WO2024130699A1

WO2024130699A1 - Method and system for estimating vehicle mass and drag coefficient

Info

Publication number: WO2024130699A1
Application number: PCT/CN2022/141402
Authority: WO
Inventors: 杨佳宁
Original assignee: 采埃孚商用车系统(青岛)有限公司
Priority date: 2022-12-23
Filing date: 2022-12-23
Publication date: 2024-06-27

Abstract

A method and system for estimating a vehicle mass and a drag coefficient. The method for estimating a vehicle mass and a drag coefficient may comprise: initializing value ranges and initial distributions of a vehicle mass and a drag coefficient; performing Monte Carlo sampling on the vehicle mass and the drag coefficient on the basis of the value ranges and the initial distributions to obtain a plurality of groups of vehicle mass and drag coefficient sampling values; determining a predicted vehicle driving force for each group of vehicle mass and drag coefficient sampling values according to a vehicle dynamics model and the plurality of groups of vehicle mass and drag coefficient sampling values; calculating a loss rate of the predicted vehicle driving force of each group of vehicle mass and drag coefficient sampling values according to a loss function; and when the calculation result converges, taking the group of vehicle mass and drag coefficient sampling values having the minimum loss rate as an estimation result. The method and the system can reduce the complexity of programming.

Description

Method and system for estimating vehicle mass and drag coefficient

Technical Field

The present application relates to vehicle parameter and environmental parameter estimation, and more particularly to techniques for estimation of vehicle mass and drag coefficient.

Background technique

When using the vehicle dynamics equation to calculate the vehicle mass and drag coefficient, the existing calculation method often samples at two close time points under the assumption that the driving resistance and ramp resistance remain unchanged in a short period of time. The vehicle traction and acceleration can be derived from fixed parameters such as engine torque and vehicle speed, and the vehicle dynamics equation for the two time points is subtracted and divided to obtain the vehicle mass. After that, the drag coefficient can be further calculated based on the vehicle dynamics equation and the vehicle mass.

In the calculation method of the prior art, the vehicle mass and the drag coefficient are calculated separately, and the vehicle mass is calculated first and then the drag coefficient. In addition, when the acceleration between two samples is almost the same, the numerical calculation error is often too large or wrong because the denominator is very small (close to zero) or zero. Furthermore, the selection of the time point of the two samples will significantly affect the accuracy of the final calculation result, which further increases the complexity of programming when implemented in a computer program.

Summary of the invention

A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

In order to overcome the above-mentioned defects in the prior art, the present disclosure provides a method and system capable of simultaneously estimating vehicle mass and drag coefficient.

According to a first aspect of the present disclosure, a method for estimating a vehicle mass and a drag coefficient is provided, comprising: initializing a value range and an initial distribution of the vehicle mass and the drag coefficient; performing Monte Carlo sampling on the vehicle mass and the drag coefficient based on the value range and the initial distribution to obtain a plurality of groups of vehicle mass and drag coefficient sampling values; determining a predicted vehicle driving force for each group of vehicle mass and drag coefficient sampling values according to a vehicle dynamics model and the plurality of groups of vehicle mass and drag coefficient sampling values; calculating a loss rate of the predicted vehicle driving force for each group of vehicle mass and drag coefficient sampling values according to a loss function; and taking a group of vehicle mass and drag coefficient sampling values with a minimum loss rate as an estimation result when the calculation results converge.

According to a second aspect of the present disclosure, a system for estimating vehicle mass and drag coefficient is provided, comprising: a processor; and a memory coupled to the processor, the memory storing instructions for executing the method steps described in the first aspect of the present disclosure.

According to a third aspect of the present disclosure, there is provided a computer-readable medium having instructions stored thereon, which, when executed by a processor, implement the method steps as described in the first aspect of the present disclosure.

Compared with the prior art, in the present application, the Monte Carlo method is introduced and multiple samplings are performed to complete the estimation of the vehicle mass and the drag coefficient. Under the premise of ensuring a sufficient number of sampling times, the technical solution disclosed in the present application can estimate the vehicle mass and the drag coefficient at the same time rather than in steps while ensuring the accuracy of the estimation of the vehicle mass and the drag coefficient, and avoids excessive numerical calculation errors or errors that may be introduced due to the use of division, and simplifies the programming complexity because it does not require careful selection of the sampling time points.

BRIEF DESCRIPTION OF THE DRAWINGS

The above features and advantages of the present disclosure can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

FIG. 1 is an example flow chart illustrating an example method for estimating vehicle mass and drag coefficient according to aspects of the present disclosure.

FIG. 2 is a schematic diagram illustrating a vehicle dynamics model for predicting vehicle driving force according to aspects of the present disclosure.

FIG. 3 is a schematic diagram illustrating a vehicle powertrain model for calculating actual vehicle driving force according to aspects of the present disclosure.

4 is a diagram illustrating a computer that may include various components configured to perform operations for the techniques disclosed herein, in accordance with aspects of the present disclosure.

Unless otherwise noted, it should be appreciated that modules/components referenced with the same/similar reference numbers across the various figures generally refer to the same modules/components.

Detailed ways

The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may be extended based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description.

Methods for estimating vehicle mass using vehicle dynamics are used in Automated Mechanical Transmission (AMT) and Electronically Controlled Brake System (EBS) systems. Methods for estimating driving resistance are commonly used in autonomous driving (AD) or advanced driving assisted system (ADAS) software systems. Both methods are based on the use of vehicle dynamics equations, as shown below.

where F is the traction, m is the vehicle mass, g is the acceleration due to gravity, α is the ramp angle, f _r0 and f _r1 are the rolling resistance coefficients, v is the vehicle speed, v _wind is the wind speed, C _w is the aerodynamic coefficient, ρ is the air density, and A _veh is the vehicle frontal area.

Assuming that α is small (cosα≈1) and assuming that v _wind can be ignored, the above equation (1) can be rewritten as:

For each product in the above formula (2), ma represents the inertial force, mg (f _r0 + f _r1 v) is the rolling resistance,

is the air resistance, and mgsinα is the ramp resistance.

The above formula (2) can be further rewritten as:

F＝ma+A+Bv+Cv ² +mgsinα. (3)

The product A+Bv+ ^Cv2 represents the total running resistance, wherein A+Bv represents the rolling resistance and ^Cv2 represents the air resistance. This method of expressing the running resistance is generally referred to in the art as the ABC method.

In the existing method for calculating the vehicle mass and drag coefficient, it is assumed that the driving resistance and the slope resistance remain unchanged in a short time, and sampling is performed at two close time points _t1 and _t2 . The above formula (3) at these two sampling time points is subtracted to obtain:

m＝(F ₁ −F ₂ )/(a ₁ −a ₂ ) (4)

The traction force and acceleration can be derived from the actually measured engine torque, vehicle speed, etc. The above formula (4) can also be rewritten in other forms (for example, power or energy forms). After the vehicle mass is obtained according to formula (4), it is substituted into formula (3). When the road slope is known, for example, according to the slope sensor installed in the AMT, the driving resistance can be obtained.

However, in the above calculation method, the vehicle mass and the drag coefficient are calculated separately, and the vehicle mass is calculated first and then the drag coefficient. In addition, when the accelerations _a1 and _a2 between two samplings are almost the same, the numerical calculation error is often too large or wrong because the numerator is very small (close to zero) or zero. Furthermore, the selection of the time points _t1 and _t2 of the two samplings will significantly affect the accuracy of the final calculation results, which further increases the complexity of programming when implemented in a computer program.

According to the technical solution of the present application, the estimation of the vehicle mass and drag coefficient is completed by introducing the Monte Carlo method and performing multiple sampling. Under the premise of ensuring a sufficient number of sampling times, the technical solution of the present disclosure can estimate the vehicle mass and drag coefficient at the same time rather than in steps while ensuring the estimation accuracy of the vehicle mass and drag coefficient, and avoids excessive numerical calculation errors or errors that may be introduced by the use of division, and simplifies programming complexity because it does not require careful selection of sampling time points.

Referring first to FIG. 1 , FIG. 1 is a flow chart illustrating an example method 100 for estimating vehicle mass and drag coefficient in accordance with aspects of the present disclosure.

The method 100 may include, at step 105, initializing the range and initial distribution of the vehicle mass and the drag coefficient. In some examples, the vehicle mass and the drag coefficient may include the vehicle mass m and the drag coefficient, such as the constant term A, the linear term coefficient B, and the quadratic term coefficient C, as shown above in equation (3).

In some examples, the range of values of the vehicle mass and the drag coefficient can be determined according to the actual conditions of the parameters, for example, according to one or more of the vehicle type, load conditions, national standards, etc. In the example of a 49-ton semi-trailer tractor, the range of values of the vehicle mass m can be from 8 tons (only the front of the vehicle) to 50 tons (overloaded by 1 ton). In this example, the range of values of the constant term A, the first-order coefficient B, and the second-order coefficient C in the drag coefficient can be determined by referring to, for example, the standard GB/T 18386-2017, as shown in Table 1 below. It should be noted that, according to experience, the values in Table 1 are usually larger than the values of the actual vehicle. In some examples, the range of values of the constant term A in the drag coefficient can be 1000 to 3000, the range of values of the first-order coefficient B in the drag coefficient can be 0 to 25, and the range of values of the second-order coefficient C in the drag coefficient can be 0.2 to 0.3.

Table 1

In some examples, the initial distribution of the vehicle mass and the drag coefficient may be selected from an existing probability distribution model. In some examples, the initial distribution of the vehicle mass m and the drag coefficients A, B, and C may be determined to be uniformly distributed. In the example of a 49-ton semi-trailer tractor, the initial distribution of the vehicle mass m may be determined to be uniformly distributed in the range from 8 tons to 50 tons. It should be noted that the distribution of the vehicle mass m and the drag coefficients A, B, and C may be updated as the results of the calculations and iterative executions are performed, as will be described in more detail below.

The method 100 may include, at step 110, performing Monte Carlo sampling on the vehicle mass and the drag coefficient based on the value range and the initial distribution to obtain multiple groups of vehicle mass and drag coefficient sample values. Monte Carlo sampling is a sampling method known to those skilled in the art and will not be described in detail herein. In some examples, step 110 may include, for each sampling, randomly sampling values according to the value range and distribution of the parameters. In some examples, step 110 may include performing N Monte Carlo sampling on the vehicle mass m and the drag coefficients A, B, and C based on the value range and the initial distribution to obtain N groups of vehicle mass and drag coefficient sample values (m, A, B, C). For the number of samplings N, the number of initial samplings may be determined to be a larger number, such as 100. Because it is not known where the true value falls at the beginning, a larger number of initial samplings can prevent omissions. It should be noted that as the number of iterations of the calculation increases, the number of samplings may gradually decrease, for example, 100->80->60->50->40->..., as will be described in more detail below.

Next, method 100 may include, at step 115, determining a predicted vehicle driving force for each set of vehicle mass and drag coefficient sample values based on a vehicle dynamics model and a plurality of sets of vehicle mass and drag coefficient sample values. Step 115 may be described in conjunction with FIG. 1 and FIG. 2 . FIG. 2 schematically shows a diagram of a vehicle dynamics model. As shown in FIG. 2 , the vehicle dynamics model may take vehicle speed and road slope as inputs, and based on the N sets of vehicle mass and drag coefficient sample values sampled in step 110, calculate N predicted vehicle driving forces for each set of vehicle mass and drag coefficient sample values according to the above formula (3). In some examples, the vehicle speed and road slope may be known.

Method 100 may then include, at step 120, calculating the loss rate of the predicted vehicle driving force for each set of vehicle mass and drag coefficient sample values according to the loss function. Step 120 may be described in conjunction with Figures 1 and 3. Figure 3 schematically shows a diagram of a vehicle powertrain model. As shown in Figure 3, the vehicle powertrain model may use the actual measured engine torque as input to calculate the actual vehicle driving force based on the fixed parameters of the vehicle powertrain. In some examples, the fixed parameters of the vehicle powertrain may include one or more of the following or a combination thereof: transmission type, shifting strategy, gear ratios of each gear, final reducer ratio, transmission efficiency. In some examples, step 120 may include calculating the loss rate of the predicted vehicle driving force for each set of vehicle mass and drag coefficient sample values based on the predicted vehicle driving force and the actual vehicle driving force according to the loss function as shown below to obtain N loss rates:

Where _Fact is the actual vehicle driving force, _Fcal is the predicted vehicle driving force for each set of vehicle mass and drag coefficient sample values, and t is the duration of each sampling. In some examples, based on experience, the value range n of t can be 0.01 seconds-120 seconds. In some examples, n can be preferably determined to be 20 seconds. It should be understood by those skilled in the art that the above value of n is only an example, and other values may be adopted in other implementations depending on actual requirements without departing from the scope of the present application. The value of the loss rate reflects the degree of closeness between the predicted vehicle driving force and the actual vehicle driving force for each set of vehicle mass and drag coefficient sample values within the duration n. In other words, the smaller the loss rate, the greater the possibility that the set of vehicle mass and drag coefficient sample values (m, A, B, C) are close to the true value. In some examples, the vehicle dynamics model and vehicle powertrain model mentioned in step 115 and step 120 can be implemented by a computer program, and will not be repeated here.

Continuing to refer to FIG1 , the method 100 may include, at step 125, determining whether the calculation result converges. The method 100 may further include, at step 130, when the calculation result converges, taking a set of vehicle mass and drag coefficient sample values with a minimum loss rate as the estimation result, or, at step 135, when the calculation result does not converge, adjusting the distribution of one or more of the vehicle mass and the drag coefficient according to the calculated loss rate and iteratively executing steps 110 to 125 based on the adjusted distribution until the estimation result is obtained.

In some examples, step 125 may include determining that the calculation result converges when the number of groups whose predicted vehicle driving force loss rate is lower than the loss rate threshold calculated in step 120 reaches a convergence threshold. In the example of a 49-ton semi-trailer tractor, assuming that the initial sampling number N is 100, when the number of groups whose predicted vehicle driving force loss rate is lower than 10% reaches 90 (assuming that the convergence threshold is set to 90% of the sampling number N), the calculation result converges. In some examples, one or both of the loss rate threshold and the convergence threshold may be determined depending on a variety of factors, including signal accuracy, calculation sampling time, etc. In some examples, one or both of the loss rate threshold and the convergence threshold may be dynamically adjusted by the operator according to the sampling results during the sampling process. In some examples, the range of the loss rate threshold may be between 1%-25%. In some examples, the range of the convergence threshold may be between 60%-99% of the sampling number.

In some examples, the loss rate threshold and the convergence threshold can be set by the user. It should be understood by those skilled in the art that the specific values of the above loss rate threshold and the convergence threshold are only an example, and other values may be adopted in other implementations depending on actual requirements without departing from the scope of the present application.

In some examples, step 130 may include, when it is determined in step 125 that the calculation results converge, for example, when the number of groups in which the calculated predicted vehicle driving force loss rate is less than 10% reaches 90, taking a set of sample values of vehicle mass and drag coefficient with the lowest loss rate as the estimation result.

In some examples, step 135 may include, when it is determined in step 125 that the calculation result does not converge, for example, when the number of groups whose calculated loss rate of predicted vehicle driving force is less than 10% is less than 90, adjusting the distribution of vehicle mass from an initial distribution uniformly distributed between 8 tons and 50 tons to an adjusted distribution uniformly distributed between 15 and 45 tons according to the calculated N loss rates, and performing steps 110-125 based on the adjusted distribution until an estimated result is obtained when the calculation result converges. In some examples, in step 135, when performing steps 110-125 based on the adjusted distribution, the number of sampling times N may be reduced with each iteration. In some examples, when performing steps 110-125 based on a uniformly distributed vehicle mass between 15 and 45 tons, the number of sampling times N may be reduced from 100 to 80. As mentioned above, as the number of iterations of the calculation increases, the number of sampling times may gradually decrease, for example, 100->80->60->50->40->…. Those skilled in the art will appreciate that the specific value of the sampling times and the reduction range are merely examples, and other values may be used in other implementations depending on actual requirements without departing from the scope of the present application. In some examples, the fixed parameters of the vehicle powertrain involved in step 115, the sampling duration involved in step 120, the loss rate threshold and the convergence threshold involved in step 125, and other parameters may be determined during the initialization in step 105.

Next, refer to Figure 4, which is a block diagram of an exemplary computer system 012 suitable for implementing computers in some embodiments of the present disclosure. The computer system 012 shown in Figure 4 is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

As shown in Figure 4, computer system 012 is presented in the form of a general-purpose computing device. Components of computer system 012 may include, but are not limited to: one or more processors or processing units 016, system memory 028, and bus 018 connecting different system components (including system memory 028 and processing unit 016).

Bus 018 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

The computer system 012 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer system 012, including volatile and non-volatile media, removable and non-removable media.

The system memory 028 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 030 and/or cache memory 032. The computer system 012 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, the storage system 034 may be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 4 , commonly referred to as a “hard drive”). Although not shown in FIG. 4 , a disk drive for reading and writing a removable non-volatile disk (such as a “floppy disk”) and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical media) may be provided. In these cases, each drive may be connected to the bus 018 via one or more data medium interfaces. The memory 028 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

A program/utility 040 having a set (at least one) of program modules 042 may be stored, for example, in the memory 028. Such program modules 042 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 042 generally perform the functions and/or methods of the embodiments described in the present disclosure.

The computer system 012 may also communicate with one or more external devices 014 (e.g., keyboard, pointing device, display 024, etc.). In the present disclosure, the computer system 012 communicates with an external radar device, and may also communicate with one or more devices that enable a user to interact with the computer system 012, and/or communicate with any device that enables the computer system 012 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input/output (I/O) interface 022. In addition, the computer system 012 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through a network adapter 020. As shown, the network adapter 020 communicates with other modules of the computer system 012 through a bus 018. It should be understood that, although not shown in FIG. 4 , other hardware and/or software modules may be used in conjunction with the computer system 012, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

The processing unit 016 executes various functional applications and data processing by running the programs stored in the system memory 028, such as implementing the method flow provided by the present invention.

According to another aspect of the present disclosure, an embodiment of a non-transitory computer storage medium is provided, on which computer executable instructions are stored. When the computer executable instructions are executed by a computer, the computer performs the operations of any aspect of the method for estimating vehicle mass and drag coefficient as described above.

The computer program may be provided in a computer storage medium, that is, the computer storage medium is encoded with a computer program, and when the program is executed by one or more computers, the one or more computers execute the method flow and/or device operation shown in the above embodiments of the present disclosure. For example, the method flow provided by the embodiments of the present disclosure is executed by the above one or more processors.

With the development of time and technology, the meaning of medium is becoming more and more extensive, and the dissemination of computer programs is no longer limited to tangible media, but can also be directly downloaded from the Internet, etc. Any combination of one or more computer-readable media can be used.

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

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

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

It will be appreciated by those skilled in the art that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, boxes, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Technicians may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of this disclosure.

The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from/to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

A method for estimating vehicle mass and drag coefficient, comprising:

Step 1: Initialize the value range and initial distribution of vehicle mass and drag coefficient;

Step 2, performing Monte Carlo sampling on the vehicle mass and the drag coefficient based on the value range and the initial distribution to obtain multiple groups of vehicle mass and drag coefficient sampling values;

Step 3, determining a predicted vehicle driving force for each set of vehicle mass and drag coefficient sample values according to the vehicle dynamics model and the plurality of sets of vehicle mass and drag coefficient sample values;

Step 4, calculating the loss rate of the predicted vehicle driving force for each set of vehicle mass and drag coefficient sample values according to the loss function; and

Step 51: When determining that the calculation results have converged, a set of vehicle mass and drag coefficient sampling values with a minimum loss rate is taken as the estimation result.
The method according to claim 1, characterized in that the method further comprises:

Step 52: when it is determined that the calculation result does not converge, adjusting the distribution of one or more of the vehicle mass and the drag coefficient according to the calculated loss rate;

Steps 2 to 4 are iteratively performed based on the adjusted distribution until an estimation result is obtained.
The method of claim 1, wherein the drag coefficient comprises a constant term A, a linear term coefficient B, and a quadratic term coefficient C of the drag coefficient in the following vehicle dynamics equation:

F＝ma+A+Bv+ Cv2 +mgsinα

Where F is the traction force, m is the vehicle mass, a is the vehicle acceleration, v is the vehicle speed, g is the acceleration due to gravity, and α is the ramp angle.
The method as claimed in claim 1 is characterized in that initializing the value range and initial distribution of the vehicle mass and drag coefficient further includes: determining the value range and initial distribution of the vehicle mass and drag coefficient according to one or more of the vehicle type, load condition, national standards, etc.
The method of claim 1, wherein the loss function comprises:

Where F act is the actual vehicle driving force, F cal is the predicted vehicle driving force for each set of vehicle mass and drag coefficient sample values, and t is the duration of each sampling.
The method as claimed in claim 5 is characterized in that the actual vehicle driving force is calculated based on the measured actual engine torque and fixed parameters of the vehicle powertrain, wherein the fixed parameters of the vehicle powertrain include one or more of the following: gearbox type, shifting strategy, speed ratio of each gear, speed ratio of the final reducer, and transmission efficiency.
The method as claimed in claim 1 is characterized in that the step 51 further includes determining that the calculation result converges when the number of groups whose loss rate of the predicted vehicle driving force is lower than the loss rate threshold reaches a convergence threshold, wherein the loss rate threshold and the convergence threshold can be set by a user.
The method of claim 2, wherein the number of the plurality of sets of vehicle mass and drag coefficient sample values is reduced with each iteration.
A system for estimating vehicle mass and drag coefficient, comprising:

Processor; and

A memory coupled to the processor, the memory storing instructions for executing the method according to any one of claims 1-8.
A computer readable medium having instructions stored thereon, which when executed by a processor implement the operations of any of the methods of claims 1-8.