WO2024093016A1 - Vehicle weight estimation method and apparatus, storage medium and engineering vehicle - Google Patents
Vehicle weight estimation method and apparatus, storage medium and engineering vehicle Download PDFInfo
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Definitions
- 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.
- the weight of the mixer truck with automatic transmission seriously affects the starting and shifting strategies of the mixer truck.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- hardware devices such as processors, electronic devices, servers (including cloud servers), or packaged into software programs to be run.
- 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.
- Fig. 1 is a flow chart of a method for estimating vehicle weight according to an embodiment of the present application.
- the method for estimating vehicle weight specifically includes the following steps.
- S140 Estimate the vehicle weight of the target vehicle using the differential equation coefficients and the covariance matrix.
- the target vehicle can be a specified vehicle or any vehicle.
- 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.
- 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.
- 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.
- the differential equation is an equation containing the differential of the unknown function and the independent variable.
- the differential equation is an equation about the vehicle acceleration, slope, and the vehicle wheel-end driving force.
- 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.
- the mathematical transformation includes: linearization, Laplace transform, forward difference, backward difference, Z-inverse transform, etc.
- the differential equation coefficients are used to represent the coefficients of each parameter in the differential equation.
- a differential equation coefficient is set for each parameter in the differential equation, and the differential equation coefficients may be the same or different.
- 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.
- the covariance matrix may include vehicle acceleration.
- the differential equation may be simulated according to the simulation software to obtain the covariance matrix and differential equation coefficients of the differential equation.
- step S140 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- the driving state may include: normal driving state and abnormal driving state.
- 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.
- the preset vehicle operating condition is used to indicate that the target vehicle is in a stable operating state.
- the vehicle operating condition may be one condition or multiple conditions.
- the corresponding vehicle operating condition may be set according to the operating state.
- 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.
- 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.
- 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.
- the vehicle's operating state 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.
- 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.
- the linear transformation may include: Laplace transformation, forward difference method and Z-inverse transformation, etc.
- 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.
- 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.
- the vehicle dynamics model is:
- 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
- ⁇ 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)
- ⁇ is the rotational mass conversion coefficient
- v is the vehicle speed (km/h)
- a is the vehicle acceleration (m/s ⁇ 2).
- the vehicle dynamics model is transformed into the following form by Laplace transform:
- a k AF k +BF k-1 +CF k-2 +Da k-1 +Ea k-2 +F ⁇ k +G ⁇ k-1 +H ⁇ k-2
- 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 ⁇ k-1 and ⁇ k-2 are the road slopes at the time of vehicle k-1 and k-2, respectively.
- A, B, C, D, E, F, G, and H are the coefficients of the differential equation containing the vehicle weight m.
- 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.
- 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.
- 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.
- 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.
- the initial vehicle data is used to represent the vehicle data collected or received by the sensor or controller.
- 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.
- a method for estimating vehicle weight provided in an embodiment of the present application includes the following steps.
- S560 Select the coefficient of the differential equation corresponding to the minimum value of the variance of the acceleration to estimate the vehicle weight.
- 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.
- 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.
- 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.
- the calculated vehicle weight can also be processed by median filtering to avoid unreasonable jumps in the vehicle weight during the convergence process.
- Fig. 6 is a schematic diagram of the structure of a vehicle weight estimation device according to an embodiment of the present application.
- 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.
- 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
- 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.
- 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.
- the determination module 620 is further configured to: utilize linear transformation to process the vehicle dynamics model to obtain the differential equation.
- 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.
- 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.
- 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.
- the vehicle weight estimation method provided in the above embodiments of this application has the corresponding functional modules and beneficial effects of executing the vehicle weight estimation method.
- the device includes: a control device; the control device is used to implement the above-mentioned vehicle weight estimation method.
- 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.
- 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.
- 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.
- DSP digital signal processor
- ASIC application-specific integrated circuit
- FPGA field programmable gate array
- 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.
- the program may include a program code, and the program code includes computer operation instructions.
- 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.
- 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.
- RAN radio access network
- WLAN wireless local area network
- 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.
- 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.
- 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.
- 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.
- the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
- 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.
- the disclosed terminals, devices and methods can be implemented in other ways.
- 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.
- 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.
- 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.
- 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.
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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
本申请涉及工程机械技术领域,尤其涉及一种车重的估算方法、装置、存储介质及工程车辆。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.
发明背景Background of the Invention
目前,由于搅拌车的质量变化大且工况复杂,那么对于匹配自动变速器的搅拌车来说,车重严重影响着搅拌车的起步和换挡策略。而在相关技术中,通常会借助一些外在的测量设备进行车重的测量,无法准确地估算搅拌车的车重。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.
发明内容Summary of the invention
为了解决上述问题,本申请提出一种车重的估算方法、装置、存储介质及工程车辆,能够有效提升车重估算的准确性。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.
第一方面,本申请实施例提供了一种车重的估算方法,包括:利用目标车辆的整车数据构建车辆动力学模型;基于所述车辆动力学模型确定对应的差分方程;基于所述差分方程确定对应的差分方程系数和协方差矩阵;利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重。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.
第二方面,本申请实施例提供了一种车重的估算装置,包括:构建模块,用于利用目标车辆的整车数据构建车辆动力学模型;确定模块,用于基于所述车辆动力学模型确定对应的差分方程;处理模块,用于基于所述差分方程确定对应的差分方程系数和协方差矩阵;估算模块,用于利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重。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.
第三方面,本申请实施例提供了一种工程车辆,包括:控制设备,所述控制设备用于实现上述的车重的估算方法。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.
第四方面,本申请实施例提供了一种存储介质,所述存储介质上存储有计算机程序,所述计算机程度被处理器运行时,实现上述的车重的估算方法。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.
上述申请中的一个实施例具有如下优点或有益效果:One embodiment of the above application has the following advantages or beneficial effects:
基于车辆动力学模型确定差分方程,通过差分方程确定差分方差系数和协方差矩阵,再利用差分方差系数和协方差矩阵估算车重,这样可以无需借助外界设备,就能够准确地对车重进行评估。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.
附图简要说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本申请实施例或相关技术中的技术方案,下面将对实施例或相关技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本申请的实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据提供的附图获得其他的附图。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.
图1为本申请一实施例提供的一种车重的估算方法的流程示意图。FIG1 is a flow chart of a method for estimating vehicle weight provided in one embodiment of the present application.
图2为本申请一实施例提供的一种车重的估算方法的流程示意图。FIG2 is a flow chart of a method for estimating vehicle weight provided in accordance with an embodiment of the present application.
图3为本申请一实施例提供的协方差矩阵的示意图。FIG. 3 is a schematic diagram of a covariance matrix provided in an embodiment of the present application.
图4为本申请一实施例提供的差分方程系数的示意图。FIG. 4 is a schematic diagram of differential equation coefficients provided in an embodiment of the present application.
图5为本申请一实施例提供的一种车重的估算方法的具体流程示意图。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.
图6为本申请一实施例提供的一种车重的估算装置的结构示意图。FIG6 is a schematic diagram of the structure of a vehicle weight estimation device provided in one embodiment of the present application.
图7为本申请一实施例提供的一种工程车辆的结构示意图。FIG. 7 is a schematic structural diagram of an engineering vehicle provided in one embodiment of the present application.
图8为本申请一实施例提供的一种控制设备的结构示意图。FIG8 is a schematic diagram of the structure of a control device provided in an embodiment of the present application.
实施本申请的方式Methods of implementing this application
本申请实施例技术方案适用于应用在检测车重的场景中,例如,搅拌车等。采用本申请实施例技术方案,能够更加准确地估算车重。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.
本申请实施例技术方案可示例性地应用于处理器、电子设备、服务器(包括云服务器)等硬件设备,或包装成软件程序被运行,当硬件设备执行本申请实施例技术方案的处理过程,或上述软件程序被运行时,可以实现根据车辆动力学模型确定的差分方程估算车重的目的。本申请实施例只对本申请技术方案的具体处理过程进行示例性介绍,并不对本申请技术方案的具体实现形式进行限定,任意的可以执行本申请技术方案处理过程的技术实现形式,都可以被本申请实施例所采用。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.
下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。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.
图1是根据本申请一实施例的车重的估算方法的流程图。在一示例性实施例中,该一种车重的估算方法,具体包括如下步骤。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、利用目标车辆的整车数据构建车辆动力学模型。S110: construct a vehicle dynamics model using the whole vehicle data of the target vehicle.
S120、基于所述车辆动力学模型确定对应的差分方程。S120. Determine a corresponding differential equation based on the vehicle dynamics model.
S130、基于所述差分方程确定对应的差分方程系数和协方差矩阵。S130. Determine corresponding differential equation coefficients and covariance matrix based on the differential equation.
S140、利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重。S140: Estimate the vehicle weight of the target vehicle using the differential equation coefficients and the covariance matrix.
在步骤S110中,示例性地,目标车辆可以是指定的某一车辆,还可以是任意车辆。在本实施例中,目标车辆的种类是搅拌车,还可以是其他类别的车辆,在此不作限定。整车数据用于表示目标车辆行驶时所采集的数据。可选地,整车数据可以是过滤后的数据,还可以是过滤前的数据。整车数据可以包括:车辆轮端驱动力、道路坡度、车辆行驶速度等。可选地,整车数据可以是直接检测得到的,还可以是通过其他传感器检测得到的。车辆动力学模型用于表示车辆的行驶状态,其中,车辆动力学模型是包含车重的函数,具体由车辆轮端驱动力、车重、重力加速度、道路坡度、滚动阻力系数、风阻系数、迎风面积、旋转质量换算系数、车辆行驶速度、车辆加速度构成。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.
在步骤S120中,示例性地,差分方程是包含未知函数的差分及自变数的方程。在本实施例中,差分方程是关于车辆加速度、坡度、车辆轮端驱动力的方程。具体地,将车辆动力学模型通过数学转换得到差分方程,以达到解差分方程,来求车辆动力学模型的近似解的目的,使得连续问题离散化。其中,数学转换包括:线性化、拉普拉斯变换、前向差分、后向差分、Z反变换等。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.
在步骤S130中,示例性地,差分方程系数用于表示差分方程中各个参数的系数。可选地,针对差分方程中每一个参数设置一个差分方程系数,差分方程系数可以相同,也可以不同。可选地,差分方程系数可以是包含车重的系数,例如,若A=0.5*m,其中,A为差分方程系数,m为车重。具体地,每个差分方程可以确定出多组差分方程系数。协方差矩阵表示一组随机变量之间的两两线性相关性。可选地,协方差矩阵可以包括车辆加速度。具体地,可以根据仿真软件对差分方程进行仿真,就可以得到差分方程的协方差矩阵和差分方程系数。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.
在步骤S140中,示例性地,由于车辆动力学模型是包含车重的函数,而差分方程由车辆动力学模型转换得到,因此根据差分方程系数和协方差矩阵对车辆动力学模型或差分方程进行计算可以直接得到目标车辆的车重。还可以是在车辆动力学模型转换为差分方程时,将差分方程系数通过车重进行表示。这样,根据协方差矩阵选择合适的差分方程系数,即可估算出车重。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.
在本申请的技术方案中,基于车辆动力学模型确定差分方程,通过差分方程确定差分方差系数和协方差矩阵,再利用差分方差系数和协方差矩阵估算车重,可以无需借助外界设备,就能够准确地对车重进行评估。而且由于差分方程的收敛速度较快,所以,不仅可以更准确地估算车重,还可以提升估算车重的速度。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.
在一种实施方式中,如图2所示,所述利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重,包括如下步骤。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、基于所述协方差矩阵确定车辆加速度的方差最小值;S210, determining a minimum variance value of vehicle acceleration based on the covariance matrix;
S220、基于所述车辆加速度的方差最小值对应的差分方程系数和所述差分方程,计算得到车重。S220. Calculate the vehicle weight based on the differential equation coefficient corresponding to the minimum variance of the vehicle acceleration and the differential equation.
示例性地,如图3和图4所示,由于协方差矩阵的对角线上的元素为加速度的方差,且方差越小车重估算越精确,所以在对角线上的元素中选择方差最小值,确定方差最小值对应的差分方程系数,这样,使得根据上述差分方程系数和差分方程计算得到车重更加准确。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.
在一种实施方式中,所述利用所述整车数据构建车辆动力学模型,包括:基于整车数据确定所述目标车辆的运行状态;在所述运行状态满足预设的车辆运行条件的情况下,构建所述车辆动力学模型。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.
示例性地,目标车辆的运行状态可以是根据实时采集的一个或多个整车数据确定的,还可以是根据多个整车数据计算后的结果确定的。其中,运行状态可以包括:制动状态、停车状态、起步状态、怠速状态、行驶状态等。可选地,行驶状态可以包括:正常行驶状态和非正常行驶状态。可选地,非正常行驶状态包括:车辆加速度大于预设第一阈值、车辆加速度小于预设第二阈值或坡度大于预设第三阈值。其中,预设第一阈值、预设第二阈值和预设第三阈值可以是根据实际需要进行设置的,在此不作限定。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.
示例性地,预设的车辆运行条件用于表示目标车辆属于稳定运行的情况。可选地,车辆运行条件可以是一个条件,还可以是多个条件。具体地,可以是根据运行状态设置对应的车辆运行条件。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.
具体地,由于车辆换挡过程中离合器传扭无法准确获取,因此,停车状态、0油门过程(即怠速状态)无法准确估算车重。又由于刹车过程车辆的轮端驱动力的不准确,因此刹车过程(即制动状态)无法准确估算车重。车辆加速度过大或过小、坡度过大均无法准确估算车重。可以理解的是,上述情况使得目标车辆无法正常行驶,那么,在估算车重时,会导致不正常的车重跳动。这样,预设的车辆运行条件可以包括:运行状态不为停车状态、制动状态、怠速状态和非正常行驶状态中的任意一种。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.
在本实施例中,预先将满足预设的车辆运行条件的运行状态设置为对应第一标志位,将不满足预设的车辆运行条件的运行状态设置为对应第二标志位。其中,第一标志位可以为1,第二标志位可以为0,也可以根据实际需要进行设置,这样 可以及时通知工作人员,当前车辆处于不稳定的工况下。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.
在获取整车数据后,确定车辆的运行状态。若车辆的运行状态不为停车状态、制动状态、怠速状态和非正常行驶状态中的任意一种,则说明车辆处于稳定的工况下,输出标志位1。若车辆的运行状态为停车状态、制动状态、怠速状态和非正常行驶状态中的任意一种,则说明车辆处于不稳定的工况下,输出标志位0,从而可以对车辆的工况进行筛选,筛选出稳定的工况,避免车重浮动较大的问题,从而使得车重的估算更准确。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.
在一种实施方式中,所述基于所述车辆动力学模型确定对应的差分方程,包括:利用线性变换,对所述车辆动力学模型进行处理得到所述差分方程。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.
示例性地,线性变换可以包括:拉普拉斯变换、前向差分法和Z反变换等。具体地,可以采用一种线性变换方法对车辆动力学模型进行转换,也可以是结合多种线性变换方法对车辆动力学模型进行转换得到差分方程。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.
优选地,利用线性变换,对所述车辆动力学模型进行处理得到所述差分方程,包括:根据拉普拉斯变换对所述车辆动力学模型进行转换,得到中间函数;根据前向差分法和Z反变换联合对所述中间函数进行转换,得到所述差分方程。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.
在本实施例中,车辆动力学模型为:In this embodiment, the vehicle dynamics model is:
其中,F
t为车辆轮端驱动力,m为车重(kg),g为重力加速度,g取9.81m/s^2,θ为道路坡度,道路坡度通过传感器获取,f为滚动阻力系数,f取0.000056v+0.0076,Cd为风阻系数,A为迎风面积(m^2),δ为旋转质量换算系数,v为车辆行驶速度(km/h),a为车辆加速度(m/s^2)。
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).
通过泰勒展开式对sinθ进行线性化
因为滚动阻力系数很小(f=0.000056v+0.0076),滚动阻力对于车重估计影响也很小,因此可取cosθ=1进行线性化。
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.
对车辆动力学模型进行拉普拉斯变换,转化为如下形式:The vehicle dynamics model is transformed into the following form by Laplace transform:
s
2F
t(s)=(a
1s
2-6b
1)θ(s)+(d
1s
2+c
1s+e
1)a(s);
s 2 F t (s) = (a 1 s 2 -6b 1 )θ(s) + (d 1 s 2 +c 1 s+e 1 )a(s);
其中,a
1b
1c
1d
1e
1均已知。
Among them, a 1 b 1 c 1 d 1 e 1 are all known.
最后采用前向差分法
和Z反变换对上述转化后的方程进行转化得到差分方程,具体为:
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
a k =AF k +BF k-1 +CF k-2 +Da k-1 +Ea k-2 +Fθ k +Gθ k-1 +Hθ k-2
其中,a
k、a
k-1、a
k-2分别为k、k-1、k-2时刻的加速度;
分别为车辆k、k-1、k-2时刻的车辆轮端驱动力,θ
k-1、θ
k-2分别为k-1、k-2时刻的道路坡度。其中,A、B、C、D、E、F、G、H为含车重m的差分方程系数。
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.
在一种实施方式中,所述基于所述差分方程确定对应的差分方程系数和协方差矩阵,包括:对所述差分方程进行计算得到协方差矩阵;根据所述协方差矩阵更新差分方程系数,得到多组差分方程系数。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.
具体地,在MATLAB仿真软件中采用带遗忘因子的递推最小二乘法对差分方程进行计算,计算过程中得到协方差矩阵,同时在递推计算过程中更新差分方程系数,直至收敛,得到多组差分方程系数,从而实现快速且准确地对差分方程进行求解。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.
在一种实施方式中,所述目标车辆的整车数据的获取方法,包括:获取所述目标车辆的初始整车数据;对所述初始整车数据进行过滤处理,得到所述目标车辆的整车数据。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.
示例性地,初始整车数据用于表示传感器或控制器采集或接收到的车辆数据。可选地,可以是对所有初始整车数据进行过滤,也可以是对部分初始整车数据进行过滤。过滤处理可以是一阶低通滤波,还可以是其他过滤方式。具体地,采用一阶低通滤波时,不同的数据可以采用不同的滤波系数。不同的数据也可以采用相同的滤波系数,可以根据实际需要进行设置。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.
在本实施例中,如图5所示,本申请一实施例提供的车重的估算方法包括如下步骤。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,获取目标车辆的初始整车数据。S510, obtaining initial vehicle data of the target vehicle.
S520,采用一阶低通滤波对筛选后的初始整车数据进行平滑处理,得到整车数据。S520, using a first-order low-pass filter to smooth the filtered initial vehicle data to obtain vehicle data.
S530,根据整车数据确定车辆运行状态,车辆运行状态满足预设的车辆运行条件,则输出标志位1,并根据整车数据构建车辆动力学模型。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,将车辆动力学模型转换为差分方程。S540, converting the vehicle dynamics model into a differential equation.
S550,通过带遗忘因子的递推最小二乘法对差分方程进行递推计算,得到多组差分方程系数和协方差矩阵。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,选取加速度的方差最小值对应的差分方程系数对车重进行估算。S560: Select the coefficient of the differential equation corresponding to the minimum value of the variance of the acceleration to estimate the vehicle weight.
具体地,获取目标车辆的初始整车数据后,筛选需要的初始整车数据,例如,轮端驱动力、变速箱输出轴转速、道路坡度等。采用一阶低通滤波对筛选后的初 始整车数据进行平滑处理,得到整车数据,使得整车数据更加平滑,从而估算车重更加准确。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.
根据整车数据确定车辆运行状态,从而确定车辆运行状态是否满足预设的车辆运行条件,若满足,则输出标志位1,并根据整车数据构建车辆动力学模型。若不满足,则输出标志位0。利用线性变换将车辆动力学模型转换为差分方程,通过带遗忘因子的递推最小二乘法对差分方程进行递推计算,得到多组差分方程系数和协方差矩阵。由于协方差矩阵的对角线上的元素是加速度的方差,而方差越小车重估算值越准确,因此,选取车辆加速度的方差最小值对应的差分方程系数对车重进行估算。可见,采用上述方法在软件中进行仿真,运行速度快,收敛情况较好,收敛速度较快,经过模型仿真实测一般收敛速度在20-30s(步长设置为0.1s),使得车重的估算结果更准确。进一步地,还可以计算出的车重进行中位数滤波处理,避免在收敛过程中车重出现不合理的跳动。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.
相应的,图6是根据本申请一实施例的车重的估算装置的结构示意图。在一示例性实施例中,本申请实施例还提出一种车重的估算装置,该装置包括:构建模块610,用于利用目标车辆的整车数据构建车辆动力学模型;确定模块620,用于基于所述车辆动力学模型确定对应的差分方程;处理模块630,用于基于所述差分方程确定对应的差分方程系数和协方差矩阵;估算模块640,用于利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重。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.
在一种实施方式中,估算模块640,还用于:基于所述协方差矩阵确定车辆加速度的方差最小值;基于所述车辆加速度的方差最小值对应的差分方程系数和所述差分方程,计算得到车重。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.
在一种实施方式中,构建模块610,还用于:基于整车数据确定所述目标车辆的运行状态;在所述运行状态满足预设的车辆运行条件的情况下,构建所述车辆动力学模型。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.
在一种实施方式中,确定模块620,还用于:利用线性变换,对所述车辆动力学模型进行处理得到所述差分方程。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.
在一种实施方式中,利用线性变换,对所述车辆动力学模型进行处理得到所述差分方程,包括:根据拉普拉斯变换对所述车辆动力学模型进行转换,得到中间函数;根据前向差分法和Z反变换联合对所述中间函数进行转换,得到所述差分方程。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.
在一种实施方式中,处理模块630,还用于:对所述差分方程进行计算得到协方差矩阵;根据所述协方差矩阵更新差分方程系数,得到多组差分方程系数。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.
在一种实施方式中,所述装置还包括:获取模块,用于获取所述目标车辆的初始整车数据;过滤模块,用于对所述初始整车数据进行过滤处理,得到所述目标车辆的整车数据。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.
本实施例提供的车重的估算装置,与本申请上述实施例所提供的车重的估算方法属于同一申请构思,可执行本申请上述任意实施例所提供的车重的估算方法,具备执行车重的估算方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节,可参见本申请上述实施例提供的车重的估算方法的具体处理内容,此处不再加以赘述。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.
本申请另一实施例还提出一种工程车辆,如图7所示,该设备包括:控制设备;所述控制设备用于实现上述的车重的估算方法。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.
在本申请的技术方案中,工程车辆具体可以为搅拌车。工程车辆由于采用车重的估算方法,可以准确地对车重进行估算。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.
如图8所示,控制设备可以包括:存储器800和处理器810;其中,所述存储器800与所述处理器810连接,用于存储程序;所述处理器810,用于通过运行所述存储器800中存储的程序,实现上述任一实施例公开的车重的估算方法。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.
具体的,上述电子设备还可以包括:总线、通信接口820、输入设备830和输出设备840。Specifically, the electronic device may further include: a bus, a communication interface 820 , an input device 830 and an output device 840 .
处理器810、存储器800、通信接口820、输入设备830和输出设备840通过总线相互连接。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.
总线可包括一通路,在计算机***各个部件之间传送信息。A bus may include a pathway that transfers information between components of a computer system.
处理器810可以是通用处理器,例如通用中央处理器(CPU)、微处理器等,也可以是特定应用集成电路(application-specific integrated circuit,ASIC),或一个或多个用于控制本发明方案程序执行的集成电路。还可以是数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。 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.
处理器810可包括主处理器,还可包括基带芯片、调制解调器等。The processor 810 may include a main processor, and may also include a baseband chip, a modem, and the like.
存储器800中保存有执行本发明技术方案的程序,还可以保存有操作***和其他关键业务。具体地,程序可以包括程序代码,程序代码包括计算机操作指令。更具体的,存储器800可以包括只读存储器(read-only memory,ROM)、可存储静态信息和指令的其他类型的静态存储设备、随机存取存储器(random access memory,RAM)、可存储信息和指令的其他类型的动态存储设备、磁盘存储器、flash等等。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.
输入设备830可包括接收用户输入的数据和信息的装置,例如键盘、鼠标、摄像头、扫描仪、光笔、语音输入装置、触摸屏、计步器或重力感应器等。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.
输出设备840可包括允许输出信息给用户的装置,例如显示屏、打印机、扬声器等。Output device 840 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.
通信接口820可包括使用任何收发器一类的装置,以便与其他设备或通信网络通信,如以太网,无线接入网(RAN),无线局域网(WLAN)等。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.
处理器810执行存储器800中所存放的程序,以及调用其他设备,可用于实现本申请上述实施例所提供的任意一种车重的估算方法的各个步骤。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.
除了上述方法和设备以外,本申请的实施例还可以是计算机程序产品,其包括计算机程序指令,所述计算机程序指令在被处理器运行时使得所述处理器执行本说明书上述“示例性方法”部分中描述的根据本申请各种实施例的车重的估算方法中的步骤。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.
所述计算机程序产品可以以一种或多种程序设计语言的任意组合来编写用于执行本申请实施例操作的程序代码,所述程序设计语言包括面向对象的程序设计语言,诸如Java、C++等,还包括常规的过程式程序设计语言,诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。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.
此外,本申请的实施例还可以是存储介质,其上存储有计算机程序,计算机程序被处理器执行本说明书上述“示例性方法”部分中描述的根据本申请各种实施例的车重的估算方法中的步骤。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.
上述的电子设备的具体工作内容,以及上述的计算机程序产品和存储介质上的计算机程序被处理器运行时的具体工作内容,均可以参见上述的方法实施例的内容,此处不再赘述。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.
对于前述的各方法实施例,为了简单描述,故将其都表述为一系列的动作组合,但是本领域技术人员应该知悉,本申请并不受所描述的动作顺序的限制,因为依据本申请,某些步骤可以采用其他顺序或者同时进行。其次,本领域技术人员也应该知悉,说明书中所描述的实施例均属于优选实施例,所涉及的动作和模块并不一定是本申请所必须的。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.
需要说明的是,本说明书中的各个实施例均采用递进的方式描述,每个实施例重点说明的都是与其他实施例的不同之处,各个实施例之间相同相似的部分互相参见即可。对于装置类实施例而言,由于其与方法实施例基本相似,所以描述 的比较简单,相关之处参见方法实施例的部分说明即可。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.
本申请各实施例方法中的步骤可以根据实际需要进行顺序调整、合并和删减,各实施例中记载的技术特征可以进行替换或者组合。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.
本申请各实施例种装置及终端中的模块和子模块可以根据实际需要进行合并、划分和删减。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.
本申请所提供的几个实施例中,应该理解到,所揭露的终端,装置和方法,可以通过其它的方式实现。例如,以上所描述的终端实施例仅仅是示意性的,例如,模块或子模块的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个子模块或模块可以结合或者可以集成到另一个模块,或一些特征可以忽略,或不执行。另一点,所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口,装置或模块的间接耦合或通信连接,可以是电性,机械或其它的形式。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.
作为分离部件说明的模块或子模块可以是或者也可以不是物理上分开的,作为模块或子模块的部件可以是或者也可以不是物理模块或子模块,即可以位于一个地方,或者也可以分布到多个网络模块或子模块上。可以根据实际的需要选择其中的部分或者全部模块或子模块来实现本实施例方案的目的。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.
另外,在本申请各个实施例中的各功能模块或子模块可以集成在一个处理模块中,也可以是各个模块或子模块单独物理存在,也可以两个或两个以上模块或子模块集成在一个模块中。上述集成的模块或子模块既可以采用硬件的形式实现,也可以采用软件功能模块或子模块的形式实现。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.
专业人员还可以进一步意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、计算机软件或者二者的结合来实现,为了清楚地说明硬件和软件的可互换性,在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。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.
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处理器执行的软件单元,或者二者的结合来实施。软件单元可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。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.
最后,还需要说明的是,在本文中,诸如第一和第二等之类的关系术语仅仅 用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。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.
对所公开的实施例的上述说明,使本领域技术人员能够实现或使用本申请。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的,本文中所定义的一般原理可以在不脱离本申请的精神或范围的情况下,在其它实施例中实现。因此,本申请将不会被限制于本文所示的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。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 (16)
- 一种车重的估算方法,包括:A method for estimating vehicle weight, comprising:利用目标车辆的整车数据构建车辆动力学模型;Use the whole vehicle data of the target vehicle to build a vehicle dynamics model;基于所述车辆动力学模型确定对应的差分方程;determining a corresponding differential equation based on the vehicle dynamics model;基于所述差分方程确定对应的差分方程系数和协方差矩阵;Determining corresponding difference equation coefficients and a covariance matrix based on the difference equation;利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重。The vehicle weight of the target vehicle is estimated using the difference equation coefficients and the covariance matrix.
- 根据权利要求1所述的方法,其中,所述利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重,包括: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:基于所述协方差矩阵确定车辆加速度的方差最小值;Determining a minimum variance value of vehicle acceleration based on the covariance matrix;基于所述车辆加速度的方差最小值对应的差分方程系数和所述差分方程,计算得到车重。The vehicle weight is calculated based on the differential equation coefficient corresponding to the minimum variance of the vehicle acceleration and the differential equation.
- 根据权利要求1或2所述的方法,其中,所述利用所述整车数据构建车辆动力学模型,包括:The method according to claim 1 or 2, wherein the constructing a vehicle dynamics model using the whole vehicle data comprises:基于整车数据确定所述目标车辆的运行状态;Determining the operating status of the target vehicle based on the vehicle data;在所述运行状态满足预设的车辆运行条件的情况下,构建所述车辆动力学模型。When the operating state satisfies a preset vehicle operating condition, the vehicle dynamics model is constructed.
- 根据权利要求3所述的方法,其中,所述运行状态包括:制动状态、停车状态、起步状态、怠速状态、行驶状态,其中,所述行驶状态包括:正常行驶状态和非正常行驶状态。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.
- 根据权利要求4所述的方法,其中,所述非正常行驶状态包括:车辆加速度大于预设第一阈值、车辆加速度小于预设第二阈值或坡度大于预设第三阈值。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.
- 根据权利要求5所述的方法,其中,所述预设的车辆运行条件包括:所述运行状态不为所述停车状态、所述制动状态、所述怠速状态和所述非正常行驶状态中的任意一种。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.
- 根据权利要求1至6任一项所述的方法,其中,所述基于所述车辆动力学模型确定对应的差分方程,包括:The method according to any one of claims 1 to 6, wherein determining the corresponding differential equation based on the vehicle dynamics model comprises:利用线性变换,对所述车辆动力学模型进行处理得到所述差分方程。The vehicle dynamics model is processed using linear transformation to obtain the differential equation.
- 根据权利要求7所述的方法,其中,所述利用线性变换,对所述车辆动力学模型进行处理得到所述差分方程,包括:The method according to claim 7, wherein the step of processing the vehicle dynamics model using linear transformation to obtain the differential equation comprises:根据拉普拉斯变换对所述车辆动力学模型进行转换,得到中间函数;Converting the vehicle dynamics model according to Laplace transform to obtain an intermediate function;根据前向差分法和Z反变换联合对所述中间函数进行转换,得到所述差分方程。The intermediate function is transformed according to the forward difference method and the Z-inverse transformation to obtain the differential equation.
- 根据权利要求1至8任一项所述的方法,其中,所述基于所述差分方程确 定对应的差分方程系数和协方差矩阵,包括: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:对所述差分方程进行计算得到协方差矩阵;Calculating the difference equation to obtain a covariance matrix;根据所述协方差矩阵更新差分方程系数,得到多组差分方程系数。The difference equation coefficients are updated according to the covariance matrix to obtain multiple groups of difference equation coefficients.
- 根据权利要求1至9任一项所述的方法,其中,所述目标车辆的整车数据的获取方法,包括: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:获取所述目标车辆的初始整车数据;Acquiring initial vehicle data of the target vehicle;对所述初始整车数据进行过滤处理,得到所述目标车辆的整车数据。The initial whole vehicle data is filtered to obtain the whole vehicle data of the target vehicle.
- 根据权利要求10所述的方法,其中,所述过滤处理是一阶低通滤波。The method according to claim 10, wherein the filtering process is a first order low pass filtering.
- 根据权利要求1至11任一项所述的方法,其中,所述目标车辆的种类是搅拌车。The method according to any one of claims 1 to 11, wherein the type of the target vehicle is a mixer truck.
- 根据权利要求1至12任一项所述的方法,其中,所述整车数据包括:车辆轮端驱动力、道路坡度、车辆行驶速度。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.
- 一种车重的估算装置,包括:A vehicle weight estimation device, comprising:构建模块,用于利用目标车辆的整车数据构建车辆动力学模型;A construction module for constructing a vehicle dynamics model using the full vehicle data of the target vehicle;确定模块,用于基于所述车辆动力学模型确定对应的差分方程;A determination module, configured to determine a corresponding differential equation based on the vehicle dynamics model;处理模块,用于基于所述差分方程确定对应的差分方程系数和协方差矩阵;A processing module, configured to determine corresponding difference equation coefficients and a covariance matrix based on the difference equation;估算模块,用于利用所述差分方程系数和所述协方差矩阵,估算所述目标车辆的车重。An estimation module is used to estimate the vehicle weight of the target vehicle by using the differential equation coefficients and the covariance matrix.
- 一种工程车辆,包括:An engineering vehicle, comprising:控制设备,所述控制设备用于执行如权利要求1至13中任意一项所述的车重的估算方法。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.
- 一种存储介质,所述存储介质上存储有计算机程序,所述计算机程度被处理器运行时,实现如权利要求1至13中任意一项所述的车重的估算方法。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.
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