Gradient estimation method, device and equipment and storage medium

Abstract

The invention discloses a slope estimation method, device and equipment and a storage medium, and the method comprises the steps: introducing a vehicle transverse acceleration under a turning condition, introducing a vehicle vertical acceleration under a pitching condition, fusing the turning condition and the pitching condition, and obtaining a dynamic model of a complex condition; and optimizing Kalman filtering estimation by using the kinetic model of the complex working condition to obtain the slope estimated under the complex working condition. The gradient estimation accuracy under the complex working condition can be improved, and inaccurate gear selection caused by inaccurate gradient estimation is avoided.

Description

Technical Field

[0001] The present invention relates to AMT control technology, and in particular to a slope estimation method, device, equipment and storage medium. Background Art

[0002] In the field of AMT (Automated Mechanical Transmission) control technology for medium- and heavy-duty trucks, slope information is crucial for the vehicle's shift control strategy. Accurate slope estimation enables the vehicle to smoothly negotiate ramps under various road conditions, achieving efficient and stable driving.

[0003] Currently, a common method for estimating slope is to use an accelerometer to collect the vehicle's acceleration in the x-direction (the vehicle's forward direction) and then use a Kalman filter algorithm to estimate the slope. However, in real-world driving, vehicles often encounter various complex driving conditions, and the shortcomings of existing technologies have gradually become apparent. When a vehicle turns on a slope, the vehicle's velocity direction and the x-direction measured by the accelerometer differ. The vehicle's velocity direction is the direction of the front wheels, while the accelerometer, mounted on the vehicle body, measures the x-direction in the direction of the vehicle body (the direction of motion of the rear wheels). This directional discrepancy prevents the basic principles of Kalman filtering from being accurately applied, resulting in significant errors in the estimated slope. Similarly, when a vehicle accelerates or brakes suddenly, the vehicle body pitches. This pitching phenomenon also causes a discrepancy between the vehicle's velocity direction and the measured acceleration direction, further affecting the accurate estimation of the slope. However, because existing technologies fail to fully account for these complex factors, they are unable to accurately estimate the slope under complex driving conditions such as pitching.

[0004] Therefore, how to improve the accuracy of slope estimation under complex working conditions and avoid inaccurate gear selection caused by inaccurate slope estimation is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The main purpose of the present invention is to provide a slope estimation method, device, equipment and storage medium, which can improve the accuracy of slope estimation under complex working conditions and avoid inaccurate gear selection due to inaccurate slope estimation.

[0006] In a first aspect, the present application provides a slope estimation method, the method comprising the steps of: By introducing the vehicle's lateral acceleration in cornering conditions and the vehicle's vertical acceleration in pitching conditions, the cornering and pitching conditions are integrated to obtain a dynamic model for complex conditions. The dynamic model of complex working conditions is used to optimize the Kalman filter estimation and obtain the estimated slope under complex working conditions.

[0007] In combination with the first aspect above, as an optional implementation method, the lateral acceleration of the vehicle is introduced under the turning condition to re-establish the dynamic model under the turning condition, which is: dv / dt=asenx cosδ+aseny sinδ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, aseny is the lateral acceleration of the vehicle, g is the acceleration due to gravity, and α is the slope; Based on the introduction of the vehicle vertical acceleration under the pitching condition, the dynamic model under the pitching condition is re-established, which is: dv / dt=asenx cosβ+asenz sinβ-g sinα, where β is the pitch angle of the vehicle and asenz is the vertical acceleration of the vehicle.

[0008] In combination with the first aspect above, as an optional implementation method, the established dynamic model under the turning condition and the dynamic model under the pitching condition are integrated to obtain a dynamic model for the complex condition; The dynamic model of the complex working condition is: dv / dt=asenx cosδ cosβ+aseny sinδ+asenz sinβ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, β is the pitch angle of the vehicle, aseny is the lateral acceleration of the vehicle, asenz is the vertical acceleration of the vehicle, g is the acceleration of gravity, and α is the slope.

[0009] In combination with the first aspect above, as an optional implementation, the values of asenx, aseny, and asenz are obtained according to a three-axis acceleration sensor installed in a transmission control system TCU; The turning radius of the vehicle is calculated based on the acquired left front wheel speed, right front wheel speed and wheelbase, and the steering angle of the vehicle is calculated based on the turning radius and the acquired wheelbase.

[0010] In combination with the first aspect above, as an optional implementation, according to the formula: , estimate the pitch angle of the vehicle, where is the calibration coefficient, is the rear wheel speed, is the front wheel speed.

[0011] In combination with the first aspect above, as an optional implementation method, a state space model is established based on a vehicle dynamics model; The dynamic model of the complex working condition is input into the state space model to optimize the Kalman filter estimation to obtain the slope under the complex working condition.

[0012] In combination with the first aspect above, as an optional implementation, the state space model is: x(k+1)=Ax(k)+Bu y(k+1)=Cx(k)+Du Where x(k+1) is the state of the vehicle at time k+1, x(k) is the state of the vehicle at time k, y(k+1) is the speed at time k+1, y(k) is the speed at time k, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, D is the direct transmission matrix, u is the input, u=asenx cosδ cosβ+aseny sinδ+asenz sinβ.

[0013] In a second aspect, the present application provides a slope estimation device, the device comprising: A processing module, which is used to integrate the turning and pitching conditions based on the introduction of the vehicle's lateral acceleration during cornering and the vertical acceleration during pitching to obtain a dynamic model for complex conditions; The estimation module is used to optimize the Kalman filter estimation using the dynamic model of complex working conditions to obtain the estimated slope under complex working conditions.

[0014] In a third aspect, the present application further provides an electronic device comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the first aspects is implemented.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing computer program instructions, which, when executed by a computer, enables the computer to execute any one of the methods described in the first aspect.

[0016] This application provides a slope estimation method, device, equipment, and storage medium. The method includes the following steps: integrating the vehicle's lateral acceleration under turning conditions and its vertical acceleration under pitching conditions to obtain a dynamic model for complex conditions; and optimizing Kalman filter estimation using the dynamic model for complex conditions to obtain an estimated slope under these conditions. This application can improve the accuracy of slope estimation under complex conditions and avoid inaccurate gear selection caused by inaccurate slope estimation.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] Figure 1 This is a flow chart of a slope estimation method provided in an embodiment of the present application; Figure 2 A schematic diagram of a slope estimation device provided in an embodiment of the present application; Figure 3 A schematic diagram of a vehicle dynamics model under a turning condition provided in an embodiment of the present application; Figure 4 A schematic diagram of a vehicle dynamics model under a pitching condition provided in an embodiment of the present application; Figure 5 This is a schematic diagram of a vehicle on a slope provided in an embodiment of the present application; Figure 6 A schematic diagram of an electronic device provided in an embodiment of the present application; Figure 7 A schematic diagram of a computer-readable program medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0021] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the blocks shown in the drawings are functional entities that do not necessarily correspond to physically or logically separate entities.

[0022] The embodiments of the present application provide a slope estimation method, apparatus, device and storage medium, which can improve the accuracy of slope estimation under complex working conditions and avoid inaccurate gear selection caused by inaccurate slope estimation.

[0023] To achieve the above technical effects, the general ideas of this application are as follows: A slope estimation method, comprising the steps of: S101: Based on the introduction of the vehicle's lateral acceleration in the cornering condition and the introduction of the vehicle's vertical acceleration in the pitching condition, the cornering condition and the pitching condition are integrated to obtain a dynamic model of the complex condition.

[0024] S102: Optimizing the Kalman filter estimation using the dynamic model of the complex working condition to obtain an estimated slope under the complex working condition.

[0025] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0026] Reference Figure 1 , Figure 1 The figure shows a flow chart of a slope estimation method provided by the present invention. Figure 1 As shown, the method includes the steps of: Step S101: Based on the introduction of the vehicle's lateral acceleration during cornering and the vehicle's vertical acceleration during pitching, the cornering and pitching conditions are integrated to obtain a dynamic model for the complex condition. It should be noted that cornering is a vehicle yaw condition.

[0027] Specifically, before integrating the turning and pitching conditions to obtain a dynamic model for complex conditions, the following steps are required: Based on the introduction of vehicle lateral acceleration under turning conditions, the dynamic model under turning conditions is re-established, which is: dv / dt=asenx cosδ+aseny sinδ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, aseny is the lateral acceleration of the vehicle, g is the acceleration due to gravity, and α is the slope; Based on the introduction of the vehicle vertical acceleration under the pitching condition, the dynamic model under the pitching condition is re-established, which is: dv / dt=asenx cosβ+asenz sinβ-g sinα, where β is the pitch angle of the vehicle and asenz is the vertical acceleration of the vehicle. It should be explained that the pitch condition includes sudden braking and sudden acceleration. The dynamic model under the turning condition and the dynamic model under the pitching condition are integrated to obtain the dynamic model under the complex condition. The dynamic model of the complex working condition is: dv / dt=asenx cosδ cosβ+aseny sinδ+asenz sinβ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, β is the pitch angle of the vehicle, aseny is the lateral acceleration of the vehicle, asenz is the vertical acceleration of the vehicle, g is the acceleration of gravity, and α is the slope.

[0028] Among them, the values of asenx, aseny, and asenz are obtained according to the three-axis acceleration sensor installed in the transmission control system TCU; The turning radius of the vehicle is calculated based on the acquired left front wheel speed, right front wheel speed and wheelbase, and the steering angle of the vehicle is calculated based on the turning radius and the acquired wheelbase.

[0029] According to the formula: , estimate the pitch angle of the vehicle, where is the calibration coefficient, is the rear wheel speed, is the front wheel speed.

[0030] It should be explained that when a vehicle is on a slope and turning at the same time, the direction of the vehicle's speed is the direction of movement of the front wheels, and the acceleration sensor is installed on the vehicle body, and the measured x-direction is the direction of the vehicle body (the direction of movement of the rear wheels). At this time, the direction of the vehicle speed and the direction of the measured acceleration are different, and the basic principle of Kalman filter estimation is no longer consistent, so the estimated slope will be inaccurate.

[0031] When the vehicle accelerates or brakes suddenly, the vehicle body will pitch, which will also cause the direction of vehicle speed and measured acceleration to be different, resulting in inaccurate estimated slope. For easy understanding, see the example based on the turning condition. Figure 3 When turning, the forces acting on the vehicle are the traction in the direction of the speed and the centripetal force pointing toward the center of the turn. At this time, the direction of the vehicle's speed is the direction of the front wheels, and the angle between it and the direction of the vehicle body is the steering angle δ. Under this condition, the vehicle's lateral acceleration aseny needs to be introduced to establish the dynamic equation in the direction of the vehicle's speed: dv / dt=asenx cosδ+aseny sinδ-g sinα.

[0032] Based on the vehicle pitch condition, see Figure 4 When the vehicle brakes heavily, the body will tilt forward, and the force on the vehicle is the braking force in the direction of the speed. At this time, the direction of the vehicle speed is horizontal to the road surface, and the pitch angle of the vehicle is β. Under this working condition, the vertical acceleration of the vehicle needs to be introduced assenz to establish the dynamic equation in the direction of the speed: dv / dt=asenx cosβ+asenz sinβ-g sinα.

[0033] Combining these two working conditions, we can get the vehicle dynamics equation: dv / dt=asenx cosδ cosβ+aseny sinδ+asenz sinβ-g sinα.

[0034] The steering angle is calculated according to the Ackerman formula: tanδ=L / R, where L is the vehicle wheelbase and R is the turning radius. , where b is the wheelbase, is the left front wheel speed, is the right front wheel speed.

[0035] Estimating the pitch angle based on the wheel speed difference: During braking, the front axle load increases, causing the wheel speed to decrease, while the rear axle load increases, causing the wheel speed to increase. The wheel speed difference and the pitch angle are positively correlated.

[0036] According to the formula: , estimate the pitch angle of the vehicle, where is the calibration coefficient, is the rear wheel speed, is the front wheel speed.

[0037] The acceleration asenx of multi-sensor fusion cosδ cosβ+aseny sinδ+asenz By using sinβ as the acceleration input of the Kalman filter, an estimated slope that is suitable for multiple working conditions can be obtained, which can significantly improve the calculation accuracy of complex working conditions.

[0038] Step S102: Optimize the Kalman filter estimation using the dynamic model of the complex working condition to obtain the estimated slope under the complex working condition.

[0039] Specifically, based on the vehicle dynamics model, a state space model is established; The dynamic model of the complex working condition is input into the state space model to optimize the Kalman filter estimation to obtain the slope under the complex working condition.

[0040] The state space model is: x(k+1)=Ax(k)+Bu y(k+1)=Cx(k)+Du Where x(k+1) is the state of the vehicle at time k+1, x(k) is the state of the vehicle at time k, y(k+1) is the speed at time k+1, y(k) is the speed at time k, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, D is the direct transmission matrix, u is the input, u=asenx cosδ cosβ+aseny sinδ+asenz What needs to be explained is that x=[v;sinα] is the vehicle speed and slope, y=v is the vehicle speed, that is, u is the input, x is the state, and y is the output.

[0041] For an example to help you understand, see Figure 5 , define input u = asenx , state x = [v; sinα] , and output y = v , where asenx is the vehicle's longitudinal acceleration. In this method, it is the final value after integrating the x, y, and z-axis accelerations, or the total acceleration of the x, y, and z axes in the velocity direction. v is the vehicle speed, and α is the slope.

[0042] Based on the vehicle's dynamic model, a state space model of the vehicle under special working conditions is established: x(k+1)=Ax(k)+Bu; y(k+1)=Cx(k)+Du, where x(k+1) is the vehicle state at time k+1, x(k) is the vehicle state at time k, y(k+1) is the vehicle speed at time k+1, and y(k) is the vehicle speed at time k. To discuss the change in state, v(k+1)=v+asenx in the x(k+1) state Tg sinα Tsinα(k+1)=sinα(k) The slope angle at the next moment can be considered to be equal to the previous moment. That is, A=[1 -g T; 0 1] B=[T;0]C=[1 0] D=0 Where T is the period, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, and D is the direct transmission matrix. Only by getting the input u can we get the estimated value of the slope. According to the theoretical analysis above, input u= asenx cosδ cosβ+aseny sinδ+asenz sinβ, asenx, aseny, and asenz are the sensor accelerations of the x, y, and z axes, which are obtained from the three-axis acceleration sensor installed in the transmission control system TCU. x and z need to be self-learned and corrected according to the installation angle of the transmission. The correction method is: on a straight road, when the vehicle is stationary, calibrate asenx and asenz to 0, that is, asenx-asenxoffset=0, asenz-asenzoffset=0, and obtain the values of asenxoffset and asenzoffset, that is, the self-learning deviation values, which are stored in the EEPROM.

[0043] To summarize, by introducing the vehicle lateral acceleration aseny under cornering conditions and re-establishing the dynamic model under this condition, the estimated slope under this condition can be accurately obtained. By introducing the vehicle vertical acceleration asenz under sudden acceleration or sudden braking conditions and re-establishing the dynamic model under this condition, the estimated slope under this condition can be accurately obtained. By integrating the cornering condition with the sudden acceleration and sudden braking conditions and establishing the dynamic model of the vehicle under complex conditions, a more accurate estimated slope under complex conditions can be obtained.

[0044] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a slope estimation device provided by the present invention, as shown in FIG. Figure 2 As shown, the device includes: Processing module 201 is used to introduce the vehicle lateral acceleration under the turning condition and the vehicle vertical acceleration under the pitching condition, and integrate the turning condition and the pitching condition to obtain a dynamic model of the complex condition.

[0045] The estimation module 202 is used to optimize the Kalman filter estimation using the dynamic model of the complex working condition to obtain the estimated slope under the complex working condition.

[0046] Furthermore, in a possible implementation, the processing module is further configured to introduce the vehicle lateral acceleration under the turning condition to re-establish the dynamic model under the turning condition, which is: dv / dt=asenx cosδ+aseny sinδ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, aseny is the lateral acceleration of the vehicle, g is the acceleration due to gravity, and α is the slope; Based on the introduction of the vehicle vertical acceleration under the pitching condition, the dynamic model under the pitching condition is re-established, which is: dv / dt=asenx cosβ+asenz sinβ-g sinα, where β is the pitch angle of the vehicle and asenz is the vertical acceleration of the vehicle.

[0047] Furthermore, in a possible implementation, the processing module is further configured to fuse the established dynamic model under a turning condition and the established dynamic model under a pitching condition to obtain a dynamic model under a complex condition; The dynamic model of the complex working condition is: dv / dt=asenx cosδ cosβ+aseny sinδ+asenz sinβ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, β is the pitch angle of the vehicle, aseny is the lateral acceleration of the vehicle, asenz is the vertical acceleration of the vehicle, g is the acceleration of gravity, and α is the slope.

[0048] Furthermore, in a possible implementation, the processing module is further configured to obtain the values of asenx, aseny, and asenz based on a three-axis acceleration sensor installed in a transmission control system TCU; The turning radius of the vehicle is calculated based on the acquired left front wheel speed, right front wheel speed and wheelbase, and the steering angle of the vehicle is calculated based on the turning radius and the acquired wheelbase.

[0049] Furthermore, in a possible implementation manner, the processing module is further configured to calculate the output signal according to the formula: , estimate the pitch angle of the vehicle, where is the calibration coefficient, is the rear wheel speed, is the front wheel speed.

[0050] Furthermore, in a possible implementation, the estimation module is further configured to establish a state space model based on the vehicle dynamics model; The dynamic model of the complex working condition is input into the state space model to optimize the Kalman filter estimation to obtain the slope under the complex working condition.

[0051] Furthermore, in a possible implementation, the estimation module is further configured to optimize the Kalman filter estimation result using the state space model, and the expression is: x(k+1)=Ax(k)+Bu y(k+1)=Cx(k)+Du Where x(k+1) is the state of the vehicle at time k+1, x(k) is the state of the vehicle at time k, y(k+1) is the speed at time k+1, y(k) is the speed at time k, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, D is the direct transmission matrix, u is the input, u=asenx cosδ cosβ+aseny sinδ+asenz sinβ.

[0052] Reference Figure 3 , Figure 3 The figure shows a schematic diagram of the vehicle dynamics model under the turning condition provided by the present invention. Figure 3 As shown: Based on the turning conditions, such as Figure 3 When turning, the forces acting on the vehicle are the traction in the direction of the speed and the centripetal force pointing toward the center of the turn. At this time, the direction of the vehicle's speed is the direction of the front wheels, and the angle between it and the direction of the vehicle body is the steering angle δ. Under this condition, the vehicle's lateral acceleration aseny needs to be introduced to establish the dynamic equation in the direction of the vehicle's speed: dv / dt=asenx cosδ+aseny sinδ-g sinα.

[0053] Reference Figure 4 , Figure 4 FIG. 1 is a schematic diagram of a vehicle dynamics model under pitching conditions provided by the present invention, as shown in FIG. Figure 4 As shown: Based on the vehicle pitch condition, such as Figure 4 When the vehicle brakes heavily, the body will tilt forward, and the force on the vehicle is the braking force in the direction of the speed. At this time, the direction of the vehicle speed is horizontal to the road surface, and the pitch angle of the vehicle is β. Under this working condition, the vertical acceleration of the vehicle needs to be introduced to establish the dynamic equation in the direction of the speed: dv / dt=asenx cosβ+asenz sinβ-g sinα Reference Figure 5 , Figure 5 FIG. 1 is a schematic diagram of a vehicle provided by the present invention on a slope, as shown in FIG. Figure 5 As shown: Define input u=asenx, state x=[v; sinα], output y=v, where asenx is the longitudinal acceleration of the vehicle. In this method, it is the final value after integrating the accelerations of the x, y, and z axes, or it can be understood as the total acceleration of x, y, and z in the velocity direction. v is the vehicle speed, and α is the slope. Based on the vehicle's dynamic model, a state space model of the vehicle under special working conditions is established: x(k+1)=Ax(k)+Bu; y(k+1)=Cx(k)+Du, where x(k+1) is the state of the vehicle at time k+1, x(k) is the state of the vehicle at time k, y(k+1) is the vehicle speed at time k+1, and y(k) is the vehicle speed at time k. Discuss the change in state. In the x(k+1) state, v(k+1)=v+asenx Tg sinα Tsinα(k+1)=sinα(k) The slope angle at the next moment can be considered to be equal to the previous moment. That is, A=[1 -g T; 0 1] B=[T;0]C=[1 0] D=0 where T is the period, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, and D is the direct transmission matrix.

[0054] By getting the input u, we can get the estimated value of the slope. According to the theoretical analysis above, input u = asenx cosδ cosβ+aseny sinδ+asenz sinβ asenx, aseny, and asenz are the sensor accelerations of the x, y, and z axes, which are obtained based on the three-axis acceleration sensor installed in the transmission control system TCU. x and z need to be self-learned and corrected based on the installation angle of the transmission. The correction method is: on a straight road, when the vehicle is stationary, calibrate asenx and asenz to 0, that is, asenx-asenxoffset=0, asenz-asenzoffset=0, and obtain the values of asenxoffset and asenzoffset, which are also self-learning deviation values, and store the deviation values in the EEPROM.

[0055] The steering angle is calculated according to the Ackerman formula: tanδ=L / R, where L is the vehicle wheelbase and R is the turning radius. , where b is the wheelbase, is the left front wheel speed, is the right front wheel speed.

[0056] Estimating the pitch angle based on the wheel speed difference: During braking, the front axle load increases, causing the wheel speed to decrease, while the rear axle load increases, causing the wheel speed to increase. The wheel speed difference and the pitch angle are positively correlated.

[0057] According to the formula: , estimate the pitch angle of the vehicle, where is the calibration coefficient, is the rear wheel speed, The front wheel speed is obtained from the wheel speed sensor.

[0058] To summarize, this application introduces the vehicle lateral acceleration aseny under cornering conditions and re-establishes the dynamic model under these conditions, which can accurately obtain the estimated slope under these conditions. It introduces the vehicle vertical acceleration asenz under sudden acceleration or sudden braking conditions (pitch conditions) and re-establishes the dynamic model under these conditions, which can accurately obtain the estimated slope under these conditions.

[0059] By integrating turning conditions and pitching conditions and establishing a dynamic model of the vehicle under complex conditions, a more accurate estimated slope can be obtained under complex conditions, thereby improving the accuracy of slope estimation under complex conditions and avoiding inaccurate gear selection caused by inaccurate slope estimation.

[0060] Refer to the following Figure 4 An electronic device 400 according to this embodiment of the present invention will be described. Figure 4 The electronic device 400 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0061] like Figure 6 As shown, electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, the aforementioned at least one processing unit 610, the aforementioned at least one storage unit 620, and a bus 630 connecting various system components (including storage unit 620 and processing unit 610).

[0062] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above “Example Method” section of this specification.

[0063] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 621 and / or a cache memory unit 622 , and may further include a read-only memory unit (ROM) 623 .

[0064] The storage unit 620 may also include a program / utility 624 having a set (at least one) of program modules 625, such program modules 625 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0065] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0066] The electronic device 600 can also communicate with one or more external devices (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). This communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0067] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions for enabling a computing device (such as a personal computer, server, terminal device, or network device) to execute the methods according to the embodiments of the present disclosure.

[0068] According to the solution of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above-mentioned method of this specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to perform the steps according to various exemplary embodiments of the present invention described in the "Exemplary Methods" section of this specification.

[0069] refer to Figure 7 , a program product 700 for implementing the above-described method according to an embodiment of the present invention is described. The program product 700 may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0070] The program product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0071] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0072] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0073] Program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0074] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0075] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

Claims

1. A slope estimation method, characterized in that: include: By introducing the vehicle's lateral acceleration in cornering conditions and the vehicle's vertical acceleration in pitching conditions, the cornering and pitching conditions are integrated to obtain a dynamic model for complex conditions. The dynamic model of complex working conditions is used to optimize the Kalman filter estimation and obtain the estimated slope under complex working conditions.

2. The method according to claim 1, characterized in that Before fusing the turning condition and the pitching condition to obtain the dynamic model of the complex condition, the following steps are included: Based on the introduction of vehicle lateral acceleration under turning conditions, the dynamic model under turning conditions is re-established, which is: dv / dt=asenx cosδ+aseny sinδ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, aseny is the lateral acceleration of the vehicle, g is the acceleration due to gravity, and α is the slope; Based on the introduction of the vehicle vertical acceleration under the pitching condition, the dynamic model under the pitching condition is re-established, which is: dv / dt=asenx cosβ+asenz sinβ-g sinα, where β is the pitch angle of the vehicle and asenz is the vertical acceleration of the vehicle.

3. The method according to claim 1, characterized in that The turning condition and the pitching condition are integrated to obtain a dynamic model of a complex condition, including: The dynamic model under the turning condition and the dynamic model under the pitching condition are integrated to obtain the dynamic model under the complex condition. The dynamic model of the complex working condition is: dv / dt=asenx cosδ cosβ+aseny sinδ+asenz sinβ-g sinα, where asenx is the longitudinal acceleration of the vehicle read by the sensor, δ is the steering angle, β is the pitch angle of the vehicle, aseny is the lateral acceleration of the vehicle, asenz is the vertical acceleration of the vehicle, g is the acceleration of gravity, and α is the slope.

4. The method according to claim 3, characterized in that include: According to the three-axis acceleration sensor installed in the transmission control system TCU, the values of asenx, aseny, and asenz are obtained; The turning radius of the vehicle is calculated based on the acquired left front wheel speed, right front wheel speed and wheelbase, and the steering angle of the vehicle is calculated based on the turning radius and the acquired wheelbase.

5. The method according to claim 3, characterized in that include: According to the formula: , estimate the pitch angle of the vehicle, where is the calibration coefficient, is the rear wheel speed, is the front wheel speed.

6. The method according to claim 1, characterized in that The method of optimizing the Kalman filter estimation using the dynamic model of complex working conditions to obtain the estimated slope under complex working conditions includes: Based on the vehicle dynamics model, a state space model is established; The dynamic model of the complex working condition is input into the state space model to optimize the Kalman filter estimation to obtain the slope under the complex working condition.

7. The method according to claim 6, characterized in that include: The state space model is: x(k+1)=Ax(k)+Bu y(k+1)=Cx(k)+Du Where x(k+1) is the state of the vehicle at time k+1, x(k) is the state of the vehicle at time k, y(k+1) is the speed at time k+1, y(k) is the speed at time k, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, D is the direct transmission matrix, u is the input, u=asenx cosδ cosβ+aseny sinδ+asenz sinβ.

8. A slope estimation device, characterized in that: include: A processing module, which is used to integrate the turning and pitching conditions based on the introduction of the vehicle's lateral acceleration during cornering and the vertical acceleration during pitching to obtain a dynamic model for complex conditions; The estimation module is used to optimize the Kalman filter estimation using the dynamic model of complex working conditions to obtain the estimated slope under complex working conditions.

9. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer program instructions are stored therein, and when the computer program instructions are executed by a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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