Vehicle weight estimation methods, devices, storage media, and engineering vehicles

Abstract

This application provides a method, device, storage medium, and engineering vehicle for estimating vehicle weight. The specific implementation involves: constructing a vehicle dynamics model using the whole vehicle data of the target vehicle; determining the corresponding difference equation based on the vehicle dynamics model; determining the corresponding difference equation coefficients and covariance matrix based on the difference equation; and estimating the vehicle weight of the target vehicle using the difference equation coefficients and the covariance matrix. According to the technical solution of this application, the accuracy of vehicle weight estimation can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of engineering machinery technology, and in particular to a method, device, storage medium, and engineering vehicle for estimating vehicle weight. Background Technology

[0002] Currently, due to the large variations in the mass of concrete mixer trucks and their complex operating conditions, the truck weight significantly impacts the starting and shifting strategies of mixer trucks equipped with automatic transmissions. Existing technologies typically rely on external measuring devices to assess vehicle weight, which cannot accurately estimate the truck's weight. Summary of the Invention

[0003] To address the aforementioned issues, this application proposes a method, apparatus, storage medium, and engineering vehicle for estimating vehicle weight, which can effectively improve the accuracy of vehicle weight estimation.

[0004] According to a first aspect of the embodiments of this application, a method for estimating vehicle weight is provided, comprising:

[0005] A vehicle dynamics model is constructed using the whole vehicle data of the target vehicle;

[0006] The corresponding difference equations are determined based on the vehicle dynamics model.

[0007] Based on the difference equation, determine the corresponding difference equation coefficients and covariance matrix;

[0008] The weight of the target vehicle is estimated using the coefficients of the difference equation and the covariance matrix.

[0009] According to a second aspect of the embodiments of this application, a vehicle weight estimation device is provided, comprising:

[0010] The building module is used to construct a vehicle dynamics model using the whole vehicle data of the target vehicle;

[0011] The determination module is used to determine the corresponding difference equations based on the vehicle dynamics model.

[0012] The processing module is used to determine the corresponding difference equation coefficients and covariance matrix based on the difference equation;

[0013] An estimation module is used to estimate the weight of the target vehicle using the coefficients of the difference equation and the covariance matrix.

[0014] A third aspect of this application provides an engineering vehicle, comprising:

[0015] A control device for implementing the above-described method for estimating vehicle weight.

[0016] The fourth aspect of this application provides a storage medium storing a computer program, which, when run by a processor, implements the above-described method for estimating vehicle weight.

[0017] One embodiment of the above application has the following advantages or beneficial effects:

[0018] The vehicle weight can be accurately assessed by using the vehicle dynamics model to determine the difference equations, the difference variance coefficients and covariance matrix, and then the difference variance coefficients and covariance matrix can be used to estimate the vehicle weight. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for estimating vehicle weight according to an embodiment of this application;

[0021] Figure 2 A flowchart illustrating a method for estimating vehicle weight according to an embodiment of this application;

[0022] Figure 3 A schematic diagram of a covariance matrix provided in an embodiment of this application;

[0023] Figure 4 A schematic diagram of the coefficients of a difference equation provided in an embodiment of this application;

[0024] Figure 5 A schematic flowchart illustrating a virtual dress-up method provided in one embodiment of this application;

[0025] Figure 6 A schematic diagram of a vehicle weight estimation device provided in an embodiment of this application;

[0026] Figure 7 This is a structural schematic diagram of an engineering vehicle provided in one embodiment of this application;

[0027] Figure 8 This is a schematic diagram of the structure of a control device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solution of this application is applicable to scenarios involving vehicle weight detection, such as concrete mixer trucks. Using the technical solution of this application, vehicle weight can be estimated more accurately.

[0029] The technical solutions of this application can be applied, by way of example, to hardware devices such as processors, electronic devices, and servers (including cloud servers), or packaged as software programs and run. When the hardware device executes the processing procedure of the technical solutions of this application, or when the aforementioned software program is run, the purpose of estimating vehicle weight based on the difference equation determined by the vehicle dynamics model can be achieved. This application only provides illustrative descriptions of the specific processing procedure of the technical solutions of this application and does not limit the specific implementation form of the technical solutions of this application. Any technical implementation form that can execute the processing procedure of the technical solutions of this application can be adopted by this application.

[0030] The technical solutions of the embodiments of this application will be clearly and completely described 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 of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] Exemplary methods

[0032] Figure 1 This is a flowchart of a vehicle weight estimation method according to an embodiment of this application. In an exemplary embodiment, the vehicle weight estimation method specifically includes:

[0033] S110. Construct a vehicle dynamics model using the whole vehicle data of the target vehicle;

[0034] S120. Determine the corresponding difference equations based on the vehicle dynamics model;

[0035] S130. Determine the corresponding difference equation coefficients and covariance matrix based on the difference equation;

[0036] S140. Using the coefficients of the difference equation and the covariance matrix, estimate the weight of the target vehicle.

[0037] In step S110, exemplarily, the target vehicle can be a specific vehicle or any vehicle. In this embodiment, the target vehicle is a mixer truck, but it can also be other types of vehicles, which are not limited here. Vehicle data is used to represent the data collected when the target vehicle is in motion. Optionally, the vehicle data can be filtered data or unfiltered data. Vehicle data can include: vehicle wheel-end driving force, road gradient, vehicle speed, etc. Optionally, the vehicle data can be directly detected or obtained through other sensors. The vehicle dynamics model is used to represent the vehicle's driving state, wherein the vehicle dynamics model is a function including vehicle weight, specifically composed of vehicle wheel-end driving force, vehicle weight, gravitational acceleration, road gradient, rolling resistance coefficient, wind resistance coefficient, frontal area, rotational mass conversion factor, vehicle speed, and vehicle acceleration.

[0038] In step S120, exemplarily, the difference equation is an equation containing the difference of unknown functions and independent variables. In this embodiment, the difference equation is an equation concerning vehicle acceleration, gradient, and vehicle wheel-end driving force. Specifically, the vehicle dynamics model is mathematically transformed to obtain the difference equation, so as to solve the difference equation to find an approximate solution to the vehicle dynamics model, thereby discretizing the continuous problem. The mathematical transformation includes: linearization, Laplace transform, forward differencing, backward differencing, inverse Z-transform, etc.

[0039] In step S130, for example, the difference equation coefficients are used to represent the coefficients of each parameter in the difference equation. Optionally, a difference equation coefficient is set for each parameter in the difference equation; the difference equation coefficients can be the same or different. Optionally, the difference equation coefficients can include vehicle weight; for example, if A = 0.5 * m, where A is the difference equation coefficient and m is the vehicle weight. Specifically, multiple sets of difference equation coefficients can be determined for each difference equation. The covariance matrix represents the pairwise linear correlation between a set of random variables. Optionally, the covariance matrix can include vehicle acceleration. Specifically, the covariance matrix and difference equation coefficients can be obtained by simulating the difference equation using simulation software.

[0040] In step S140, for example, since the vehicle dynamics model is a function that includes the vehicle weight, and the difference equation is derived from the vehicle dynamics model, the vehicle weight can be directly obtained by calculating the vehicle dynamics model or difference equation based on the difference equation coefficients and the covariance matrix. Alternatively, when converting the vehicle dynamics model into a difference equation, the difference equation coefficients can be represented by the vehicle weight. In this way, by selecting appropriate difference equation coefficients based on the covariance matrix, the vehicle weight can be estimated.

[0041] In the technical solution of this application, a difference equation is determined based on a vehicle dynamics model. The difference variance coefficients and covariance matrix are then determined through the difference equations, and the vehicle weight is estimated using the difference variance coefficients and covariance matrix. This allows for accurate assessment of vehicle weight without the need for external equipment. Moreover, because the difference equations converge quickly, not only can the vehicle weight be estimated more accurately, but the speed of weight estimation can also be improved.

[0042] In one implementation, such as Figure 2 As shown, estimating the weight of the target vehicle using the coefficients of the difference equation and the covariance matrix includes:

[0043] S210. Determine the minimum variance of the vehicle acceleration based on the covariance matrix;

[0044] S220. Based on the coefficients of the difference equation corresponding to the minimum variance of the vehicle acceleration and the difference equation, the vehicle weight is calculated.

[0045] For example, such as Figure 3-4 As shown, since the elements on the diagonal of the covariance matrix represent the variance 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 to determine the coefficients of the difference equation corresponding to the minimum variance. This makes the vehicle weight calculated based on the above difference equation coefficients and difference equation more accurate.

[0046] In one implementation, constructing a vehicle dynamics model using the vehicle data includes:

[0047] The operating status of the target vehicle is determined based on the vehicle data;

[0048] When the operating state meets the preset vehicle operating conditions, the vehicle dynamics model is constructed.

[0049] For example, the operating state of the target vehicle can be determined based on one or more real-time collected vehicle data, or it can be determined based on the result of calculations from multiple vehicle data. The operating state can include: braking state, parking state, starting state, idling state, driving state, etc. Optionally, the driving state can include: normal driving state and abnormal driving state. Optionally, abnormal driving states include: vehicle acceleration greater than a preset first threshold, vehicle acceleration less than a preset second threshold, or gradient greater than a preset third threshold. The preset first threshold, preset second threshold, and preset third threshold can be set according to actual needs and are not limited here.

[0050] For example, preset vehicle operating conditions are used to indicate that the target vehicle is operating stably. Optionally, vehicle operating conditions can be one condition or multiple conditions. Specifically, corresponding vehicle operating conditions can be set according to the operating status.

[0051] Specifically, because the clutch torque transmission cannot be accurately obtained during gear shifting, the vehicle weight cannot be accurately estimated in the stationary state or during the 0-throttle process (i.e., idling state). Furthermore, due to the inaccuracy of the wheel-end driving force during braking, the vehicle weight cannot be accurately estimated during braking (i.e., braking state). Excessive or insufficient vehicle acceleration, or excessively steep inclines, also prevent accurate weight estimation. Understandably, these conditions prevent the target vehicle from moving normally, leading to abnormal weight fluctuations when estimating its weight. Therefore, the preset vehicle operating conditions can include: the operating state not being any of the following: stationary state, braking state, idling state, or abnormal driving state.

[0052] In this embodiment, the operating state that meets the preset vehicle operating conditions is set to the corresponding first flag bit, and the operating state that does not meet the preset vehicle operating 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. This allows for timely notification to staff that the vehicle is currently in an unstable operating condition.

[0053] After acquiring the vehicle data, the vehicle's operating status is determined. If the vehicle's operating status is not any of the following—stopped, braking, idling, or abnormal driving—then the vehicle is in a stable operating condition, and a flag of 1 is output. If the vehicle's operating status is any of the following—stopped, braking, idling, or abnormal driving—then the vehicle is in an unstable operating condition, and a flag of 0 is output. This allows for filtering of the vehicle's operating conditions, identifying stable conditions, avoiding large fluctuations in vehicle weight, and thus making the vehicle weight estimation more accurate.

[0054] In one implementation, determining the corresponding difference equation based on the vehicle dynamics model includes:

[0055] The vehicle dynamics model is processed using linear transformation to obtain the difference equation.

[0056] For example, linear transformations can include Laplace transform, forward difference method, and inverse Z-transform. Specifically, a single linear transformation method can be used to transform the vehicle dynamics model, or multiple linear transformation methods can be combined to transform the vehicle dynamics model to obtain difference equations.

[0057] Preferably, the vehicle dynamics model is processed using a linear transformation to obtain the difference equation, including:

[0058] The vehicle dynamics model is transformed using the Laplace transform to obtain an intermediate function;

[0059] The intermediate function is transformed by combining the forward difference method and the inverse Z-transform to obtain the difference equation.

[0060] In this embodiment, the vehicle dynamics model is as follows:

[0061]

[0062] Among them, F t The driving force at the vehicle wheel end is denoted as m, the vehicle weight (kg) is denoted as g, g is the acceleration due to gravity (g = 9.81 m / s²), θ is the road slope (obtained by a sensor), f is the rolling resistance coefficient (f = 0.000056v + 0.0076), Cd is the wind resistance coefficient, A is the frontal area (m²), δ is the rotational mass conversion factor, v is the vehicle speed (km / h), and a is the vehicle acceleration (m / s²).

[0063] Linearizing sinθ using Taylor expansion Because the rolling resistance coefficient is very small (f = 0.000056v + 0.0076), the rolling resistance has little impact on the vehicle weight estimation. Therefore, cosθ = 1 can be used for linearization.

[0064] The vehicle dynamics model is transformed using a Laplace transform, resulting in the following form:

[0065] s 2 F t (s)=(a1s 2 -6b1)θ(s)+(d1s 2 +c1s+e1)a(s);

[0066] Among them, a1b1c1d1e1 are all known.

[0067] Finally, the forward difference method was adopted. The inverse Z-transform is used to transform the above transformed equation to obtain the difference equation, specifically:

[0068] a k =AF k +BF k-1 +CF k-2 +Da k-1 +Ea k-2 +Fθ k +Gθ k-1 +Hθ k-2

[0069] Among them, a ka k-1 a k-2 , respectively, represent the accelerations at times k, k-1, and k-2; Let θ represent the vehicle's wheel-end driving force at times k, k-1, and k-2, respectively. k-1 θ k-2 Let A, B, C, D, E, F, G, and H be the road gradients at times k-1 and k-2, respectively. A, B, C, D, E, F, G, and H are the coefficients of the difference equation including the vehicle weight m.

[0070] In one implementation, determining the corresponding difference equation coefficients and covariance matrix based on the difference equation includes:

[0071] The covariance matrix is ​​obtained by calculating the difference equation.

[0072] The coefficients of the difference equations are updated based on the covariance matrix to obtain multiple sets of difference equation coefficients.

[0073] Specifically, the recursive least squares method with a forgetting factor is used in the MATLAB simulation software to calculate the difference equation. During the calculation, the covariance matrix is ​​obtained, and the coefficients of the difference equation are updated during the recursive calculation until convergence, resulting in multiple sets of coefficients of the difference equation, thereby achieving a fast and accurate solution to the difference equation.

[0074] In one embodiment, the method for acquiring the vehicle data of the target vehicle includes:

[0075] Obtain the initial vehicle data of the target vehicle;

[0076] The initial vehicle data is filtered to obtain the vehicle data of the target vehicle.

[0077] For example, initial vehicle data is used to represent vehicle data collected or received by sensors or controllers. Optionally, all initial vehicle data can be filtered, or only a portion of the initial vehicle data can be filtered. The filtering process can be first-order low-pass filtering, or other filtering methods. Specifically, when using first-order low-pass filtering, different data can use different filtering coefficients. Different data can also use the same filtering coefficients, which can be set according to actual needs.

[0078] In this embodiment, as Figure 5 As shown, after obtaining the initial vehicle data of the target vehicle, the necessary initial vehicle data is filtered, such as wheel-end driving force, transmission output shaft speed, and road gradient. A first-order low-pass filter is used to smooth the filtered initial vehicle data, resulting in smoother overall vehicle data and thus more accurate vehicle weight estimation.

[0079] The vehicle's operating state is determined based on the vehicle's overall data, thus confirming whether the operating state meets preset vehicle operating conditions. If it does, a flag of 1 is output, and a vehicle dynamics model is constructed based on the overall vehicle data. If not, a flag of 0 is output. The vehicle dynamics model is converted into difference equations using linear transformation. The difference equations are then recursively calculated using a recursive least squares method with a forgetting factor, yielding multiple sets of difference equation coefficients and a covariance matrix. Since the diagonal elements of the covariance matrix represent the variance of acceleration, and a smaller variance results in a more accurate vehicle weight estimate, the difference equation coefficients corresponding to the minimum variance of vehicle acceleration are selected to estimate the vehicle weight. It is evident that the above method, when used in software simulation, results in fast operation and good convergence. The convergence speed is typically 20-30 seconds (with a step size of 0.1 seconds), leading to more accurate vehicle weight estimation. Furthermore, the calculated vehicle weight can be filtered using median filtering to prevent unreasonable fluctuations in weight during convergence.

[0080] Exemplary device

[0081] Correspondingly, Figure 6 This is a schematic diagram of a vehicle weight estimation device according to an embodiment of this application. In an exemplary embodiment, this application also provides a vehicle weight estimation device, which includes:

[0082] Module 610 is used to build a vehicle dynamics model using the whole vehicle data of the target vehicle;

[0083] The determination module 620 is used to determine the corresponding difference equations based on the vehicle dynamics model;

[0084] Processing module 630 is used to determine the corresponding difference equation coefficients and covariance matrix based on the difference equation;

[0085] The estimation module 640 is used to estimate the weight of the target vehicle using the coefficients of the difference equation and the covariance matrix.

[0086] In one implementation, the estimation module 640 is further configured to:

[0087] The minimum variance of vehicle acceleration is determined based on the covariance matrix.

[0088] The vehicle weight is calculated based on the coefficients of the difference equation corresponding to the minimum variance of the vehicle acceleration and the difference equation itself.

[0089] In one implementation, the construction module 610 is further configured to:

[0090] The operating status of the target vehicle is determined based on the vehicle data;

[0091] When the operating state meets the preset vehicle operating conditions, the vehicle dynamics model is constructed.

[0092] In one implementation, the determining module 620 is further configured to:

[0093] The vehicle dynamics model is processed using linear transformation to obtain the difference equation.

[0094] In one implementation, the vehicle dynamics model is processed using a linear transformation to obtain the difference equation, including:

[0095] The vehicle dynamics model is transformed using the Laplace transform to obtain an intermediate function;

[0096] The intermediate function is transformed by combining the forward difference method and the inverse Z-transform to obtain the difference equation.

[0097] In one embodiment, the processing module 630 is further configured to:

[0098] The covariance matrix is ​​obtained by calculating the difference equation.

[0099] The coefficients of the difference equations are updated based on the covariance matrix to obtain multiple sets of difference equation coefficients.

[0100] In one embodiment, the device further includes:

[0101] The acquisition module is used to acquire the initial vehicle data of the target vehicle;

[0102] The filtering module is used to filter the initial vehicle data to obtain the vehicle data of the target vehicle.

[0103] The vehicle weight estimation device provided in this embodiment belongs to the same concept as the vehicle weight estimation method provided in the above embodiments of this application. It 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 for executing the vehicle weight estimation method. Technical details not described in detail in this embodiment can be found in the specific processing content of the vehicle weight estimation method provided in the above embodiments of this application, and will not be repeated here.

[0104] Exemplary electronic devices

[0105] Another embodiment of this application also proposes an engineering vehicle, such as Figure 7 As shown, the device includes: a control device; the control device is used to implement the above-described method for estimating vehicle weight.

[0106] In the technical solution of this application, the engineering vehicle can specifically be a concrete mixer truck. Because the engineering vehicle employs a weight estimation method, its weight can be accurately estimated.

[0107] like Figure 8 As shown, the control device may include: a memory 800 and a processor 810;

[0108] The memory 800 is connected to the processor 810 and is used to store programs;

[0109] The processor 810 is configured to implement the vehicle weight estimation method disclosed in any of the above embodiments by running the program stored in the memory 800.

[0110] Specifically, the aforementioned electronic device may also include: a bus, a communication interface 820, an input device 830, and an output device 840.

[0111] The processor 810, memory 800, communication interface 820, input device 830, and output device 840 are interconnected via a bus. Among them:

[0112] A bus can include a pathway for transmitting information between various components of a computer system.

[0113] The processor 810 can 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 used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0114] The processor 810 may include a main processor, as well as a baseband chip, modem, etc.

[0115] The memory 800 stores a program that executes the technical solution of this invention, and may also store an operating system and other key business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 800 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0116] Input device 830 may include a device for receiving user input data and information, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.

[0117] Output device 840 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0118] The communication interface 820 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0119] The processor 810 executes the program stored in the memory 800 and calls other devices, which can be used to implement the various steps of any of the vehicle weight estimation methods provided in the above embodiments of this application.

[0120] Exemplary computer program products and storage media

[0121] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the vehicle weight estimation methods according to various embodiments of this application described in the "Exemplary Methods" section of this specification.

[0122] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0123] Furthermore, embodiments of this application may also be storage media storing a computer program, which is executed by a processor in the steps of the vehicle weight estimation methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0124] The specific working content of the aforementioned electronic device, as well as the specific working content of the aforementioned computer program product and the computer program on the storage medium being run by the processor, can all be found in the content of the aforementioned method embodiments, and will not be repeated here.

[0125] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0126] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0127] The steps in the methods of the various embodiments of this application can be adjusted, merged, or deleted in order according to actual needs, and the technical features described in each embodiment can be replaced or combined.

[0128] The modules and sub-modules in the various embodiments of the present application's devices and terminals can be merged, divided, and deleted according to actual needs.

[0129] It should be understood that the disclosed terminals, devices, and methods can be implemented in other ways, given the several embodiments provided in this application. For example, the terminal embodiments described above are merely illustrative. For instance, the division of modules or sub-modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0130] The modules or submodules described as separate components may or may not be physically separate. The components that constitute a module or submodule may or may not be physical modules or submodules; that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules can be selected to achieve the purpose of this embodiment's solution, depending on actual needs.

[0131] Furthermore, the functional modules or sub-modules in the various embodiments of this application can be integrated into one processing module, or each module or sub-module can exist physically separately, or two or more modules or sub-modules can be integrated into one module. The integrated modules or sub-modules described above can be implemented in hardware or in the form of software functional modules or sub-modules.

[0132] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software unit executed by a processor, or a combination of both. The software unit can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0134] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0135] The above description of the disclosed embodiments enables those skilled in the art to make or use this 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 this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating vehicle weight, characterized in that, include: A vehicle dynamics model is constructed using the whole vehicle data of the target vehicle; The corresponding difference equations are determined based on the vehicle dynamics model. Based on the difference equation, determine the corresponding difference equation coefficients and covariance matrix; The weight of the target vehicle is estimated using the coefficients of the difference equation and the covariance matrix. The step of estimating the weight of the target vehicle using the coefficients of the difference equation and the covariance matrix includes: determining the minimum variance of the vehicle acceleration based on the covariance matrix; The vehicle weight is calculated based on the coefficients of the difference equation corresponding to the minimum variance of the vehicle acceleration and the difference equation itself.

2. The method according to claim 1, characterized in that, The process of constructing a vehicle dynamics model using the vehicle data includes: The operating status of the target vehicle is determined based on the vehicle data; When the operating state meets the preset vehicle operating conditions, the vehicle dynamics model is constructed.

3. The method according to claim 1, characterized in that, The determination of the corresponding difference equation based on the vehicle dynamics model includes: The vehicle dynamics model is processed using linear transformation to obtain the difference equation.

4. The method according to claim 3, characterized in that, The process of using linear transformation to process the vehicle dynamics model to obtain the difference equation includes: The vehicle dynamics model is transformed using the Laplace transform to obtain an intermediate function; The intermediate function is transformed by combining the forward difference method and the inverse Z-transform to obtain the difference equation.

5. The method according to claim 1, characterized in that, The step of determining the corresponding difference equation coefficients and covariance matrix based on the difference equation includes: The covariance matrix is ​​obtained by calculating the difference equation. The coefficients of the difference equations are updated based on the covariance matrix to obtain multiple sets of difference equation coefficients.

6. The method according to claim 1, characterized in that, The method for acquiring the whole vehicle data of the target vehicle includes: Obtain the initial vehicle data of the target vehicle; The initial vehicle data is filtered to obtain the vehicle data of the target vehicle.

7. A vehicle weight estimation device, characterized in that, include: The building module is used to construct a vehicle dynamics model using the whole vehicle data of the target vehicle; The determination module is used to determine the corresponding difference equations based on the vehicle dynamics model. The processing module is used to determine the corresponding difference equation coefficients and covariance matrix based on the difference equation; An estimation module is used to estimate the weight of the target vehicle using the coefficients of the difference equation and the covariance matrix. The estimation module is specifically used to determine the minimum variance of the vehicle acceleration based on the covariance matrix. The vehicle weight is calculated based on the coefficients of the difference equation corresponding to the minimum variance of the vehicle acceleration and the difference equation itself.

8. An engineering vehicle, characterized in that, include: A control device for performing the vehicle weight estimation method as claimed in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the vehicle weight estimation method as described in any one of claims 1 to 6.

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