Road slope estimation method and device, electronic equipment, storage medium and vehicle

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses a road gradient estimation method and device, electronic equipment, a storage medium and a vehicle. The method comprises the steps of obtaining at least one kind of vehicle state information; establishing a first vehicle longitudinal dynamic equation based on vehicle longitudinal kinematics, and calculating a first road slope according to the first vehicle longitudinal dynamic equation and the at least one kind of vehicle state information; establishing a second vehicle longitudinal dynamic equation based on the inertial navigation signal, and calculating a second road gradient according to the second vehicle longitudinal dynamic equation and the at least one vehicle state information; and estimating the current driving road slope at least according to the first road slope, the second road slope and a preset weight coefficient group. According to the embodiment of the invention, at least a distributed driving control configuration can be supported, and the real-time performance, the economical efficiency, the stability and the driving condition coverage degree of the whole vehicle control system can be improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method, apparatus, electronic device, storage medium, and vehicle for estimating road gradient. Background Technology

[0002] Electrification of commercial vehicles has excellent prospects in the current domestic and international development context. Many automakers have begun to adopt distributed drive control configurations, which can effectively simplify the transmission system structure, reduce the overall vehicle weight, effectively reduce energy consumption, and improve the layout flexibility of the chassis system.

[0003] In a distributed drive control configuration, each drive wheel has an independent electric drive system. The controller manages the output speed and torque of each drive motor, enabling robust vehicle operation on complex road surfaces and improving vehicle comfort and handling stability. However, this distributed drive configuration places high demands on the accuracy and comprehensiveness of vehicle sensor data. Specifically, vehicle weight and road gradient affect the axle loads, thus influencing torque distribution and control, impacting vehicle stability and fuel economy. These are crucial vehicle parameters and inputs for distributed drive control.

[0004] Based on extensive analysis of real-world driving data, road gradient plays a crucial role in driving safety, fuel economy, and comfort. It not only affects vehicle power performance but also directly impacts dynamic characteristics and driver handling. When traversing steep roads, vehicles are prone to inappropriate acceleration and deceleration, frequent gear shifting, and other issues. This not only increases the risk of traffic accidents but also significantly increases fuel consumption and emissions. Therefore, ensuring the accuracy of real-time road gradient estimation is extremely important. Summary of the Invention

[0005] The purpose of this invention is to provide a road gradient estimation method, device, electronic device, storage medium, and vehicle, which can at least improve the real-time performance, economy, stability, and coverage of driving conditions of the vehicle control system.

[0006] To address the aforementioned technical problems, in a first aspect, the present invention provides a road slope estimation method, comprising at least:

[0007] Obtain at least one type of vehicle status information;

[0008] A first vehicle longitudinal dynamic equation based on vehicle longitudinal kinematics is established, and a first road gradient is calculated based on the first vehicle longitudinal dynamic equation and at least one of the vehicle state information.

[0009] A second vehicle longitudinal dynamic equation based on inertial navigation signals is established, and a second road gradient is calculated based on the second vehicle longitudinal dynamic equation and at least one of the vehicle state information.

[0010] The current driving road slope is estimated based at least on the first road slope, the second road slope, and a preset weighting coefficient group.

[0011] Optionally, the longitudinal dynamic equation of the first vehicle is determined at least by the following means:

[0012] ;

[0013] In the above formula, T tp Indicates the total vehicle drive torque, i g Indicates the gearbox ratio, i0 represents the final drive ratio, and η represents the gearbox ratio. T R represents the transmission efficiency. ω The radius of the wheel is represented by m, the mass of the vehicle is represented by g, the acceleration due to gravity is represented by f, and the rolling resistance coefficient is represented by C. D The values ​​represent the drag coefficient, A represents the frontal area, v represents the vehicle speed, and φ represents the driving speed. Slope The longitudinal slope of the model is represented by δ, the vehicle rotational mass conversion factor is represented by a. x This represents the longitudinal acceleration in the vehicle's own coordinate system.

[0014] Optionally, the gradient of the first road is calculated at least in the following ways:

[0015] ;

[0016] In the above formula, Slope Dyn This indicates the slope of the first road.

[0017] Optionally, the second vehicle longitudinal dynamic equation is determined at least by the following means:

[0018] ;

[0019] In the above formula, a x,IMU a represents the longitudinal acceleration of the vehicle in the global coordinate system based on the IMU. x φ represents the longitudinal acceleration in the vehicle's own coordinate system. slope1 This represents the theoretical slope of the road where the vehicle is currently driving, and g represents the acceleration due to gravity.

[0020] Optionally, the gradient of the second road is calculated at least in the following ways:

[0021] ;

[0022] In the above formula, φ slope This indicates the road gradient where the vehicle is currently driving, a x_Filter,IMU Indicates the acceleration of the filtered IMU, a x_FilterSlope represents the longitudinal acceleration of the filter. Ser This indicates the gradient of the second road.

[0023] Optionally, the current driving road surface gradient is estimated at least in the following ways:

[0024] ;

[0025] In the above formula, Slope Dyn Slope represents the gradient of the first road. Ser The slope represents the second road gradient, the slope represents the current driving road gradient, and k1 and k2 form the preset weight coefficient group.

[0026] Based on the same concept, in a second aspect, the present invention also provides a road slope estimation device for performing the road slope estimation method described in any one of the first aspects;

[0027] The road slope estimation device includes at least:

[0028] The information acquisition module is used to acquire at least one type of vehicle status information;

[0029] The first calculation module is used to establish a first vehicle longitudinal dynamic equation based on the vehicle's longitudinal kinematics, and to calculate a first road slope based on the first vehicle longitudinal dynamic equation and at least one of the vehicle state information.

[0030] The second calculation module is used to establish a second vehicle longitudinal dynamic equation based on inertial navigation signals, and to calculate a second road slope based on the second vehicle longitudinal dynamic equation and at least one of the vehicle state information.

[0031] The slope estimation module is used to estimate the current driving road surface slope based at least on the first road slope, the second road slope, and a preset weighting coefficient group.

[0032] Based on the same concept, in a third aspect, the present invention also provides an electronic device including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the program to implement the steps in the road slope estimation method of any of the first aspects.

[0033] Based on the same concept, in a fourth aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the road slope estimation method of any one of the first aspects.

[0034] Based on the same concept, in a fifth aspect, the present invention also provides a vehicle that integrates at least the road slope estimation device as described in any of the second aspects.

[0035] The technical solution provided by the embodiments of the present invention firstly acquires at least one vehicle state information; further, it establishes a first vehicle longitudinal dynamic equation based on vehicle longitudinal kinematics, and calculates a first road slope based on the first vehicle longitudinal dynamic equation and at least one vehicle state information; further, it establishes a second vehicle longitudinal dynamic equation based on inertial navigation signals, and calculates a second road slope based on the second vehicle longitudinal dynamic equation and at least one vehicle state information; finally, it estimates the current driving road surface slope based at least on the first road slope, the second road slope, and a preset weight coefficient group.

[0036] Therefore, the road slope estimation method of this invention considers parameters such as vehicle speed, tire rolling resistance coefficient, and air resistance coefficient, and estimates the road slope in real time by constructing a vehicle kinematics and longitudinal dynamics model. Simultaneously, to improve the accuracy and stability of the slope estimation algorithm, the estimated road slope must be obtained by fusing the longitudinal road slope calculated by the vehicle dynamics model (i.e., the aforementioned first road slope) and the longitudinal road slope estimated based on IMU sensors (i.e., the aforementioned second road slope). This invention can at least support a distributed drive control configuration, which is beneficial for improving the real-time performance, economy, stability, and coverage of driving conditions of the vehicle control system. Attached Figure Description

[0037] Figure 1 This is a flowchart of a road slope estimation method provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of a road slope estimation device provided in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0041] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0042] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0043] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0044] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0045] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0046] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0047] Figure 1 This is a flowchart of a road slope estimation method provided by an embodiment of the present invention. This embodiment is at least applicable to the slope estimation scenario of new energy distributed drive commercial vehicles. The road slope estimation method can be, but is not limited to, executed by the road slope estimation device in this embodiment of the present invention as the execution subject, and the execution subject can be implemented in software and / or hardware. Figure 1 As shown, this road slope estimation method includes at least the following steps:

[0048] S1. Obtain at least one type of vehicle status information.

[0049] The vehicle status information may include the vehicle's drive torque, final drive ratio, wheel radius, vehicle mass, frontal area, and driving speed.

[0050] S2. Establish the first vehicle longitudinal dynamic equation based on the vehicle's longitudinal kinematics, and calculate the first road slope based on the first vehicle longitudinal dynamic equation and at least one vehicle state information.

[0051] In this context, both passenger cars and commercial vehicles can be considered rigid bodies during motion. The driving torque of the entire vehicle is transmitted to each wheel through the transmission system. Therefore, the vehicle experiences resistance during driving, including rolling resistance, air resistance, gradient resistance, and acceleration resistance. Based on the balance equation formed by the vehicle's driving force and the combined resistance, the longitudinal dynamics driving equation of the vehicle can be established as follows:

[0052] ;

[0053] In the above formula, F t F represents the driving force. r F represents rolling resistance. a F represents air resistance. s F represents slope resistance. j This indicates acceleration resistance.

[0054] More specifically, the calculation methods for the above forces are as follows:

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] .

[0060] Based on this, in one specific implementation, the longitudinal dynamic equation of the first vehicle may optionally be determined at least in the following ways:

[0061] ;

[0062] In the above formula, T tp Indicates the total vehicle drive torque, i g Indicates the gearbox ratio, i0 represents the final drive ratio, and η represents the gearbox ratio. T R represents the transmission efficiency. ω The radius of the wheel is represented by m, the mass of the vehicle is represented by g, the acceleration due to gravity is represented by f, and the rolling resistance coefficient is represented by C. D The values ​​represent the drag coefficient, A represents the frontal area, v represents the vehicle speed, and φ represents the driving speed. Slope The longitudinal slope of the model is represented by rad, δ represents the vehicle rotational mass conversion factor, and ax This represents the longitudinal acceleration in the vehicle's own coordinate system.

[0063] In another specific implementation, the first road gradient may optionally be calculated at least in the following ways:

[0064] ;

[0065] In the above formula, Slope Dyn This indicates the first road gradient (in %).

[0066] Generally, the gradient of roads in regular cities and on highways is relatively small. For example, the gradient of highways in plains and hilly areas is generally 3%, the gradient of Class I highways in plains and hilly areas is generally 4%, the gradient of Class IV highways in plains and hilly areas is generally 5%, the gradient of highways in mountainous and hilly areas is generally 5%, the gradient of Class I highways in mountainous and hilly areas is generally 6%, and the gradient of Class IV highways in mountainous and hilly areas is generally 9%.

[0067] S3. Establish the second vehicle longitudinal dynamic equation based on the inertial navigation signal, and calculate the second road slope based on the second vehicle longitudinal dynamic equation and at least one vehicle state information.

[0068] Among them, the Inertial Navigation System (INS) can detect and measure specific force, acceleration, vibration, angular velocity, and longitudinal / lateral / vertical multi-degree-of-freedom motion attitude information in the vehicle control system without relying on external information and energy. This provides reliable and continuous information on vehicle position, attitude, and acceleration for the vehicle's VCU motion control decisions. For new energy vehicles and intelligent driving vehicles, the more accurate positioning and vehicle status information provided by INS has become essential for new energy vehicles to achieve more precise adaptive torque control in complex driving environments.

[0069] For the road gradient estimation based on inertial navigation system signals in step S3, the main method used is the vehicle's three-axis attitude angles and acceleration measured by the inertial measurement unit (IMU). An IMU typically includes a gyroscope, accelerometers, and magnetometers. The IMU can continuously measure the vehicle's own acceleration in the world coordinate system (a...). x,IMU a y,IMU a z,IMU ) and attitude in its own coordinate system (α) IMU ,β IMU γIMU ).

[0070] Therefore, in another specific implementation, the second vehicle longitudinal dynamic equation may optionally be determined at least in the following manner:

[0071] ;

[0072] In the above formula, a x,IMU a represents the longitudinal acceleration of the vehicle in the global coordinate system based on the IMU. x φ represents the longitudinal acceleration in the vehicle's own coordinate system. slope1 This represents the theoretical slope of the road where the vehicle is currently driving, and g represents the acceleration due to gravity.

[0073] To ensure the smoothness and accuracy of the slope calculation, the following filtering process is required to address the frequent spikes in the acceleration measurement and the unstable raw signal acquired by the IMU sensor.

[0074] The original signal is low-pass filtered; signal value and rate of change are limited, and the specific execution process is as follows:

[0075] Low-pass filtering can be expressed as:

[0076] ;

[0077] In the above formula, K represents the low-pass filter coefficient ( y(k) represents the output of the low-pass filter at time step k, and u(k) represents the input of the low-pass filter at time step k.

[0078] Numerical constraints are expressed as follows:

[0079] ;

[0080] The signal rate of change limit is expressed as:

[0081] ;

[0082] By applying the aforementioned low-pass filtering and output limitation to the acceleration, a relatively usable filtered IMU acceleration and filtered longitudinal acceleration are obtained, and the road gradient based on the inertial navigation system signal (i.e., the second road gradient below) is calculated.

[0083] In yet another specific implementation, the second road gradient may optionally be calculated at least in the following manner:

[0084] ;

[0085] In the above formula, φ slopeThis indicates the road gradient (in rad) where the vehicle is currently driving. x_Filter,IMU Indicates the acceleration of the filtered IMU, a x_Filter Slope represents the longitudinal acceleration of the filter. Ser This indicates the gradient of the second road (in %).

[0086] S4. Estimate the current driving road slope based at least on the first road slope, the second road slope, and the preset weight coefficient group.

[0087] Among them, a proportional coefficient can be set according to the vehicle's driving status to complete the fusion and updating of road slope information based on inertial navigation system signals and road slope information based on vehicle longitudinal dynamics model and vehicle longitudinal kinematics model.

[0088] In yet another specific implementation, the current driving road surface gradient can optionally be estimated at least in the following ways:

[0089] ;

[0090] In the above formula, Slope Dyn Indicates the first road slope. Ser The slope represents the second road gradient, and the slope represents the current driving road gradient. k1 and k2 form a preset weight coefficient group.

[0091] Understandably, k1 and k2 are at least related to the current driving speed, and their settings can be based on the speed MAP to associate weighting coefficients, thereby obtaining the current driving road slope more accurately.

[0092] Furthermore, to ensure the accuracy of the driving slope, logical constraints need to be added to the calculated slope update. Because the IMU sensor is quite sensitive, the measured acceleration values ​​fluctuate significantly. To avoid errors or poor accuracy in the calculated slope, the longitudinal slope estimation update for driving conditions can be processed as follows:

[0093] 1. During the vehicle's initial stage, i.e. when the vehicle speed is less than a certain value (which can be calibrated), the longitudinal slope must remain at the previous estimated slope value and the slope value should not be updated.

[0094] 2. During turning, the estimated longitudinal slope value must remain the same as the value at the previous moment;

[0095] 3. Road slope estimates based on inertial navigation system signals and those based on vehicle longitudinal dynamics and kinematics models will not be updated if they fluctuate frequently and do not converge.

[0096] 3. When the difference between the longitudinal acceleration of the IMU global coordinate system and the longitudinal acceleration of the vehicle body coordinate system is less than the threshold (the initial value can be set to 0.3), the longitudinal slope of the IMU should be zero.

[0097] 4. To prevent fluctuations in IMU longitudinal acceleration or reference vehicle speed from affecting gradient estimation, low-pass filtering is applied to the IMU longitudinal acceleration and reference vehicle speed.

[0098] 5. To prevent fluctuations in longitudinal acceleration or reference vehicle speed calculated from the vehicle's coordinate system from affecting the slope estimation obtained based on dynamics, low-pass filtering is also required for both the vehicle's longitudinal acceleration and the reference vehicle speed.

[0099] 6. When calibrating the MAP by integrating the road longitudinal slope calculated by the vehicle dynamics model and the road longitudinal slope calculated by the IMU sensor, firstly, at low vehicle speeds, the road slope calculated by the vehicle dynamics model accounts for a higher proportion; secondly, at higher vehicle speeds, the road slope calculated by the IMU sensor accounts for a higher proportion.

[0100] The technical solution provided in this embodiment firstly acquires at least one vehicle state information; further, it establishes a first vehicle longitudinal dynamic equation based on vehicle longitudinal kinematics, and calculates a first road slope based on the first vehicle longitudinal dynamic equation and at least one vehicle state information; further, it establishes a second vehicle longitudinal dynamic equation based on inertial navigation signals, and calculates a second road slope based on the second vehicle longitudinal dynamic equation and at least one vehicle state information; finally, it estimates the current driving road surface slope based at least on the first road slope, the second road slope, and a preset weight coefficient group.

[0101] Therefore, the road slope estimation method in this embodiment considers parameters such as vehicle speed, tire rolling resistance coefficient, and air resistance coefficient. It estimates the road slope in real time by constructing vehicle kinematics and longitudinal dynamics models. Furthermore, to improve the accuracy and stability of the slope estimation algorithm, the estimated road slope must be obtained by fusing the longitudinal road slope calculated by the vehicle dynamics model (i.e., the aforementioned first road slope) and the longitudinal road slope estimated based on IMU sensors (i.e., the aforementioned second road slope). This embodiment can at least support a distributed drive control configuration, which is beneficial for improving the real-time performance, economy, stability, and coverage of driving conditions of the vehicle control system.

[0102] It should be noted that compared to traditional centralized commercial vehicles, distributed drive electric commercial vehicles have a simplified structure and higher transmission efficiency, and are considered a better chassis platform for intelligent connected and autonomous vehicles, offering better handling. However, this also presents greater challenges to the dynamic drive control of new energy commercial vehicles. Accurately estimating the vehicle's current state is beneficial for establishing a more accurate vehicle model, improving the handling stability of the entire vehicle control system, and promoting the development of drive control technology for new energy commercial vehicles. This invention proposes an adaptive road slope estimation method for new energy distributed drive commercial vehicles. In addition to the aforementioned beneficial effects, this invention also helps to solve the problems of existing vehicle slope estimation methods being susceptible to changes in road environment and errors between the calibrated and actual values ​​of the drag coefficient.

[0103] Figure 2 This is a schematic diagram of a road slope estimation device provided in an embodiment of the present invention. This embodiment is at least applicable to slope estimation scenarios for new energy distributed drive commercial vehicles. The road slope estimation device can be implemented using software and / or hardware. Figure 2 As shown, the road slope estimation device is used to perform the road slope estimation method of any of the foregoing embodiments or implementations.

[0104] A road slope estimation device includes at least the following:

[0105] Information acquisition module 110 is used to acquire at least one type of vehicle status information;

[0106] The first calculation module 120 is used to establish a first vehicle longitudinal dynamic equation based on the vehicle's longitudinal kinematics, and to calculate a first road slope based on the first vehicle longitudinal dynamic equation and at least one vehicle state information.

[0107] The second calculation module 130 is used to establish a second vehicle longitudinal dynamic equation based on inertial navigation signals, and to calculate the second road slope based on the second vehicle longitudinal dynamic equation and at least one vehicle state information.

[0108] The slope estimation module 140 is used to estimate the current driving road slope based at least on the first road slope, the second road slope, and a preset weight coefficient group.

[0109] Optionally, the longitudinal dynamic equation of the first vehicle is determined at least by the following means:

[0110] ;

[0111] In the above formula, T tp Indicates the total vehicle drive torque, i g Indicates the gearbox ratio, i0 represents the final drive ratio, and η represents the gearbox ratio. T R represents the transmission efficiency. ωThe radius of the wheel is represented by m, the mass of the vehicle is represented by g, the acceleration due to gravity is represented by f, and the rolling resistance coefficient is represented by C. D The values ​​represent the drag coefficient, A represents the frontal area, v represents the vehicle speed, and φ represents the driving speed. Slope The longitudinal slope of the model is represented by δ, the vehicle rotational mass conversion factor is represented by a. x This represents the longitudinal acceleration in the vehicle's own coordinate system.

[0112] Optionally, the gradient of the first road shall be calculated at least in the following ways:

[0113] ;

[0114] In the above formula, Slope Dyn This indicates the gradient of the first road.

[0115] Optionally, the second vehicle longitudinal dynamic equation is determined at least by the following means:

[0116] ;

[0117] In the above formula, a x,IMU a represents the longitudinal acceleration of the vehicle in the global coordinate system based on the IMU. x φ represents the longitudinal acceleration in the vehicle's own coordinate system. slope1 This represents the theoretical slope of the road where the vehicle is currently driving, and g represents the acceleration due to gravity.

[0118] Alternatively, the gradient of the second road may be calculated at least in the following ways:

[0119] ;

[0120] In the above formula, φ slope This indicates the road gradient where the vehicle is currently driving, a x_Filter,IMU Indicates the acceleration of the filtered IMU, a x_Filter Slope represents the longitudinal acceleration of the filter. Ser This indicates the gradient of the second road.

[0121] Alternatively, the current driving road gradient can be estimated at least in the following ways:

[0122] ;

[0123] In the above formula, Slope Dyn Indicates the first road slope. Ser The slope represents the second road gradient, and the slope represents the current driving road gradient. k1 and k2 form a preset weight coefficient group.

[0124] The technical solution provided in this embodiment firstly acquires at least one vehicle state information through an information acquisition module; further, it establishes a first vehicle longitudinal dynamic equation based on the vehicle's longitudinal kinematics through a first calculation module, and calculates a first road slope based on the first vehicle longitudinal dynamic equation and at least one vehicle state information; further, it establishes a second vehicle longitudinal dynamic equation based on inertial navigation signals through a second calculation module, and calculates a second road slope based on the second vehicle longitudinal dynamic equation and at least one vehicle state information; finally, it estimates the current driving road surface slope through a slope estimation module based at least on the first road slope, the second road slope, and a preset weight coefficient group.

[0125] Therefore, the road slope estimation device in this embodiment considers parameters such as vehicle speed, tire rolling resistance coefficient, and air resistance coefficient, and estimates the road slope in real time by constructing a vehicle kinematics and longitudinal dynamics model. Simultaneously, to improve the accuracy and stability of the slope estimation algorithm, the estimated road slope must be obtained by fusing the longitudinal road slope calculated by the vehicle dynamics model (i.e., the aforementioned first road slope) and the longitudinal road slope estimated based on IMU sensors (i.e., the aforementioned second road slope). This embodiment can at least support a distributed drive control configuration, which is beneficial for improving the real-time performance, economy, stability, and coverage of driving conditions of the vehicle control system.

[0126] This embodiment provides an electronic device. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. See also: Figure 3 The electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the road slope estimation methods described above are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other via a communication bus and / or other forms of connection mechanisms (not shown). The memory 1002 stores a computer program executable by the processor. When the electronic device 1000 is running, the processor 1001 executes the computer program to perform the road slope estimation method in any optional implementation of the above embodiments, to at least achieve the following functions: acquiring at least one vehicle state information; establishing a first vehicle longitudinal dynamic equation based on vehicle longitudinal kinematics, and calculating a first road slope based on the first vehicle longitudinal dynamic equation and at least one vehicle state information; establishing a second vehicle longitudinal dynamic equation based on inertial navigation signals, and calculating a second road slope based on the second vehicle longitudinal dynamic equation and at least one vehicle state information; estimating the current driving road surface slope based at least on the first road slope, the second road slope, and a preset weight coefficient set.

[0127] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the road slope estimation method provided in all embodiments of this application: acquiring at least one vehicle state information; establishing a first vehicle longitudinal dynamic equation based on vehicle longitudinal kinematics, and calculating a first road slope based on the first vehicle longitudinal dynamic equation and at least one vehicle state information; establishing a second vehicle longitudinal dynamic equation based on inertial navigation signals, and calculating a second road slope based on the second vehicle longitudinal dynamic equation and at least one vehicle state information; and estimating the current driving road surface slope based at least on the first road slope, the second road slope, and a preset weight coefficient group.

[0128] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0129] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0130] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0131] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] This invention also provides a vehicle that integrates at least the road slope estimation device described in any of the above embodiments or implementations, which will not be repeated here.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating road slope, characterized in that, At least including: Obtain at least one type of vehicle status information; A first vehicle longitudinal dynamic equation based on vehicle longitudinal kinematics is established, and a first road gradient is calculated based on the first vehicle longitudinal dynamic equation and at least one of the vehicle state information. A second vehicle longitudinal dynamic equation based on inertial navigation signals is established, and a second road gradient is calculated based on the second vehicle longitudinal dynamic equation and at least one of the vehicle state information. The current driving road slope is estimated based at least on the first road slope, the second road slope, and a preset weighting coefficient group.

2. The road slope estimation method according to claim 1, characterized in that, The first vehicle longitudinal dynamic equation is determined at least in the following ways: ； In the above formula, T tp Indicates the total vehicle drive torque, i g Indicates the gearbox ratio, i0 represents the final drive ratio, and η represents the gearbox ratio. T R represents the transmission efficiency. ω The radius of the wheel is represented by m, the mass of the vehicle is represented by g, the acceleration due to gravity is represented by f, and the rolling resistance coefficient is represented by C. D The values ​​represent the drag coefficient, A represents the frontal area, v represents the vehicle speed, and φ represents the driving speed. Slope The longitudinal slope of the model is represented by δ, the vehicle rotational mass conversion factor is represented by a. x This represents the longitudinal acceleration in the vehicle's own coordinate system.

3. The road slope estimation method according to claim 2, characterized in that, The gradient of the first road is calculated at least in the following ways: ； In the above formula, Slope Dyn This indicates the slope of the first road.

4. The road slope estimation method according to claim 1, characterized in that, The second vehicle longitudinal dynamic equation is determined at least by the following means: ； In the above formula, a x,IMU a represents the longitudinal acceleration of the vehicle in the global coordinate system based on the IMU. x φ represents the longitudinal acceleration in the vehicle's own coordinate system. slope1 This represents the theoretical slope of the road where the vehicle is currently driving, and g represents the acceleration due to gravity.

5. The road slope estimation method according to claim 4, characterized in that, The gradient of the second road can be calculated at least in the following ways: ； In the above formula, φ slope This indicates the road gradient where the vehicle is currently driving, a x_Filter,IMU Indicates the acceleration of the filtered IMU, a x_Filter Slope represents the longitudinal acceleration of the filter. Ser This indicates the gradient of the second road.

6. The road slope estimation method according to claim 1, characterized in that, The current driving road gradient is estimated at least in the following ways: ； In the above formula, Slope Dyn Slope represents the gradient of the first road. Ser The slope represents the second road gradient, the slope represents the current driving road gradient, and k1 and k2 form the preset weight coefficient group.

7. A road slope estimation device, characterized in that, Used to perform the road slope estimation method according to any one of claims 1-6; The road slope estimation device includes at least: The information acquisition module is used to acquire at least one type of vehicle status information; The first calculation module is used to establish a first vehicle longitudinal dynamic equation based on the vehicle's longitudinal kinematics, and to calculate a first road slope based on the first vehicle longitudinal dynamic equation and at least one of the vehicle state information. The second calculation module is used to establish a second vehicle longitudinal dynamic equation based on inertial navigation signals, and to calculate a second road slope based on the second vehicle longitudinal dynamic equation and at least one of the vehicle state information. The slope estimation module is used to estimate the current driving road surface slope based at least on the first road slope, the second road slope, and a preset weighting coefficient group.

8. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the road slope estimation method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps in the road slope estimation method according to any one of claims 1 to 6.

10. A vehicle, characterized in that, It integrates at least the road slope estimation device as described in claim 7.

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