JP7351012B2 - Vehicle weight estimation method, device, electronic device, storage medium and computer program - Google Patents

Description

cross reference

This application claims priority to the Chinese patent application filed on June 8, 2021, application number 202110650457.7, the entire contents of which are incorporated by reference into this application.

The present disclosure relates to the field of computer technology, specifically to the field of automatic driving, and particularly to a method , device , device, electronic device, storage medium, and computer program for estimating vehicle weight.

With the development of self-driving vehicle technology, the requirements for the effectiveness of automatic vehicle control are gradually increasing, and the weight change range of large vehicles such as self-driving buses and self-driving trucks is larger than that of small vehicles. Weight changes from empty to fully loaded can reach 300%. Vehicle weight is a key parameter for automatic driving software to control vehicle dynamics, determine parking position, park and stop, and monitor vehicle operation status. Vehicle weight can be used to rationally adjust automatic driving control software and monitoring software. This will further improve the safety, comfort, and power of vehicles.

Measuring the weight of a vehicle using a hardware sensor is expensive and has problems with its service life. Using software algorithms, the weight of the vehicle can be directly estimated, which is extremely economical and convenient. Techniques for estimating vehicle weight using software algorithms require providing vehicle wheel edge torque parameters to estimate vehicle weight. If the system cannot provide a wheel edge torque signal, the weight estimation software cannot operate.

The present disclosure provides a vehicle weight estimation method, device, electronic device , storage medium , and computer program .

According to a first aspect, there is provided a method for estimating the weight of a vehicle using a speed-command wheel edge torque mapping relationship according to a current speed of the vehicle and a control command for the vehicle. A method for estimating vehicle weight includes: obtaining an edge torque value; and using the obtained edge torque value to estimate a weight of the vehicle based on a vehicle longitudinal dynamics equation. ing.

According to a second aspect, there is provided a device for estimating the weight of a vehicle using a speed-command-wheel edge torque mapping relationship according to the current speed of the vehicle and a control command for the vehicle. a wheel edge torque value acquisition module arranged to obtain wheel edge torque values, and using the acquired wheel edge torque values to estimate a weight of the vehicle based on vehicle longitudinal dynamics equations; A vehicle weight estimating device is provided, including a weight estimating module disposed therein.

According to a third aspect, an electronic device includes at least one processor and a memory communicatively connected to the at least one processor, the memory storing commands that can be executed by the at least one processor. The present invention provides an electronic device in which the command is executed by the at least one processor so that the at least one processor can execute the method.

According to a fourth aspect, there is provided a non-transitory computer readable storage medium storing computer commands for causing a computer to perform the above method.

According to a fifth aspect, there is provided a computer program that , when executed by a processor, implements the method described above.

The vehicle weight estimation method and device according to the present disclosure can more efficiently and accurately estimate vehicle weight.

It should be understood that the content described in this section is not intended to represent key points or important features of the embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other features of the disclosure will be readily understood from the following description.

Here, the drawings are used to better understand the technical solution and are not intended to limit the disclosure.
FIG. 1 shows a flowchart of a method for estimating vehicle weight according to an embodiment of the present disclosure. FIG. 2 shows a schematic diagram of a speed-command-rim torque calibration table according to an embodiment of the present disclosure. FIG. 3 shows a schematic diagram for determining the control command and vehicle acceleration to be adopted, taking into account the control command delay and vehicle acceleration filtering delay according to an embodiment of the present disclosure. FIG. 4 shows a flowchart of a method for obtaining wheel edge torque values for a vehicle using a speed-command-wheel edge torque mapping relationship. FIG. 5 shows a schematic diagram of obtaining a wheel edge torque value by performing linear interpolation in a speed-command-wheel edge torque calibration table. FIG. 6 shows a flowchart of a method for estimating the weight of a vehicle based on vehicle longitudinal dynamics equations according to an embodiment of the present disclosure. FIG. 7 shows a flowchart of a method for estimating road slope angle based on an Extended Kalman Filter (EKF) according to an embodiment of the present disclosure. FIG. 8 shows a block diagram of an apparatus for estimating vehicle weight according to an embodiment of the present disclosure. FIG. 9 depicts a block diagram of exemplary electronic equipment used to implement embodiments of the present disclosure.

Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the drawings. Various details of the embodiments of the present disclosure are included herein to provide a better understanding and are to be considered exemplary. Accordingly, it should be understood by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and configurations are omitted in the following description.

In the field of automated driving, vehicle weight estimation is performed by specifying the vehicle weight based on the input/output time function of the automated driving vehicle system. Vehicle weight estimation requires creating a mathematical model of the entire vehicle, where the weight of the entire vehicle is an important parameter in this mathematical model. Based on the input/output data of the system based on this mathematical model, the vehicle weight is estimated using a specific estimation algorithm. If the system cannot provide a wheel edge torque signal, the weight estimation algorithm cannot operate. In autonomous driving software, the wheel edge torque signal is the core signal of the vehicle chassis, and most autonomous vehicle chassis providers use the wheel edge torque They tend not to provide a signal.

FIG. 1 shows a flowchart of a method 100 for estimating vehicle weight according to an embodiment of the present disclosure.

In step S110, a wheel edge torque value of the vehicle is obtained using a speed-command-wheel edge torque mapping relationship according to the current speed of the vehicle and a control command for the vehicle.

In some embodiments, the speed-command-rim torque mapping relationship may include a speed-command-rim torque calibration table. In some embodiments, the speed-command-rim torque calibration table may be predetermined based on previously collected vehicle control commands and vehicle sensing data corresponding to the vehicle control commands. For example, vehicle sensing data including vehicle speed may be collected by vehicle sensors in a flat standard field. A flat standard field may include a field where the ground slope is less than 0.1 degree and the maximum length over which the field can be linearly accelerated is greater than 100 m. In some embodiments, after vehicle sensing data is collected, data processing techniques may be used off-line to create a speed-command-rim torque calibration table.

と、慣性モーメントＪと、角加速度

と、道路勾配角βとの少なくとも１つを含んでもよい。 In step S120, the acquired wheel edge torque value is used to calculate the weight of the vehicle based on a vehicle longitudinal dynamic equation. In some embodiments, vehicle longitudinal dynamics equations may be created based on vehicle driving condition data, and the vehicle driving condition data includes vehicle speed v and vehicle acceleration.

, moment of inertia J, and angular acceleration

and a road slope angle β.

According to the embodiments of the present disclosure, vehicle weight can be estimated more efficiently and more accurately. Additionally, according to embodiments of the present disclosure, vehicle weight can be estimated when there is no wheel edge torque feedback signal.

In some embodiments, the vehicle longitudinal dynamics equations are:

It may be.

は車両速度の導関数、即ち、車両加速度であり、単位がｍ／ｓ^２であり、
ｖは車両速度であり、単位がｍ／ｓであり、
Ｊは慣性モーメントであり、単位がｋｇ・ｍ^２であり、

は車両のヨーレートの導関数、即ち、車両角加速度であり、単位がｒａｄ／ｓ ^２であり、
Ｔｗｈｅｅｌは輪縁トルクであり、単位がＮ・ｍであり、
ｒは車両の車輪ロール半径であり、単位がｍであり、

即ち、等価風抵抗係数であり、ただし、ρは空気抵抗係数であり、Ａは車両実効風上面積、ＣＤは風抵抗係数であり、
βは道路勾配角であり、単位がｒａｄであり、
μは転がり抵抗係数であり、
ｇは重力加速度であり、単位がｍ／ｓ^２である。 However, m represents the vehicle weight, and the unit is kg,

is the derivative of the vehicle speed, i.e. the vehicle acceleration, in m/s ² ;
v is the vehicle speed, the unit is m/s,
J is the moment of inertia, the unit is kg・^m2 ,

is the derivative of ^{the vehicle yaw rate, i.e. the vehicle angular acceleration, in units of rad/s2} ,
Twheel is the wheel edge torque, the unit is N・m,
r is the wheel roll radius of the vehicle, the unit is m,

That is, it is the equivalent wind resistance coefficient, where ρ is the air resistance coefficient, A is the vehicle effective windward area, CD is the wind resistance coefficient,
β is the road slope angle, the unit is rad,
μ is the rolling resistance coefficient,
g is the gravitational acceleration, and its unit is m/ ^s2 .

相応的に、上記式（１）は、 In some embodiments, steering wheel determination conditions may be used when performing data processing for the vehicle longitudinal dynamics equation;

Correspondingly, the above formula (1) is

It may be further simplified as

In equation (2), β _μ represents the ground frictional resistance coefficient. Other than that, other parameters in equation (2) represent the same physical meanings as the same parameters in equation (1).

FIG. 2 shows a schematic diagram of a speed-command-rim torque calibration table according to an embodiment of the present disclosure.

As shown in FIG. 2, the x-axis shows vehicle speed, with units of m/s. Vehicle speed may be acquired by vehicle sensors. The y-axis shows commands for controlling the vehicle (ie, control commands). For example, the control command may include a pedal command obtained by a user depressing a vehicle accelerator pedal. In this case, the control command is expressed as a percent opening of the vehicle accelerator pedal, and is in %. The z-axis indicates the wheel edge torque value, and the unit is N·m.

In some embodiments, the commands may be divided into 10 to 20 equal parts based on the maximum-minimum interval range to obtain 10 to 20 equally spaced commands. By sequentially controlling the vehicle to accelerate from standstill to maximum speed or decelerate from maximum speed to standstill in response to these commands, a mapping relationship between speed, commands, and wheel edge torque is established. , that is, a mapping relationship of speed-command-rim torque is obtained. If the command is expressed as a percentage of vehicle accelerator pedal opening, then the speed-command-wheel-edge torque mapping relationship is based on a speed-command-wheel-edge torque calibration table as shown in Figure 2, i.e., speed-pedal opening. It may also be shown as a degree-to-rim torque calibration table.

According to embodiments of the present disclosure, sensing data collection and speed-command-rim torque calibration table creation may be performed offline, while vehicle weight estimation may be performed online. When performing vehicle weight estimation in a real-time online manner, there is a control command delay and a vehicle acceleration filtering delay, and both delays may not match. Since the vehicle acceleration determined by the driver and this control command do not match, it is necessary to determine the control command and vehicle acceleration to be adopted in consideration of the control command delay and the vehicle acceleration filtering delay.

FIG. 3 shows a schematic diagram for determining the control command and vehicle acceleration to be adopted in consideration of the control command delay and vehicle acceleration filtering delay according to an embodiment of the present disclosure.

FIG. 3 shows two cache queues: a command (Cmd) cache queue Quene1 and an acceleration (Acc) cache queue Quene2. The lengths of the command cache queue Quene1 and the acceleration cache queue Quene2 are L1 and L2, respectively. L1 and L2 are calculated based on the control command delay and vehicle acceleration filtering delay, respectively, i.e.

Cache queues Quene1 and Quene2 cache data on a first-in, first-out basis. In FIG. 3, the rightmost data in cache queues Quene1 and Quene2 is the latest data. By directly using the latest data, there is a possibility that a control command with a delay and filtered acceleration data with a delay do not match, so according to an embodiment of the present disclosure, the command cache queue Quene1 The obtained L1-th control command and the L2-th filtered acceleration data match each other using the acceleration cache queue Quene2 and the acceleration cache queue Quene2. Therefore, for the L1-th control command, the L2-th filtered acceleration data is employed as input data for estimating the vehicle weight, and more accurate results can be obtained.

According to some embodiments, the validity of the collected data may be determined. For example, the validity of the collected data may be determined based on the condition that actual steering wheel steering angle<maximum steering wheel steering angle*3%. If the actual steering angle satisfies the above conditions, the vehicle acceleration obtained by the above cache method, the vehicle speed obtained by the sensor, and the road slope angle measurement obtained by the sensor are sent to the corresponding control command. Use the value (if it exists) as valid data.

FIG. 4 shows a flowchart of a method for obtaining wheel edge torque values for a vehicle using a speed-command-wheel edge torque mapping relationship.

In step S411, in accordance with the current speed of the vehicle and the control command, a calibration section to which the current speed belongs and a calibration section to which the control command belongs are specified in the mapping relationship of speed-command-wheel edge torque. In some embodiments, the speed-command-rim torque mapping relationship may include a speed-command-rim torque calibration table. In some embodiments, the speed-command-rim torque calibration table may be represented as a speed-pedal opening-rim torque calibration table.

In step S412, a plurality of wheel edges corresponding to the specified calibration zone are determined based on the calibration zone to which the current speed belongs and the calibration zone to which the control command belongs, according to the speed-command-wheel edge torque mapping relationship. Obtain each torque value.

In step S413, a wheel edge torque value corresponding to the current vehicle speed and control command is calculated based on the plurality of wheel edge torque values.

According to embodiments of the present disclosure, a method of obtaining a wheel edge torque value of a vehicle using a speed-command-wheel edge torque mapping relationship eliminates a hardware sensor for measuring vehicle weight, and provides an accurate A wheel edge torque value can be provided.

In some embodiments, the wheel edge torque value may be obtained by linear interpolation in a speed-command-wheel edge torque calibration table depending on the vehicle's current speed and the control command.

FIG. 5 shows a schematic diagram of obtaining a wheel edge torque value by performing linear interpolation in the speed-command-wheel edge torque calibration table.

In FIG. 5, v represents the current speed of the vehicle, Cmd represents the control command, and T _wheel represents the wheel edge torque value corresponding to the current speed v and the control command Cmd. As shown in FIG. 5, in the speed-command-wheel edge torque calibration table, the calibration section [v _{t -1} , v _t ], [Cmd _t-1 , Cmd _t ], where v _t and v _t-1 are the speeds in the calibration interval to which the current speed belongs, and Cmd _t and Cmd _t-1 are These are control commands in the calibration interval to which each control command belongs.

As shown in FIG. 5, in the velocity-command-rim torque calibration table, four points around the point denoted as {v, Cmd} are (v _t-1 , Cmd _t-1 ), (v _t-1 , Cmd _t ), (v _t , Cmd _t-1 ), and (v _t , Cmd _t ), respectively. Then, the calibration wheel edge torques corresponding to (v _t-1 , Cmd _t-1 ), (v _t-1 , Cmd _t ), (v _t , Cmd _t-1 ), and (v _t , Cmd _t ), respectively Find the values T ₁ , T ₂ , T ₃ , T ₄ . Thereafter, the wheel edge torque value T _wheel to be calculated is obtained using the following formula.

According to embodiments of the present disclosure, a hardware sensor for measuring vehicle weight can be omitted and accurate wheel edge torque values can be provided.

FIG. 6 depicts a flowchart of a method for estimating vehicle weight based on vehicle longitudinal dynamics equations according to an embodiment of the present disclosure.

In step S621, a least squares recursive equation (RLS) having a forgetting factor is created for the vehicle longitudinal dynamics equation. Here, the vehicle longitudinal dynamic equation may be the equation shown in the above equation (1) or (2).

In step S622, an iterative calculation is performed using a least squares recursive equation (RLS) with a forgetting factor to obtain the weight of the vehicle.

In some embodiments, the least squares recursive equation (RLS) with a forgetting factor is

It may be shown as

（ｍは車両重量であり、例えば、車種、ブランドなどに応じてｍの初期値を設定してもよい）、

はＲＬＳアルゴリズムにおける推定すべき変数であり、ｋはｋ回目の反復計算を表し、
ｙ（ｋ）はＲＬＳアルゴリズムが観測しようとする量であり、ここでは、ｋ回目で観測された車両加速度

を表し、

の転置であり、ただし、Ｔ_{ｗｈｅｅｌ}は車両の輪縁トルクであり、ｒは車両の車輪ロール半径であり、ｖは車両速度であり、

即ち等価風抵抗係数、ただし、ρは空気抵抗係数であり、Ａは車両実効風上面積であり、Ｃ_Ｄは風抵抗係数であり、
Ｌ（ｋ）は毎回の反復計算のゲインを表し、
Ｐ（ｋ）はＲＬＳ計算の中間変数を表し、且つ、
λは忘却係数であり、且つ０＜λ＜１。幾つかの実施例において，λが０.９７に設定される。 however,

(m is the vehicle weight; for example, the initial value of m may be set depending on the vehicle type, brand, etc.)

is the variable to be estimated in the RLS algorithm, k represents the k-th iteration,
y(k) is the quantity that the RLS algorithm attempts to observe, here the kth observed vehicle acceleration

represents,

where T _wheel is the vehicle wheel edge torque, r is the vehicle wheel roll radius, v is the vehicle speed,

That is, the equivalent wind resistance coefficient, where ρ is the air resistance coefficient, A is the vehicle effective windward area, _CD is the wind resistance coefficient,
L(k) represents the gain of each iteration,
P(k) represents an intermediate variable of RLS calculation, and
λ is the forgetting factor, and 0<λ<1. In some embodiments, λ is set to 0.97.

According to the embodiment of the present disclosure, the weight of the vehicle can be estimated more accurately by using the RLS algorithm.

In addition, as shown in the vehicle longitudinal dynamics equation explained in the preamble, the road slope angle β is a key parameter for estimating the vehicle weight, and it is highly consistent with the vehicle weight, and the slope angle parameter error should reach 20%. For example, the weight estimation result error will reach 50%. In some embodiments, the road slope angle β may be obtained by a vehicle sensor. In other embodiments, the road slope angle β may be estimated based on an Extended Kalman Filter (EKF).

Hereinafter, a method for estimating the road slope angle β based on the extended Kalman filter (EKF) will be described in detail.

FIG. 7 shows a flowchart of a method 700 for estimating a road slope angle based on an Extended Kalman Filter (EKF) according to an embodiment of the present disclosure.

In step S710, the road slope angle is estimated based on the EKF system state equation and the EKF system measurement equation. In some embodiments, the system state equation for estimating road slope angle is

は道路勾配角β導関数の導関数を表す。それ以外、式（１１）における他のパラメータは、式（１）における同一パラメータと同じ物理的意味を表す。 It is. however,

represents the derivative of the road slope angle β derivative. Other than that, other parameters in equation (11) represent the same physical meanings as the same parameters in equation (1).

The EKF system noise vector and measurement noise vector are W and V, respectively, and W and V may be white Gaussian noises that are independent of each other and have an average value of zero, and the resulting EKF system The equation of state is

It is.

In addition, the obtained EKF system measurement equation is

It is.

In the above equation (12), v(k) and v(k-1) are the vehicle speeds calculated by the k-th and k-1st iteration calculations, respectively, and Δt is the vehicle speed when the EKF is actually used. Represents the period of iterative calculation, β(k), β(k-1), β(k-2) and β(k-3) are the kth, k-1st, k-2nd, k-3th, respectively. This is the road slope angle calculated by the second iterative calculation. In some embodiments, if there is a road slope angle acquired by the vehicle sensor, the initial values of β(k), β(k-1), β(k-2), and β(k-3) are Set to the road slope angle acquired by the vehicle sensor. If there is no road slope angle acquired by the vehicle sensor, the initial values of β(k), β(k-1), β(k-2), and β(k-3) are set to 0. Other than that, other parameters in equation (12) represent the same physical meanings as the same parameters in equation (1).

センサによって取得された道路勾配角が存在しない場合、

In the above equation (13), z(k) represents the vehicle speed that the EKF attempts to measure, H is the measurement matrix, and if there is a road gradient angle acquired by the sensor,

If there is no road slope angle acquired by the sensor,

According to one embodiment, the method 700 for estimating a road slope angle using an Extended Kalman Filter (EKF) may further include step S720.

In step S720, when performing iterative calculations using the EKF, the EKF is updated using the time update equation and measurement update equation of the EKF. Specifically, by combining equations (12) and (13), the EKF state space formula:

get.

はプロセス状態非線性関数であり、

は式（１２）における数式 however,

is the process state nonlinear function,

is the mathematical expression in equation (12)

It is.

を線性化する必要があるため、更新する度にヤコビ（Ｊａｃｏｂｉａｎ）行列Ｆ（ｋ）： When EKF performs repeated calculations,

Since it is necessary to linearize the Jacobian matrix F(k):

It is necessary to calculate

Let the system noise covariance matrix of EKF be Q, and the obtained EKF time update equation is:

It is.

Let the EKF measurement noise covariance matrix be R, and the obtained EKF measurement update equation is:

It is.

はＥＫＦのシステム状態を表す。また、ＥＫＦに対して初期パラメータ設定を行う時、Ｐ（０）を１０に設定し、Ｒ行列を

に設定し、且つ実際のセンシングデータノイズ特性に応じてＱ行列を設定する。 however,

represents the EKF system status. Also, when setting initial parameters for EKF, P(0) is set to 10 and R matrix is

and set the Q matrix according to the actual sensing data noise characteristics.

According to the embodiments of the present disclosure, by estimating the road slope angle based on the EKF, it is possible to reduce the hardware sensor cost for measuring the road slope angle and provide an accurate road slope angle. .

According to embodiments of the present disclosure, sensing data collection and speed-command-wheel edge torque calibration table creation may be performed offline, while vehicle weight estimation and road slope angle estimation may be performed online. It's okay. Based on the speed-command-rim torque calibration table, wheel torque information can be provided for vehicle weight estimation and road slope angle estimation. Furthermore, within each vehicle operation cycle, vehicle weight estimation and road slope angle estimation are both independently and iteratively calculated in parallel. Within each vehicle operation cycle, the weight value for vehicle weight estimation is used as an internal parameter for the next calculation cycle for road slope angle estimation, and similarly, the road slope angle for road slope angle estimation is used as the internal parameter for the next calculation cycle for vehicle weight estimation. Take it as a parameter.

According to the embodiments of the present disclosure, accurate vehicle weight and road slope angle information can be provided. In large, heavy-duty vehicle energy optimization applications, accurate weight and slope angle information can help the whole vehicle controller allocate energy rationally, reduce energy consumption, and greatly improve the cruising range of autonomous vehicles. can be done. Furthermore, according to the embodiments of the present disclosure, it is possible to replace a vehicle weight sensor with equivalent accuracy and significantly reduce hardware costs.

FIG. 8 shows a block diagram of an apparatus 800 for estimating vehicle weight according to an embodiment of the present disclosure.

As shown in FIG. 8, an apparatus 800 for estimating vehicle weight includes a wheel edge torque value acquisition module 810 and a weight estimation module 820.

The wheel edge torque value acquisition module 810 is arranged to obtain wheel edge torque values of the vehicle using a speed-command-wheel edge torque mapping relationship in response to a current speed of the vehicle and a control command for the vehicle. There is. In some embodiments, the speed-command-rim torque mapping relationship may include a speed-command-rim torque calibration table. In some embodiments, the speed-command-rim torque calibration table may be predetermined based on previously collected vehicle control commands and vehicle sensing data corresponding to the vehicle control commands.

、慣性モーメントＪ、角加速度

及び道路勾配角βのうちの少なくとも１つを含んでもよい。 Weight estimation module 820 is arranged to use the obtained wheel edge torque values to estimate the weight of the vehicle based on vehicle longitudinal dynamics equations. In some embodiments, vehicle longitudinal dynamics equations may be created based on vehicle driving condition data, and the vehicle driving condition data includes vehicle speed v, vehicle acceleration

, moment of inertia J, angular acceleration

and a road slope angle β.

In some examples, the wheel edge torque value acquisition module 810 may include a first sub-module, a second sub-module, and a third sub-module. The first sub-module identifies a calibration interval to which the current speed belongs and a calibration interval to which the control command belongs in the mapping relationship of speed-command-wheel-edge torque, depending on the current speed of the vehicle and the control command. The second sub-module corresponds to the identified calibration interval according to the calibration interval to which the current speed belongs and the calibration interval to which the control command belongs, based on the speed-command-wheel edge torque mapping relationship. A plurality of wheel edge torque values are each obtained. A third sub-module calculates a wheel edge torque value corresponding to the vehicle current speed and control command from the plurality of wheel edge torque values.

In some examples, weight estimation module 820 may include a fourth sub-module and a fifth sub-module. The fourth submodule creates least squares recursive equations with forgetting factors for the vehicle longitudinal dynamics equations. The fifth sub-module performs iterative calculations using a least squares recursive equation with a forgetting factor to obtain the weight of the vehicle.

According to embodiments of the disclosure, the disclosure further provides an electronic device, a readable storage medium, and a computer program product .

FIG. 9 depicts a schematic block diagram of an exemplary electronic device 900 in which embodiments of the present disclosure may be implemented. Electronic equipment is intended to refer to various types of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, large computers and other suitable computers. Electronic devices may also refer to various types of mobile devices, such as personal digital assistants, mobile phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are exemplary only and do not limit implementation of the present disclosure as described and/or required herein.

As shown in FIG. 9, the device 900 includes a calculation means 901 that is loaded into a random access memory (RAM) 903 from a computer program stored in a read only memory (ROM) 902 or from a storage means 908. Various suitable operations and processes may be performed based on the computer program. The RAM 903 may further store various programs and data necessary for operating the device 900. Calculation means 901, ROM 902, and RAM 903 are interconnected via bus 904. An input/output (I/O) interface 905 is also connected to bus 904.

A plurality of components in the device 900 are connected to an I/O interface 905, and include input means 906 such as a keyboard and mouse, output means 907 such as various types of displays and speakers, and output means 907 such as a magnetic disk, an optical disk, etc. It includes storage means 908 and communication means 909, such as a network card, modem, wireless communication transceiver, etc. The communication means 909 enables the device 900 to exchange information and data with other devices via a computer network such as the Internet and/or various electrical networks.

The calculation means 901 may be various general-purpose and/or dedicated processing modules having processing and computing capabilities. Some examples of the calculation means 901 include a central processing unit (CPU), a GPU (Graphics Processing Unit), various dedicated artificial intelligence (AI) calculation chips, a calculation unit that runs various machine learning model algorithms, and a DSP (Digital Signal). and any suitable processor, controller, microcontroller, etc. The calculation means 901 executes the methods and processes described in the preamble, for example the method for estimating vehicle weight. For example, in some embodiments, the method for estimating vehicle weight may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage means 908. In some embodiments, part or all of the computer program may be loaded and/or installed on the device 900 via the ROM 902 and/or the communication means 909. When the computer program is loaded into the RAM 903 and executed by the calculation means 901, it may carry out one or more steps of the method for estimating vehicle weight as described in the preamble. Alternatively, in other embodiments, the calculation means 901 may be configured to perform the method for estimating vehicle weight in any other suitable manner (e.g. via firmware).

Various embodiments of the systems and techniques described herein above include digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products. (ASSP), system on a chip (SOC), complex programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments are implemented in one or more computer programs that are executed and/or interpreted on a programmable system that includes at least one programmable processor. The programmable processor may be a special purpose or general purpose programmable processor and receives data and instructions from a storage system, at least one input device, and at least one output device. , and capable of transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

Program code for implementing the methods of this disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program codes may be provided in a flowchart and/or block diagram. The functions and operations specified in the above shall be carried out. The program code may be executed entirely on the device, partially on the device, partially on the device as a separate software package, and partially on a remote device, or It may be performed entirely on a remote device or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium and includes a program for use in or in combination with an instruction-execution system, device, or electronic device. It may also be stored. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or electronics, or any suitable combination of the above. More specific examples of machine-readable storage media include electrical connection through one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optics, compact disk read-only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the above.

A computer may implement the systems and techniques described herein to provide user interaction, and the computer may include a display device (e.g., a CRT (cathode ray tube) or a liquid crystal display (LCD) monitor), a keyboard and a pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other types of devices may further provide interaction with the user, for example, the feedback provided to the user may be any form of sensing feedback (e.g., visual feedback, auditory feedback, or haptic feedback). Input from the user may be received in any form, including voice input, speech input, or tactile input.

The systems and techniques described herein may be used in a computing system that includes background components (e.g., a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components. a system (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with embodiments of the systems and techniques described herein); The present invention may be implemented in a computing system that includes any combination of background components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks illustratively include local area networks (LANs), wide area networks (WANs), and the Internet.

A computer system may include a client and a server. Clients and servers are generally remote and typically interact via a communications network. The relationship between client and server is created by a computer program running on the relevant computer and having a client-server relationship. The server may be a cloud server, a distributed system server, or a blockchain combined server.

It should be understood that various types of flows illustrated above may be used and operations may be re-sorted, added, or deleted. For example, each operation described in this disclosure may be performed in parallel, sequentially, or in a different order, and the techniques disclosed in this disclosure may achieve the desired results. Preferably, the specification is not limited here.

In the technical solution disclosed herein, the acquisition, storage, application, etc. of the user's personal information are all in accordance with the provisions of relevant laws and regulations, and do not violate public order and morals.

The above specific embodiments do not limit the protection scope of the present disclosure. Those skilled in the art should appreciate that various modifications, combinations, subcombinations, and substitutions may be made depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims (16)

A method for estimating vehicle weight, the method comprising:
obtaining a wheel edge torque value of the vehicle using a speed-command-wheel edge torque mapping relationship in response to a current speed of the vehicle and a control command for the vehicle;
estimating a weight of the vehicle based on vehicle longitudinal dynamics equations using the obtained wheel edge torque values;
How to estimate vehicle weight. Obtaining the wheel edge torque value of the vehicle using the speed-command-wheel edge torque mapping relationship includes:
In accordance with the current speed of the vehicle and the control command, specifying a calibration section to which the current speed belongs and a calibration section to which the control command belongs in the speed-command-wheel-edge torque mapping relationship;
Based on the speed-command-wheel-edge torque mapping relationship, a plurality of wheel-edge torque values corresponding to the specified calibration interval according to the calibration interval to which the current speed belongs and the calibration interval to which the control command belongs. and to obtain each of
calculating a wheel edge torque value corresponding to a vehicle current speed and a control command from the plurality of wheel edge torque values;
The method according to claim 1. Calculating a wheel edge torque value corresponding to the vehicle current speed and a control command from the plurality of wheel edge torque values includes:

including;
however,
v is the current speed of the vehicle, v _t and v _t-1 are the speeds in the calibration interval to which the current speed belongs,
Cmd is a control command, Cmd _t and Cmd _t-1 are control commands in the calibration interval to which the control command belongs,
T _wheel is a wheel edge torque value, and T ₁ , T ₂ , T ₃ and T ₄ are a plurality of wheel edge torque values corresponding to the identified calibration interval.
The method according to claim 2. Estimating the weight of the vehicle based on the vehicle longitudinal dynamics equation includes:
creating a least squares recursive equation with a forgetting factor for the vehicle longitudinal dynamics equation;
performing an iterative calculation using a least squares recursive equation having the forgetting factor to obtain the weight of the vehicle;
The method according to claim 1. The vehicle longitudinal dynamic equation is created based on vehicle running state data, and the vehicle running state data is:
Vehicle speed v and vehicle acceleration

, moment of inertia J, and angular acceleration

and a road slope angle β.
The method according to claim 1. The road slope angle is estimated based on an extended Kalman filter EKF.
The method according to claim 5. Estimating the road slope angle based on the extended Kalman filter EKF is as follows:
estimating a road slope angle based on an EKF system state equation and an EKF system measurement equation;
The system state equation is

and
however,
m is the vehicle weight, the unit is kg,
v is the vehicle speed, the unit is m/s,
v(k) and v(k-1) are the vehicle speeds calculated by the k-th and k-1th iteration calculations, respectively,
Δt represents the cycle of iterative calculation when actually using EKF,
J is the moment of inertia of the vehicle, the unit is kg・^m2 ,

is ^{the vehicle angular acceleration in rad/s2} ,
T _wheel is the wheel edge torque of the vehicle, the unit is N・m,
r is the wheel roll radius of the vehicle, the unit is m,

That is, it is the equivalent wind resistance coefficient, where ρ is the air resistance coefficient, A is the vehicle effective windward area, _CD is the wind resistance coefficient,
β is the road slope angle, the unit is rad,
β(k), β(k-1), β(k-2) and β(k-3) are calculated by the kth, k-1st, k-2nd and k-3rd iterations, respectively. is the road slope angle,
μ is the rolling resistance coefficient,
g is the gravitational acceleration in m/ ^s2 , and W is the system noise vector of the EKF,
The system measurement equation for the EKF is

and
however,
z(k) represents the vehicle speed that the EKF attempts to measure;
V is the measurement noise vector of the EKF,
H is the measurement matrix, and if there is a road slope angle acquired by the sensor,

If there is no road slope angle acquired by the sensor,

is,
The method according to claim 6. The system noise vector W and the measurement noise vector V are white Gaussian noises that are independent of each other and have an average value of zero;
The method according to claim 7. The speed-command-wheel edge torque mapping relationship is predetermined based on vehicle control commands collected in advance and vehicle sensing data corresponding to the vehicle control commands,
However, the vehicle sensing data includes vehicle speed collected by vehicle sensors;
The method according to claim 1. A device for estimating vehicle weight,
a wheel edge torque value acquisition arranged to obtain a wheel edge torque value of the vehicle using a speed-command-wheel edge torque mapping relationship in response to a current speed of the vehicle and a control command for the vehicle; module and
a weight estimation module configured to use the obtained wheel edge torque values to estimate the weight of the vehicle based on vehicle longitudinal dynamics equations;
Vehicle weight estimation device. The wheel edge torque value acquisition module includes:
A first submodule that identifies a calibration section to which the current speed belongs and a calibration section to which the control command belongs in the speed-command-wheel-edge torque mapping relationship according to the current speed of the vehicle and the control command. and,
Based on the speed-command-wheel-edge torque mapping relationship, a plurality of wheel-edge torque values corresponding to the specified calibration interval according to the calibration interval to which the current speed belongs and the calibration interval to which the control command belongs. a second sub-module that obtains, respectively;
a third sub-module that calculates a wheel edge torque value corresponding to a vehicle current speed and a control command from the plurality of wheel edge torque values;
Apparatus according to claim 10. The third sub-module calculates a wheel edge torque value corresponding to the vehicle current speed and control command based on the following equation,

however,
v is the current speed of the vehicle, v _t and v _t-1 are the speeds in the calibration interval to which the current speed belongs,
Cmd is a control command, Cmd _t and Cmd _t-1 are control commands in the calibration interval to which the control command belongs,
T _wheel is a wheel edge torque value, and T ₁ , T ₂ , T ₃ and T ₄ are a plurality of wheel edge torque values corresponding to the identified calibration interval.
Apparatus according to claim 11. The weight estimation module includes:
a fourth submodule for creating a least squares recursive equation with a forgetting factor for the vehicle longitudinal dynamics equation;
a fifth sub-module for performing iterative calculations using the least squares recursive equation having the forgetting factor to obtain the weight of the vehicle;
Apparatus according to claim 10. at least one processor;
An electronic device comprising a memory communicatively connected to the at least one processor,
A command that can be executed by the at least one processor is stored in the memory, and the execution of the command by the at least one processor causes the at least one processor to execute the command according to any one of claims 1 to 9. can carry out the method described in section.
Electronics. storing computer commands for causing a computer to execute the method according to any one of claims 1 to 9;
Non-transitory computer-readable storage medium. A computer program implementing a method according to any one of claims 1 to 9 when executed by a processor.

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