration of gravity.
7. The vehicle pitch angle estimation system according to claim 5, wherein the Kalman filtering unit specifically comprises:
a one-step state prediction unit for performing one-step state prediction to obtain a state prediction quantity at the k time
Figure FDA0002473700050000032
Wherein, the k time represents the current time,
Figure FDA0002473700050000033
Figure FDA0002473700050000034
Figure FDA0002473700050000035
is the system state quantity at the time k-1, u (k-1) is the value of u at the time k-1, FxIs vehicle longitudinal force, h is vehicle center of mass height, M is vehicle sprung mass, M is vehicle total mass, I'yFor equivalent moment of inertia of the vehicle, MyThe pitch resisting moment of the vehicle is shown, and T is a step length;
the one-step variance prediction unit is used for acquiring process noise Q (k-1) at the moment k-1 and performing one-step prediction variance according to the process noise Q (k-1) to obtain a k-moment variance prediction quantity P (k, k-1), wherein P (k, k-1) ═ GP (k-1) G' + Q (k-1), and P (k-1) is k-1 moment variance;
a system state quantity estimation unit for acquiring observation noise R (k) at the time k and predicting quantity according to the state at the time k
Figure FDA0002473700050000036
Estimating a system state quantity at the k moment by using a variance prediction quantity P (k, k-1), an observation variable y (k) and observation noise R (k)
Figure FDA0002473700050000037
Wherein the content of the first and second substances,
Figure FDA0002473700050000038
C＝[1 0]，K(k)＝P(k,k-1)C/[C·P(k,k-1)·C′+R(k)]；
a pitch angle estimation unit for estimating the system state quantity according to the k time
Figure FDA0002473700050000041
And obtaining a pitching angle estimated value of the vehicle at the moment k.
8. The vehicle pitch angle estimation system according to claim 7, wherein the system state quantity estimation unit is further specifically configured to:
determining observation noise R (k) at the moment k according to the first longitudinal acceleration at the moment k and the corresponding relation between the first longitudinal acceleration and the observation noise;
or determining the observation noise R (k) at the time k according to the second longitudinal acceleration at the time k and the corresponding relation between the second longitudinal acceleration and the observation noise.
9. A computer device, comprising: the vehicle pitch angle estimation system according to any one of claims 5-8; or a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the vehicle pitch angle estimation method according to any one of claims 1-4.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when being executed by a processor performs the steps of the vehicle pitch angle estimation method as set forth in any one of claims 1-4.
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