WO2024130699A1 - 车辆质量和阻力系数的估算方法和系统 - Google Patents

车辆质量和阻力系数的估算方法和系统 Download PDF

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Publication number
WO2024130699A1
WO2024130699A1 PCT/CN2022/141402 CN2022141402W WO2024130699A1 WO 2024130699 A1 WO2024130699 A1 WO 2024130699A1 CN 2022141402 W CN2022141402 W CN 2022141402W WO 2024130699 A1 WO2024130699 A1 WO 2024130699A1
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Prior art keywords
drag coefficient
vehicle
vehicle mass
mass
driving force
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PCT/CN2022/141402
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English (en)
French (fr)
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杨佳宁
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采埃孚商用车系统(青岛)有限公司
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Priority to PCT/CN2022/141402 priority Critical patent/WO2024130699A1/zh
Publication of WO2024130699A1 publication Critical patent/WO2024130699A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight

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  • the present application relates to vehicle parameter and environmental parameter estimation, and more particularly to techniques for estimation of 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.
  • the vehicle mass and the drag coefficient are calculated separately, and the vehicle mass is calculated first and then the drag coefficient.
  • the numerical calculation error is often too large or wrong because the denominator is very small (close to zero) or zero.
  • 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.
  • the present disclosure provides a method and system capable of simultaneously estimating vehicle mass and drag coefficient.
  • a method for estimating a vehicle mass and a drag coefficient 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.
  • a system for estimating vehicle mass and drag coefficient 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.
  • 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.
  • the Monte Carlo method is introduced and multiple samplings are performed to complete the estimation of the vehicle mass and the drag coefficient.
  • 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.
  • 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.
  • FIG. 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.
  • modules/components referenced with the same/similar reference numbers across the various figures generally refer to the same modules/components.
  • AMT Automated Mechanical Transmission
  • EBS Electronically Controlled Brake System
  • AD autonomous driving
  • ADAS advanced driving assisted system
  • F is the traction
  • m is the vehicle mass
  • g is the acceleration due to gravity
  • ⁇ 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
  • ⁇ is the air density
  • a veh is the vehicle frontal area.
  • ma represents the inertial force
  • mg (f r0 + f r1 v) is the rolling resistance
  • mg (f r0 + f r1 v) is the rolling resistance
  • mgsin ⁇ is the ramp resistance.
  • 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.
  • 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).
  • the driving resistance can be obtained.
  • the vehicle mass and the drag coefficient are calculated separately, and the vehicle mass is calculated first and then the drag coefficient.
  • the numerical calculation error is often too large or wrong because the numerator is very small (close to zero) or zero.
  • 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.
  • the estimation of the vehicle mass and drag coefficient is completed by introducing the Monte Carlo method and performing multiple sampling.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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).
  • 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.
  • 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
  • the range of values of the second-order coefficient C in the drag coefficient can be 0.2 to 0.3.
  • 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.
  • step 110 may include, for each sampling, randomly sampling values according to the value range and distribution of the parameters.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the number of sampling times N may be reduced with each iteration.
  • 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.
  • 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.
  • Figure 4 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.
  • 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.
  • 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.
  • ISA Industry Standard Architecture
  • MAC Micro Channel Architecture
  • VESA Video Electronics Standards Association
  • PCI Peripheral Component Interconnect
  • 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.
  • 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”).
  • a disk drive for reading and writing a removable non-volatile disk such as a “floppy disk”
  • 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)
  • 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.).
  • 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.
  • I/O input/output
  • 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.
  • networks e.g., a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet
  • the network adapter 020 communicates with other modules of the computer system 012 through a bus 018.
  • 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.
  • an embodiment of a non-transitory computer storage medium on which computer executable instructions are stored.
  • the computer executable instructions When 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.
  • the method flow provided by the embodiments of the present disclosure is executed by the above one or more processors.
  • 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.
  • 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.
  • 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).
  • LAN local area network
  • WAN wide area network
  • Internet service provider e.g., via the Internet using an Internet service provider
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • 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.
  • 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.
  • the processor and the storage medium may reside in a user terminal as discrete components.

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Abstract

一种车辆质量和阻力系数的估算方法和系统，该车辆质量和阻力系数估算方法可包括：对车辆质量和阻力系数的取值范围和初始分布进行初始化；基于取值范围和初始分布对车辆质量和阻力系数进行蒙特卡洛采样以得到多组车辆质量和阻力系数采样值;根据车辆动力学模型以及多组车辆质量和阻力系数采样值来确定针对每一组车辆质量和阻力系数采样值的预测车辆驱动力；根据损失函数计算每一组车辆质量和阻力系数采样值的预测车辆驱动力的损失率；以及在计算结果收敛时取具有最小损失率的一组车辆质量和阻力系数采样值作为估算结果，该方法和系统可减小编程的复杂性。

Description

车辆质量和阻力系数的估算方法和系统 技术领域
本申请涉及车辆参数和环境参数估计，且更具体地涉及用于车辆质量和阻力系数的估算的技术。
背景技术
在使用车辆动力学方程来计算车辆质量和阻力系数时，现有的计算方法往往在行驶阻力和坡道阻力在短时间内保持不变的假定下在两个接近的时间点进行采样。通过根据引擎扭矩、车速等固定参数可推导出车辆牵引力和加速度，将针对两个时间点的车辆动力学方程通过相减并相除的方法来求得车辆质量。之后，可进一步根据车辆动力学方程和车辆质量来计算阻力系数。
在现有技术的计算方法中，车辆质量和阻力系数是分开计算的，并且先计算车辆质量随后再计算阻力系数。此外，当两次采样之间的加速度相差无几时，往往会因为分母非常小(接近于零)或为零而导致数值计算误差过大或出错。进一步，两次采样的时间点的选择会显著影响最终计算结果的准确性，这进一步提升了以计算机程序实现时编程的复杂性。
发明内容
以下给出一个或多个方面的简要概述以提供对这些方面的基本理解。此概述不是所有构想到的方面的详尽综览，并且既非旨在指认出所有方面的关键性或决定性要素亦非试图界定任何或所有方面的范围。其唯一的目的是要以简化形式给出一个或多个方面的一些概念以为稍后给出的更加详细的描述之序。
为了克服现有技术存在的上述缺陷，本公开提供了一种能够同时估算车辆质量和阻力系数的方法和系统。
根据本公开的第一方面，提供了一种用于估算车辆质量和阻力系数的方法，包括：对车辆质量和阻力系数的取值范围和初始分布进行初始化；基于取值范围和初 始分布对车辆质量和阻力系数进行蒙特卡洛采样以得到多组车辆质量和阻力系数采样值；根据车辆动力学模型以及多组车辆质量和阻力系数采样值来确定针对每一组车辆质量和阻力系数采样值的预测车辆驱动力；根据损失函数计算每一组车辆质量和阻力系数采样值的预测车辆驱动力的损失率；以及在计算结果收敛时取具有最小损失率的一组车辆质量和阻力系数采样值作为估算结果。
根据本公开的第二方面，提供了一种用于估算车辆质量和阻力系数的系统，包括：处理器；以及耦合到该处理器的存储器，该存储器存储有用于执行如本公开的第一方面所描述的方法步骤的指令。
根据本公开的第三方面，提供了一种其上存储有指令的计算机可读介质，该指令在由处理器执行时实现如本公开的第一方面所描述的方法步骤。
与现有技术相比，在本申请中，通过引入蒙特卡洛方法并进行多次采样，从而完成对车辆质量和阻力系数的估算，在保证充分的采样次数的前提下，本公开的技术方案在确保车辆质量和阻力系数的估算准确性的同时，能够在同一时间而非分步骤地对车辆质量和阻力系数进行估算，并且避免了因除法的使用而可能引入的数值计算误差过大或出错，且因为不要求对采样时间点的慎重选择而简化了编程复杂性。
附图说明
在结合以下附图阅读本公开的实施例的详细描述之后，能够更好地理解本公开的上述特征和优点。在附图中，各组件不一定是按比例绘制，并且具有类似的相关特性或特征的组件可能具有相同或相近的附图标记。
图1是解说根据本公开的各方面的用于车辆质量和阻力系数的估算方法的示例流程图。
图2是解说根据本公开的各方面的用于预测车辆驱动力的车辆动力学模型的示意性示图。
图3是解说根据本公开的各方面的用于计算实际车辆驱动力的车辆动力总成模型的示意性示图。
图4是解说根据本公开的各方面的可包括被配置成执行用于本文中所公开的各技术的操作的各种组件的计算机。
除非特别说明，应领会，跨各个附图用相同/相似附图标记所引用的模块/组件一般指代相同的模块/组件。
具体实施方式
以下由特定的具体实施例说明本发明的实施方式，本领域技术人员可由本说明书所揭示的内容轻易地了解本发明的其他优点及功效。虽然本发明的描述将结合优选实施例一起介绍，但这并不代表此发明的特征仅限于该实施方式。恰恰相反，结合实施方式作发明介绍的目的是为了覆盖基于本发明的权利要求而有可能延伸出的其它选择或改造。为了提供对本发明的深度了解，以下描述中将包含许多具体的细节。本发明也可以不使用这些细节实施。此外，为了避免混乱或模糊本发明的重点，有些具体细节将在描述中被省略。
在电控机械式自动变速箱(Automated Mechanical Transmission，下文称之为AMT)和电控制动系统(Electronically Controlled Brake System，下文称之为EBS)系统中使用车辆动力学来估计车辆质量的方法。而在自动驾驶(Autonomous Driving，下文称之为AD)或高级驾驶辅助系统(Advanced Driving Assisted System，下文称之为ADAS)软件系统中普遍使用行驶阻力的估算方法。这两种方法均基于对车辆动力学方程的使用，如下所示。
Figure PCTCN2022141402-appb-000001
其中F是牵引力，m是车辆质量，g是重力加速度，α是坡道角度，f r0和f r1是滚动阻力系数，v是车速，v wind是风速，C w是空气动力学系数，ρ是空气密度，A veh是车辆迎风面积。
假定α较小(cosα≈1)并且假定v wind可忽略不计，则上述式(1)可改写为：
Figure PCTCN2022141402-appb-000002
对于上述式(2)中的各个乘积，ma表示惯性力，mg(f r0+f r1v)是滚动阻力，
Figure PCTCN2022141402-appb-000003
是空气阻力，并且mgsinα是坡道阻力。
上述式(2)还可被进一步改写成：
F＝ma+A+Bv+Cv 2+mgsinα.    (3)
乘积A+Bv+Cv 2表示总行驶阻力，其中A+Bv表示滚动阻力而Cv 2表示空气阻力。表示行驶阻力的这一方法在本领域中通常被称为ABC方法。
在计算车辆质量和阻力系数的现有方法中，假定行驶阻力和坡道阻力在短时间内保持不变，在两个接近的时间点t 1和t 2进行采样将这两个采样时间点的上述式(3)相减，可以得到：
m＝(F 1-F 2)/(a 1-a 2)     (4)
牵引力和加速度可根据实际测得的发动机扭矩、车速等来推导得出。上述式(4)也可以以其他形式(例如，功率或能量形式)来改写。在根据式(4)得到车辆质量之后，将其代入式(3)中，在例如根据AMT中安装的坡道传感器知晓道路坡度的情况下，可以求得驾驶阻力。
然而，在上述计算方法中，车辆质量和阻力系数是分开计算的，并且先计算车辆质量随后再计算阻力系数。此外，当两次采样之间的加速度a 1和a 2相差无几时，往往会因为分子非常小(接近于零)或为零而导致数值计算误差过大或出错。进一步，两次采样的时间点t 1和t 2的选择会显著影响最终计算结果的准确性，这进一步提升了以计算机程序实现时编程的复杂性。
根据本申请的技术方案通过引入蒙特卡洛方法并进行多次采样，从而完成对车辆质量和阻力系数的估算。在保证充分的采样次数的前提下，本公开的技术方案在确保车辆质量和阻力系数的估算准确性的同时，能够在同一时间而非分步骤地对车辆质量和阻力系数进行估算，并且避免了因除法的使用而可能引入的数值计算误差过大或出错，且因为不要求对采样时间点的慎重选择而简化了编程复杂性。
首先参考图1，图1是解说根据本公开的各方面的用于车辆质量和阻力系数的估算方法100的示例流程图。
方法100可包括，在步骤105，对车辆质量和阻力系数的取值范围和初始分布进行初始化。在一些示例中，车辆质量和阻力系数可包括如上文在式(3)中所示出的车辆质量m和阻力系数，诸如常数项A、一次项系数B和二次项系数C。
在一些示例中，车辆质量和阻力系数的取值范围可根据参数的实际情况来确定，例如可根据车辆类型、载重情形、国家标准等中的一者或多者来确定。在49吨重的半挂牵引车的示例中，车辆质量m的取值范围可以是从8吨(仅有车头)到50吨(超载了1吨)。在该示例中，阻力系数中的常数项A、一次项系数B和二次项系数C的取值范围可参照例如标准GB/T 18386-2017来确定，如下表1中所示。应注意，根据经验，表1中的值通常要比实车的数值偏大一些。在一些示例中，阻力系数中的常数项A的取值范围可为1000～3000、阻力系数中的一次项系数B的取值范围可为0～25，并且阻力系数中的二次项系数C的取值范围可为0.2～0.3。
Figure PCTCN2022141402-appb-000004
表1
在一些示例中，车辆质量和阻力系数的初始分布可从现有的概率分布模型中选择。在一些示例中，车辆质量m和阻力系数A、B和C的初始分布可被确定为均匀分布。在49吨重的半挂牵引车的示例中，车辆质量m的初始分布可被确定为在从8吨到50吨的范围内均匀分布。应注意，车辆质量m和阻力系数A、B和C的分布可随着计算的结果和迭代执行而被更新，如将在下文中更详细地描述的。
方法100可包括，在步骤110，基于取值范围和初始分布对车辆质量和阻力系数进行蒙特卡洛采样以得到多组车辆质量和阻力系数采样值。蒙特卡洛采样为本领域技术人员所知晓的采样方法，并且在本文中不再赘述。在一些示例中，步骤110可包括对于每一次采样，根据参数的取值范围和分布进行随机取值。在一些示例中，步骤110可包括基于取值范围和初始分布，对车辆质量m和阻力系数A、B、C进行N次蒙特卡洛采样以得到N组车辆质量和阻力系数采样值(m，A，B，C)。对于采样次数N，初始采样的次数可被确定为较大的数目，诸如100。因为在初始时不知晓真值落在何处，初始采样次数较大可防止遗漏。应注意，随着计算的迭代次数的增加，采样次数可逐渐减小，例如100-＞80-＞60-＞50-＞40-＞…，如将在下文中更详细地描述的。
接着，方法100可包括，在步骤115，根据车辆动力学模型以及多组车辆质量和阻力系数采样值来确定针对每一组车辆质量和阻力系数采样值的预测车辆驱动力。步骤115可结合图1和图2来描述。图2示意性地示出了车辆动力学模型的图示。如图2中所示，车辆动力学模型可以以车速和道路坡度为输入，基于在步骤110中采样得到的N组车辆质量和阻力系数采样值，根据上述式(3)来计算针对每一组车辆质量和阻力系数采样值的N个预测车辆驱动力。在一些示例中，车速和道路坡度可以是已知的。
方法100随后可包括，在步骤120，根据损失函数计算每一组车辆质量和阻力系数采样值的预测车辆驱动力的损失率。步骤120可结合图1和图3来描述。图3示意性地示出了车辆动力总成模型的图示。如图3中所示，车辆动力总成模型可以以实际测得的发动机扭矩为输入，根据车辆动力总成的固定参数来计算实际车辆驱动力。在一些示例中，车辆动力总成的固定参数可包括以下一者或多者或其组合：变速箱类型、换挡策略、各档位速比、主减速器速比、传动效率。在一些示例中，步骤120可包括根据如下所示的损失函数，基于每一组车辆质量和阻力系数采样值的预测车辆驱动力和实际车辆驱动力来计算每一组车辆质量和阻力系数采样值的预测车辆驱动力的损失率以得到N个损失率：
Figure PCTCN2022141402-appb-000005
其中F act是实际车辆驱动力，F cal是每一组车辆质量和阻力系数采样值的预测车辆驱动力，t为每一次采样的持续时间。在一些示例中，根据经验，t的取值范围n可以为0.01秒-120秒。在一些示例中，n可以被优选地确定为20秒。本领域技术人员应当明白，上述n的取值仅仅是一个示例，并且在其他实现中可取决于实际要求而采用其他数值而不背离本申请的范围。损失率的值反映了在持续时间n内每一组车辆质量和阻力系数采样值的预测车辆驱动力与实际车辆驱动力之间的接近程度。换言之，损失率越小，该组车辆质量和阻力系数采样值(m，A，B，C)与真实值接近的可能性越大。在一些示例中，步骤115和步骤120中提及的车辆动力学模型和车辆动力总成模型可由计算机程序实现，并且在此不再赘述。
继续参考图1，方法100可包括，在步骤125，确定计算结果是否收敛。方法100可进一步包括，在步骤130，在计算结果收敛时，取具有最小损失率的一组车辆质量和阻力系数采样值作为估算结果，或者在步骤135，在计算结果不收敛时，根据计算所得的损失率来调整车辆质量和所述阻力系数中的一者或多者的分布以及基于经调整的分布迭代地执行步骤110至步骤125，直到得到估算结果。
在一些示例中，步骤125可包括，当在步骤120计算所得的预测车辆驱动力的损失率低于损失率阈值的组的数目达到收敛阈值时，确定计算结果收敛。在49吨重的半挂牵引车的示例中，假设初始采样次数N为100，则在计算所得的预测车辆驱动力的损失率低于10％的组的数目达到90时(假设收敛阈值设为采样次数N 的90％)，可确定计算结果收敛。在一些示例中，损失率阈值和收敛阈值中的一者或两者可取决于多种因素来确定，包括信号精度、计算采样时间等。在一些示例中，损失率阈值和收敛阈值中的一者或两者可由操作人员在采样过程期间根据采样结果动态地调整。在一些示例中，损失率阈值的范围可在1％-25％之间。在一些示例中，收敛阈值的范围可在采样次数的60％-99％之间。
在一些示例中，损失率阈值和收敛阈值能够由用户设置。本领域技术人员应当明白，上述损失率阈值和收敛阈值的具体数值仅仅是一个示例，并且在其他实现中可取决于实际要求而采用其他数值而不背离本申请的范围。
在一些示例中，步骤130可包括，当在步骤125确定计算结果收敛时，例如在计算所得的预测车辆驱动力的损失率低于10％的组的数目达到90时，取其中具有最低损失率的一组车辆质量和阻力系数的采样值作为估算结果。
在一些示例中，步骤135可包括，当在步骤125确定计算结果不收敛时，例如在计算所得的预测车辆驱动力的损失率低于10％的组的数目小于90时，将根据计算所得的N个损失率，将车辆质量的分布从在8吨到50吨之间均匀分布的初始分布调整为在15到45吨之间均匀分布的调整后分布，并基于调整后的分布执行步骤110-步骤125，直到在计算结果收敛时得到估算结果。在一些示例中，在步骤135中，在基于调整后的分布执行步骤110-步骤125时，采样次数N可随着每一次迭代被减小。在一些示例中，在基于15到45吨之间均匀分布的车辆质量执行步骤110-步骤125时，采样次数N可从100被减小为80。如上文所提及的，随着计算的迭代次数的增加，采样次数可逐渐减小，例如100-＞80-＞60-＞50-＞40-＞…。本领域技术人员应当明白，上述采样次数的具体数值和减小幅度仅仅是一个示例，并且在其他实现中可取决于实际要求而采用其他数值而不背离本申请的范围。在一些示例中，步骤115中涉及的车辆动力总成的固定参数、步骤120中涉及的采样持续时间、步骤125中涉及的损失率阈值和收敛阈值等参数可在步骤105中的初始化期间确定。
接着参考图4，图4是适于用来实现本公开的一些实施例中的计算机的示例性计算机系统012的框图。图4显示的计算机系统012仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图4所示，计算机系统012以通用计算设备的形式表现。计算机系统012的 组件可以包括但不限于：一个或者多个处理器或者处理单元016，系统存储器028，连接不同系统组件(包括系统存储器028和处理单元016)的总线018。
总线018表示几类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(ISA)总线，微通道体系结构(MAC)总线，增强型ISA总线、视频电子标准协会(VESA)局域总线以及外围组件互连(PCI)总线。
计算机系统012典型地包括多种计算机系统可读介质。这些介质可以是任何能够被计算机系统012访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
系统存储器028可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(RAM)030和/或高速缓存存储器032。计算机系统012可以进一步包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统034可以用于读写不可移动的、非易失性磁介质(图4未显示，通常称为“硬盘驱动器”)。尽管图4中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如CD-ROM、DVD-ROM或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线018相连。存储器028可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本公开各实施例的功能。
具有一组(至少一个)程序模块042的程序/实用工具040，可以存储在例如存储器028中，这样的程序模块042包括——但不限于——操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块042通常执行本公开所描述的实施例中的功能和/或方法。
计算机系统012也可以与一个或多个外部设备014(例如键盘、指向设备、显示器024等)通信，在本公开中，计算机系统012与外部雷达设备进行通信，还可与一个或者多个使得用户能与该计算机系统012交互的设备通信，和/或与使得该计算机系统012能与一个或多个其它计算设备进行通信的任何设备(例如网卡，调 制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口022进行。并且，计算机系统012还可以通过网络适配器020与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器020通过总线018与计算机系统012的其它模块通信。应当明白，尽管图4中未示出，可以结合计算机系统012使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
处理单元016通过运行存储在系统存储器028中的程序，从而执行各种功能应用以及数据处理，例如实现本工农开实施例所提供的方法流程。
根据本公开的又一方面，提供了一种非瞬态计算机存储介质的实施例，其上存储有计算机可执行指令，所述计算机可执行指令在由计算机执行时致使所述计算机执行如上文所描述的车辆质量和阻力系数的估算方法中的任一方面的操作。
计算机程序可以设置于计算机存储介质中，即该计算机存储介质被编码有计算机程序，该程序在被一个或多个计算机执行时，使得一个或多个计算机执行本公开上述实施例中所示的方法流程和/或装置操作。例如，被上述一个或多个处理器执行本公开实施例所提供的方法流程。
随着时间、技术的发展，介质含义越来越广泛，计算机程序的传播途径不再受限于有形介质，还可以直接从网络下载等。可以采用一个或多个计算机可读的介质的任意组合。
计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信 号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括——但不限于——电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括——但不限于——无线、电线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言-诸如Java、Smalltalk、C++，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机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
本领域技术人员可以理解，结合本文中所公开的实施例来描述的各种解说性逻辑板块、模块、电路、和算法步骤可实现为电子硬件、计算机软件、或这两者的组合。为清楚地解说硬件与软件的这一可互换性，各种解说性组件、框、模块、电路、和步骤在上面是以其功能性的形式作一般化描述的。此类功能性是被实现为硬件还是软件取决于具体应用和施加于整体系统的设计约束。技术人员对于每种特定应用可用不同的方式来实现所描述的功能性，但这样的实现决策不应被解读成导致脱离了本公开的范围。
结合本文所公开的实施例描述的各种解说性逻辑模块、和电路可用通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现场可编程门阵列(FPGA)或其它可编程逻辑器件、分立的门或晶体管逻辑、分立的硬件组件、或其设计成执行本文所描述功能的任何组合来实现或执行。通用处理器可以是微处理器，但在替换方案中，该处理器可以是任何常规的处理器、控制器、微控制器、或状态机。处理器还可以被实现为计算设备的组合，例如DSP与微处理器的组合、多个微处理器、与DSP核心协作的一个或多个微处理器、或任何其他此类配置。
结合本文中公开的实施例描述的方法或算法的步骤可直接在硬件中、在由处理器执行的软件模块中、或在这两者的组合中体现。软件模块可驻留在RAM存储器、闪存、ROM存储器、EPROM存储器、EEPROM存储器、寄存器、硬盘、可移动盘、CD-ROM、或本领域中所知的任何其他形式的存储介质中。示例性存储介质耦合到处理器以使得该处理器能从/向该存储介质读取和写入信息。在替换方案中，存储介质可以被整合到处理器。处理器和存储介质可驻留在ASIC中。ASIC可驻留在用户终端中。在替换方案中，处理器和存储介质可作为分立组件驻留在用户终端中。
提供对本公开的先前描述是为使得本领域任何技术人员皆能够制作或使用本公开。对本公开的各种修改对本领域技术人员来说都将是显而易见的，且本文中所定义的普适原理可被应用到其他变体而不会脱离本公开的精神或范围。由此，本公开并非旨在被限定于本文中所描述的示例和设计，而是应被授予与本文中所公开的原理和新颖性特征相一致的最广范围。
最后应说明的是：以上各实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述各实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围，其均应涵盖在本申请的权利要求和说明书的范围当中。尤其是，只要不存在结构冲突，各个实施例中所提到的各项技术特征均可以任意方式组合起来。本申请并不局限于文中公开的特定实施例，而是包括落入权利要求的范围内的所有技术方案。

Claims (10)

  1. 一种用于估算车辆质量和阻力系数的方法，包括：
    步骤一，对车辆质量和阻力系数的取值范围和初始分布进行初始化；
    步骤二，基于所述取值范围和所述初始分布对车辆质量和阻力系数进行蒙特卡洛采样以得到多组车辆质量和阻力系数采样值；
    步骤三，根据车辆动力学模型以及所述多组车辆质量和阻力系数采样值来确定针对每一组车辆质量和阻力系数采样值的预测车辆驱动力；
    步骤四，根据损失函数计算每一组车辆质量和阻力系数采样值的预测车辆驱动力的损失率；以及
    步骤五一，在确定计算结果收敛时取具有最小损失率的一组车辆质量和阻力系数采样值作为估算结果。
  2. 如权利要求1所述的方法，其特征在于，所述方法进一步包括：
    步骤五二，在确定计算结果不收敛时，根据计算所得的损失率来调整所述车辆质量和所述阻力系数中的一者或多者的分布；
    基于经调整的分布迭代地执行步骤二至步骤四，直到得到估算结果。
  3. 如权利要求1所述的方法，其特征在于，所述阻力系数包括以下车辆动力学方程中的阻力系数的常数项A、一次项系数B和二次项系数C：
    F＝ma+A+Bv+Cv 2+mgsinα
    其中F为牵引力，m为车辆质量，a为车辆加速度，v为车速，g为重力加速度，并且α为坡道角度。
  4. 如权利要求1所述的方法，其特征在于，对车辆质量和阻力系数的取值范围和初始分布进行初始化进一步包括：根据车辆类型、载重情形、国家标准等中的一者或多者来确定车辆质量和阻力系数的取值范围和初始分布。
  5. 如权利要求1所述的方法，其特征在于，所述损失函数包括：
    Figure PCTCN2022141402-appb-100001
    其中F act是实际车辆驱动力，F cal是每一组车辆质量和阻力系数采样值的预测车辆驱动力，t为每一次采样的持续时间。
  6. 如权利要求5所述的方法，其特征在于，所述实际车辆驱动力根据测得的实际发动机扭矩和车辆动力总成的固定参数来计算，其中所述车辆动力总成的固定参数包括以下一者或多者：变速箱类型、换挡策略、各档位速比、主减速器速比、传动效率。
  7. 如权利要求1所述的方法，其特征在于，所述步骤五一进一步包括在所述预测车辆驱动力的损失率低于损失率阈值的组的数目达到收敛阈值时，确定计算结果收敛，其中所述损失率阈值和所述收敛阈值能够由用户设置。
  8. 如权利要求2所述的方法，其特征在于，所述多组车辆质量和阻力系数采样值的数目随着每一次迭代的执行而被减少。
  9. 一种用于估算车辆质量和阻力系数的系统，包括：
    处理器；以及
    耦合到所述处理器的存储器，所述存储器存储有用于执行如权利要求1-8中任一项所述的方法的指令。
  10. 一种其上存储有指令的计算机可读介质，所述指令在由处理器执行时实现如权利要求1-8中任一项所述的方法的操作。
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