WO2024093016A1

WO2024093016A1 - Vehicle weight estimation method and apparatus, storage medium and engineering vehicle

Info

Publication number: WO2024093016A1
Application number: PCT/CN2023/070302
Authority: WO
Inventors: 王吕俊; 卢玉求; 于松林
Original assignee: 三一专用汽车有限责任公司
Priority date: 2022-10-31
Filing date: 2023-01-04
Publication date: 2024-05-10
Also published as: CN115659662A

Abstract

The present application provides a vehicle weight estimation method and apparatus, a storage medium and an engineering vehicle. The specific implementation solution comprises: constructing a vehicle dynamics model by using vehicle data of a target vehicle; on the basis of the vehicle dynamics model, determining a corresponding difference equation; on the basis of the difference equation, determining corresponding difference equation coefficients and a covariance matrix; and estimating the vehicle weight of the target vehicle by using the difference equation coefficients and the covariance matrix. The technical solution of the present application can effectively improve the accuracy of estimating the vehicle weight.

Description

Vehicle weight estimation method, device, storage medium and engineering vehicle

Technical Field

The present application relates to the technical field of engineering machinery, and in particular to a vehicle weight estimation method, device, storage medium and engineering vehicle.

Background of the Invention

At present, due to the large mass variation and complex working conditions of the mixer truck, the weight of the mixer truck with automatic transmission seriously affects the starting and shifting strategies of the mixer truck. In the related technology, the weight of the mixer truck is usually measured with the help of some external measuring equipment, which cannot accurately estimate the weight of the mixer truck.

Summary of the invention

In order to solve the above problems, the present application proposes a vehicle weight estimation method, device, storage medium and engineering vehicle, which can effectively improve the accuracy of vehicle weight estimation.

In a first aspect, an embodiment of the present application provides a method for estimating vehicle weight, comprising: constructing a vehicle dynamics model using whole vehicle data of a target vehicle; determining a corresponding differential equation based on the vehicle dynamics model; determining corresponding differential equation coefficients and a covariance matrix based on the differential equation; and estimating the vehicle weight of the target vehicle using the differential equation coefficients and the covariance matrix.

In a second aspect, an embodiment of the present application provides a vehicle weight estimation device, comprising: a construction module for constructing a vehicle dynamics model using the whole vehicle data of a target vehicle; a determination module for determining a corresponding differential equation based on the vehicle dynamics model; a processing module for determining corresponding differential equation coefficients and a covariance matrix based on the differential equation; and an estimation module for estimating the vehicle weight of the target vehicle using the differential equation coefficients and the covariance matrix.

In a third aspect, an embodiment of the present application provides an engineering vehicle, comprising: a control device, wherein the control device is used to implement the above-mentioned vehicle weight estimation method.

In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned vehicle weight estimation method is implemented.

One embodiment of the above application has the following advantages or beneficial effects:

The differential equation is determined based on the vehicle dynamics model, the differential variance coefficient and the covariance matrix are determined through the differential equation, and the vehicle weight is estimated using the differential variance coefficient and the covariance matrix. In this way, the vehicle weight can be accurately evaluated without the help of external equipment.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

FIG1 is a flow chart of a method for estimating vehicle weight provided in one embodiment of the present application.

FIG2 is a flow chart of a method for estimating vehicle weight provided in accordance with an embodiment of the present application.

FIG. 3 is a schematic diagram of a covariance matrix provided in an embodiment of the present application.

FIG. 4 is a schematic diagram of differential equation coefficients provided in an embodiment of the present application.

FIG. 5 is a schematic diagram of a specific flow chart of a vehicle weight estimation method provided in an embodiment of the present application.

FIG6 is a schematic diagram of the structure of a vehicle weight estimation device provided in one embodiment of the present application.

FIG. 7 is a schematic structural diagram of an engineering vehicle provided in one embodiment of the present application.

FIG8 is a schematic diagram of the structure of a control device provided in an embodiment of the present application.

Methods of implementing this application

The technical solution of the embodiment of the present application is suitable for use in scenarios where vehicle weight is detected, such as a mixer truck, etc. The technical solution of the embodiment of the present application can be used to estimate the vehicle weight more accurately.

The technical solution of the embodiment of the present application can be exemplarily applied to hardware devices such as processors, electronic devices, servers (including cloud servers), or packaged into software programs to be run. When the hardware device executes the processing of the technical solution of the embodiment of the present application, or the above software program is run, the purpose of estimating the vehicle weight according to the differential equation determined by the vehicle dynamics model can be achieved. The embodiment of the present application only exemplarily introduces the specific processing of the technical solution of the present application, and does not limit the specific implementation form of the technical solution of the present application. Any technical implementation form that can execute the processing of the technical solution of the present application can be adopted by the embodiment of the present application.

The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

Fig. 1 is a flow chart of a method for estimating vehicle weight according to an embodiment of the present application. In an exemplary embodiment, the method for estimating vehicle weight specifically includes the following steps.

S110: construct a vehicle dynamics model using the whole vehicle data of the target vehicle.

S120. Determine a corresponding differential equation based on the vehicle dynamics model.

S130. Determine corresponding differential equation coefficients and covariance matrix based on the differential equation.

S140: Estimate the vehicle weight of the target vehicle using the differential equation coefficients and the covariance matrix.

In step S110, illustratively, the target vehicle can be a specified vehicle or any vehicle. In the present embodiment, the type of the target vehicle is a mixer truck, and it can also be other types of vehicles, which are not limited here. The whole vehicle data is used to represent the data collected when the target vehicle is driving. Optionally, the whole vehicle data can be filtered data or data before filtering. The whole vehicle data can include: vehicle wheel end driving force, road slope, vehicle driving speed, etc. Optionally, the whole vehicle data can be directly detected or detected by other sensors. The vehicle dynamics model is used to represent the driving state of the vehicle, wherein the vehicle dynamics model is a function including vehicle weight, specifically composed of vehicle wheel end driving force, vehicle weight, gravitational acceleration, road slope, rolling resistance coefficient, wind resistance coefficient, frontal area, rotational mass conversion coefficient, vehicle driving speed, and vehicle acceleration.

In step S120, illustratively, the differential equation is an equation containing the differential of the unknown function and the independent variable. In this embodiment, the differential equation is an equation about the vehicle acceleration, slope, and the vehicle wheel-end driving force. Specifically, the vehicle dynamics model is converted into a differential equation by mathematical transformation to achieve the purpose of solving the differential equation to obtain an approximate solution of the vehicle dynamics model, so as to discretize the continuous problem. Among them, the mathematical transformation includes: linearization, Laplace transform, forward difference, backward difference, Z-inverse transform, etc.

In step S130, illustratively, the differential equation coefficients are used to represent the coefficients of each parameter in the differential equation. Optionally, a differential equation coefficient is set for each parameter in the differential equation, and the differential equation coefficients may be the same or different. Optionally, the differential equation coefficient may be a coefficient including the vehicle weight, for example, if A=0.5*m, wherein A is the differential equation coefficient and m is the vehicle weight. Specifically, each differential equation may determine multiple sets of differential equation coefficients. The covariance matrix represents the pairwise linear correlation between a set of random variables. Optionally, the covariance matrix may include vehicle acceleration. Specifically, the differential equation may be simulated according to the simulation software to obtain the covariance matrix and differential equation coefficients of the differential equation.

In step S140, illustratively, since the vehicle dynamics model is a function including the vehicle weight, and the differential equation is converted from the vehicle dynamics model, the vehicle weight of the target vehicle can be directly obtained by calculating the vehicle dynamics model or the differential equation according to the differential equation coefficients and the covariance matrix. Alternatively, when the vehicle dynamics model is converted into the differential equation, the differential equation coefficients are represented by the vehicle weight. In this way, the vehicle weight can be estimated by selecting appropriate differential equation coefficients according to the covariance matrix.

In the technical solution of the present application, a differential equation is determined based on a vehicle dynamics model, a differential variance coefficient and a covariance matrix are determined by the differential equation, and then the vehicle weight is estimated using the differential variance coefficient and the covariance matrix, so that the vehicle weight can be accurately evaluated without the aid of external equipment. Moreover, since the differential equation converges quickly, not only can the vehicle weight be estimated more accurately, but the speed of estimating the vehicle weight can also be increased.

In one embodiment, as shown in FIG. 2 , the method of estimating the vehicle weight of the target vehicle by using the differential equation coefficients and the covariance matrix includes the following steps.

S210, determining a minimum variance value of vehicle acceleration based on the covariance matrix;

S220. Calculate the vehicle weight based on the differential equation coefficient corresponding to the minimum variance of the vehicle acceleration and the differential equation.

Exemplarily, as shown in Figures 3 and 4, since the elements on the diagonal of the covariance matrix are the variances of acceleration, and the smaller the variance, the more accurate the vehicle weight estimation, the minimum variance is selected from the elements on the diagonal, and the differential equation coefficients corresponding to the minimum variance are determined. In this way, the vehicle weight calculated according to the above differential equation coefficients and the differential equation is more accurate.

In one embodiment, constructing a vehicle dynamics model using the whole vehicle data includes: determining the operating state of the target vehicle based on the whole vehicle data; and constructing the vehicle dynamics model when the operating state satisfies a preset vehicle operating condition.

Exemplarily, the operating state of the target vehicle may be determined based on one or more vehicle data collected in real time, or may be determined based on the result of calculation of multiple vehicle data. The operating state may include: braking state, parking state, starting state, idling state, driving state, etc. Optionally, the driving state may include: normal driving state and abnormal driving state. Optionally, the abnormal driving state includes: the vehicle acceleration is greater than a preset first threshold, the vehicle acceleration is less than a preset second threshold, or the slope is greater than a preset third threshold. The preset first threshold, the preset second threshold, and the preset third threshold may be set according to actual needs and are not limited here.

Exemplarily, the preset vehicle operating condition is used to indicate that the target vehicle is in a stable operating state. Optionally, the vehicle operating condition may be one condition or multiple conditions. Specifically, the corresponding vehicle operating condition may be set according to the operating state.

Specifically, since the clutch torque cannot be accurately obtained during the vehicle's gear shifting process, the vehicle weight cannot be accurately estimated in the parking state and the 0 throttle process (i.e., the idle state). Also, due to the inaccuracy of the wheel-end driving force of the vehicle during the braking process, the vehicle weight cannot be accurately estimated during the braking process (i.e., the braking state). The vehicle weight cannot be accurately estimated if the vehicle acceleration is too large or too small, or if the slope is too large. It is understandable that the above situation makes it impossible for the target vehicle to drive normally, so when estimating the vehicle weight, it will cause abnormal vehicle weight jumps. In this way, the preset vehicle operating conditions may include: the operating state is not any one of the parking state, braking state, idle state, and abnormal driving state.

In this embodiment, the running state that meets the preset vehicle running conditions is set to the corresponding first flag bit in advance, and the running state that does not meet the preset vehicle running conditions is set to the corresponding second flag bit. The first flag bit can be 1, and the second flag bit can be 0, or can be set according to actual needs, so that the staff can be notified in time that the current vehicle is in an unstable working condition.

After obtaining the vehicle data, determine the vehicle's operating state. If the vehicle's operating state is not any of the parking state, braking state, idling state, and abnormal driving state, it means that the vehicle is in a stable working condition, and the output flag is 1. If the vehicle's operating state is any of the parking state, braking state, idling state, and abnormal driving state, it means that the vehicle is in an unstable working condition, and the output flag is 0, so that the vehicle's working condition can be screened, and stable working conditions can be screened out to avoid the problem of large vehicle weight fluctuations, thereby making the vehicle weight estimation more accurate.

In one implementation, determining the corresponding differential equation based on the vehicle dynamics model includes: utilizing a linear transformation to process the vehicle dynamics model to obtain the differential equation.

Illustratively, the linear transformation may include: Laplace transformation, forward difference method and Z-inverse transformation, etc. Specifically, the vehicle dynamics model may be transformed by one linear transformation method, or the vehicle dynamics model may be transformed by combining multiple linear transformation methods to obtain a differential equation.

Preferably, the vehicle dynamics model is processed using linear transformation to obtain the differential equation, including: transforming the vehicle dynamics model according to Laplace transformation to obtain an intermediate function; transforming the intermediate function according to a forward difference method and a Z-inverse transformation to obtain the differential equation.

In this embodiment, the vehicle dynamics model is:

Among them, _Ft is the vehicle wheel-end driving force, m is the vehicle weight (kg), g is the gravitational acceleration, g is 9.81m/s^2, θ is the road slope, the road slope is obtained through the sensor, f is the rolling resistance coefficient, f is 0.000056v+0.0076, Cd is the drag coefficient, A is the frontal area (m^2), δ is the rotational mass conversion coefficient, v is the vehicle speed (km/h), and a is the vehicle acceleration (m/s^2).

Linearize sinθ via Taylor expansion

Since the rolling resistance coefficient is very small (f=0.000056v+0.0076), the rolling resistance has little effect on the vehicle weight estimation, so cosθ=1 can be taken for linearization.

The vehicle dynamics model is transformed into the following form by Laplace transform:

s ² F _t (s) = (a ₁ s ² -6b ₁ )θ(s) + (d ₁ s ² +c ₁ s+e ₁ )a(s);

Among them, a ₁ b ₁ c ₁ d ₁ e ₁ are all known.

Finally, forward difference method

The above transformed equations are transformed by inverse Z transformation to obtain the differential equation, which is:

a _k =AF _k +BF _k-1 +CF _k-2 +Da _k-1 +Ea _k-2 +Fθ _k +Gθ _k-1 +Hθ _k-2

Among them, a _k , a _k-1 , a _k-2 are the accelerations at time k, k-1, k-2 respectively;

are the wheel-end driving forces at the time of vehicle k, k-1, and k-2, respectively, and θ _k-1 and θ _k-2 are the road slopes at the time of vehicle k-1 and k-2, respectively. Wherein, A, B, C, D, E, F, G, and H are the coefficients of the differential equation containing the vehicle weight m.

In one embodiment, determining the corresponding differential equation coefficients and covariance matrix based on the differential equation includes: calculating the differential equation to obtain the covariance matrix; updating the differential equation coefficients according to the covariance matrix to obtain multiple sets of differential equation coefficients.

Specifically, the recursive least squares method with a forgetting factor is used in the MATLAB simulation software to calculate the difference equation. The covariance matrix is obtained during the calculation process. At the same time, the difference equation coefficients are updated during the recursive calculation process until convergence, and multiple sets of difference equation coefficients are obtained, thereby achieving fast and accurate solution to the difference equation.

In one embodiment, the method for acquiring the whole vehicle data of the target vehicle includes: acquiring initial whole vehicle data of the target vehicle; and filtering the initial whole vehicle data to obtain the whole vehicle data of the target vehicle.

Exemplarily, the initial vehicle data is used to represent the vehicle data collected or received by the sensor or controller. Optionally, all the initial vehicle data can be filtered, or part of the initial vehicle data can be filtered. The filtering process can be a first-order low-pass filter, or other filtering methods. Specifically, when using a first-order low-pass filter, different data can use different filter coefficients. Different data can also use the same filter coefficient, which can be set according to actual needs.

In this embodiment, as shown in FIG. 5 , a method for estimating vehicle weight provided in an embodiment of the present application includes the following steps.

S510, obtaining initial vehicle data of the target vehicle.

S520, using a first-order low-pass filter to smooth the filtered initial vehicle data to obtain vehicle data.

S530, determining the vehicle operating state according to the whole vehicle data, if the vehicle operating state meets the preset vehicle operating conditions, outputting a flag 1, and constructing a vehicle dynamics model according to the whole vehicle data.

S540, converting the vehicle dynamics model into a differential equation.

S550, performing recursive calculation on the difference equation by using a recursive least square method with a forgetting factor to obtain multiple groups of difference equation coefficients and covariance matrices.

S560: Select the coefficient of the differential equation corresponding to the minimum value of the variance of the acceleration to estimate the vehicle weight.

Specifically, after obtaining the initial vehicle data of the target vehicle, the required initial vehicle data is screened, such as wheel end driving force, gearbox output shaft speed, road slope, etc. The screened initial vehicle data is smoothed using a first-order low-pass filter to obtain vehicle data, making the vehicle data smoother, thereby estimating the vehicle weight more accurately.

The vehicle running state is determined according to the vehicle data, so as to determine whether the vehicle running state meets the preset vehicle running conditions. If it meets, the flag bit 1 is output, and the vehicle dynamics model is constructed according to the vehicle data. If not, the flag bit 0 is output. The vehicle dynamics model is converted into a differential equation by linear transformation, and the differential equation is recursively calculated by the recursive least squares method with forgetting factor to obtain multiple sets of differential equation coefficients and covariance matrices. Since the elements on the diagonal of the covariance matrix are the variance of acceleration, and the smaller the variance, the more accurate the vehicle weight estimation value, therefore, the differential equation coefficient corresponding to the minimum variance of the vehicle acceleration is selected to estimate the vehicle weight. It can be seen that the above method is used to simulate in the software, the running speed is fast, the convergence is good, and the convergence speed is fast. After the model simulation, the general convergence speed is 20-30s (the step size is set to 0.1s), which makes the estimation result of the vehicle weight more accurate. Furthermore, the calculated vehicle weight can also be processed by median filtering to avoid unreasonable jumps in the vehicle weight during the convergence process.

Correspondingly, Fig. 6 is a schematic diagram of the structure of a vehicle weight estimation device according to an embodiment of the present application. In an exemplary embodiment, the present application embodiment also proposes a vehicle weight estimation device, which includes: a construction module 610, which is used to construct a vehicle dynamics model using the whole vehicle data of the target vehicle; a determination module 620, which is used to determine the corresponding differential equation based on the vehicle dynamics model; a processing module 630, which is used to determine the corresponding differential equation coefficients and covariance matrix based on the differential equation; an estimation module 640, which is used to estimate the vehicle weight of the target vehicle using the differential equation coefficients and the covariance matrix.

In one embodiment, the estimation module 640 is further used to: determine the minimum variance of the vehicle acceleration based on the covariance matrix; and calculate the vehicle weight based on the differential equation coefficient corresponding to the minimum variance of the vehicle acceleration and the differential equation.

In one implementation, the construction module 610 is further used to: determine the operating state of the target vehicle based on the whole vehicle data; and construct the vehicle dynamics model when the operating state satisfies a preset vehicle operating condition.

In one implementation, the determination module 620 is further configured to: utilize linear transformation to process the vehicle dynamics model to obtain the differential equation.

In one embodiment, the vehicle dynamics model is processed using linear transformation to obtain the differential equation, including: transforming the vehicle dynamics model according to Laplace transformation to obtain an intermediate function; transforming the intermediate function according to a forward difference method and a Z-inverse transformation to obtain the differential equation.

In one implementation, the processing module 630 is further used to: calculate the difference equation to obtain a covariance matrix; and update the difference equation coefficients according to the covariance matrix to obtain multiple sets of difference equation coefficients.

In one embodiment, the device further includes: an acquisition module for acquiring initial whole vehicle data of the target vehicle; and a filtering module for filtering the initial whole vehicle data to obtain the whole vehicle data of the target vehicle.

The vehicle weight estimation device provided in this embodiment belongs to the same application concept as the vehicle weight estimation method provided in the above embodiments of this application, and can execute the vehicle weight estimation method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of executing the vehicle weight estimation method. For technical details not fully described in this embodiment, please refer to the specific processing content of the vehicle weight estimation method provided in the above embodiments of this application, and will not be repeated here.

Another embodiment of the present application further proposes an engineering vehicle, as shown in FIG7 , the device includes: a control device; the control device is used to implement the above-mentioned vehicle weight estimation method.

In the technical solution of the present application, the engineering vehicle may specifically be a mixer truck. Since the engineering vehicle adopts the vehicle weight estimation method, the vehicle weight can be accurately estimated.

As shown in FIG. 8 , the control device may include: a memory 800 and a processor 810; wherein the memory 800 is connected to the processor 810 for storing programs; and the processor 810 is used to implement the vehicle weight estimation method disclosed in any of the above embodiments by running the program stored in the memory 800.

Specifically, the electronic device may further include: a bus, a communication interface 820 , an input device 830 and an output device 840 .

The processor 810 , the memory 800 , the communication interface 820 , the input device 830 , and the output device 840 are connected to each other via a bus.

A bus may include a pathway that transfers information between components of a computer system.

Processor 810 may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the scheme of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

The processor 810 may include a main processor, and may also include a baseband chip, a modem, and the like.

The memory 800 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key businesses. Specifically, the program may include a program code, and the program code includes computer operation instructions. More specifically, the memory 800 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

The input device 830 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

Output device 840 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

The communication interface 820 may include any transceiver or the like to communicate with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

The processor 810 executes the program stored in the memory 800 and calls other devices, which can be used to implement each step of any vehicle weight estimation method provided in the above embodiments of the present application.

In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the vehicle weight estimation method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

The computer program product may be written in any combination of one or more programming languages to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent 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 addition, an embodiment of the present application may also be a storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the vehicle weight estimation method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

The specific working contents of the above-mentioned electronic device, as well as the specific working contents of the above-mentioned computer program product and the computer program on the storage medium when being executed by the processor, can all be referred to the contents of the above-mentioned method embodiments, which will not be repeated here.

For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be combined, divided and deleted according to actual needs.

In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

A method for estimating vehicle weight, comprising:

Use the whole vehicle data of the target vehicle to build a vehicle dynamics model;

determining a corresponding differential equation based on the vehicle dynamics model;

Determining corresponding difference equation coefficients and a covariance matrix based on the difference equation;

The vehicle weight of the target vehicle is estimated using the difference equation coefficients and the covariance matrix.
The method according to claim 1, wherein the estimating the vehicle weight of the target vehicle using the difference equation coefficients and the covariance matrix comprises:

Determining a minimum variance value of vehicle acceleration based on the covariance matrix;

The vehicle weight is calculated based on the differential equation coefficient corresponding to the minimum variance of the vehicle acceleration and the differential equation.
The method according to claim 1 or 2, wherein the constructing a vehicle dynamics model using the whole vehicle data comprises:

Determining the operating status of the target vehicle based on the vehicle data;

When the operating state satisfies a preset vehicle operating condition, the vehicle dynamics model is constructed.
The method according to claim 3, wherein the operating state includes: a braking state, a parking state, a starting state, an idling state, and a driving state, wherein the driving state includes: a normal driving state and an abnormal driving state.
The method according to claim 4, wherein the abnormal driving state includes: the vehicle acceleration is greater than a preset first threshold, the vehicle acceleration is less than a preset second threshold, or the slope is greater than a preset third threshold.
The method according to claim 5, wherein the preset vehicle operating condition includes: the operating state is not any one of the parking state, the braking state, the idling state and the abnormal driving state.
The method according to any one of claims 1 to 6, wherein determining the corresponding differential equation based on the vehicle dynamics model comprises:

The vehicle dynamics model is processed using linear transformation to obtain the differential equation.
The method according to claim 7, wherein the step of processing the vehicle dynamics model using linear transformation to obtain the differential equation comprises:

Converting the vehicle dynamics model according to Laplace transform to obtain an intermediate function;

The intermediate function is transformed according to the forward difference method and the Z-inverse transformation to obtain the differential equation.
The method according to any one of claims 1 to 8, wherein the determining the corresponding difference equation coefficients and covariance matrix based on the difference equation comprises:

Calculating the difference equation to obtain a covariance matrix;

The difference equation coefficients are updated according to the covariance matrix to obtain multiple groups of difference equation coefficients.
According to the method according to any one of claims 1 to 9, the method for acquiring the whole vehicle data of the target vehicle comprises:

Acquiring initial vehicle data of the target vehicle;

The initial whole vehicle data is filtered to obtain the whole vehicle data of the target vehicle.
The method according to claim 10, wherein the filtering process is a first order low pass filtering.
The method according to any one of claims 1 to 11, wherein the type of the target vehicle is a mixer truck.
The method according to any one of claims 1 to 12, wherein the whole vehicle data includes: vehicle wheel end driving force, road slope, and vehicle driving speed.
A vehicle weight estimation device, comprising:

A construction module for constructing a vehicle dynamics model using the full vehicle data of the target vehicle;

A determination module, configured to determine a corresponding differential equation based on the vehicle dynamics model;

A processing module, configured to determine corresponding difference equation coefficients and a covariance matrix based on the difference equation;

An estimation module is used to estimate the vehicle weight of the target vehicle by using the differential equation coefficients and the covariance matrix.
An engineering vehicle, comprising:

A control device, the control device being used to execute the vehicle weight estimation method as claimed in any one of claims 1 to 13.
A storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the vehicle weight estimation method as claimed in any one of claims 1 to 13 is implemented.