Vehicle speed estimation method and device based on numerical model, medium and program product

Abstract

The invention provides a vehicle speed estimation method and device based on a numerical model, a medium and a program product, and the method comprises the steps: giving a speed reference value, carrying out the difference between the speed reference value and a speed estimation value of a previous sampling period, and obtaining an error, the speed estimation value is an estimation speed of a previous sampling period calculated based on a trapezoid rule numerical model; inputting the error into a discrete proportional-integral controller to obtain a discrete control quantity; the control quantity is input into a trapezoidal rule numerical model, a speed estimation value of a next sampling period is obtained through recursive calculation, and the trapezoidal rule numerical model is obtained by discretizing a continuous time vehicle dynamics model through a trapezoidal rule. According to the method, the numerical model based on the trapezoid rule is constructed, and online estimation of the vehicle speed can be achieved only by depending on power input of the vehicle. And a reliable vehicle speed estimation result can be effectively provided under the condition that the speed signal is lost, so that safe and stable operation of the vehicle is ensured.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle speed estimation method, device, medium and program product based on a numerical model. Background Art

[0002] The measurement of vehicle speed is of great significance in the field of automobile control, and is directly related to driving safety, cruise control and vehicle dynamic stability. In modern vehicle control systems, anti-lock braking systems, electronic stability control, cruise control systems, etc. all rely on accurate vehicle speed signals. The driver can monitor the speed indicator on the dashboard to ensure that the vehicle speed is within the legal range.

[0003] At present, the mainstream vehicle speed measurement method mainly relies on sensors or state observers (such as wheel speed sensors, GPS speed measurement systems or chassis speed sensors) to obtain vehicle speed information. Once the sensor is damaged, the signal is lost or interfered by the environment, the vehicle will not be able to obtain reliable speed information, thus affecting driving safety.

[0004] Therefore, when the speed signal is unavailable, how to effectively estimate the vehicle speed becomes a technical problem to be solved urgently in the field. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present application provides a vehicle speed estimation method, device, medium and program product based on a numerical model, so as to at least solve the problem in the prior art that the vehicle speed cannot be effectively calculated when the speed signal is not available.

[0006] In order to achieve the above objectives and other advantages, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a vehicle speed estimation method based on a numerical model, comprising:

[0008] Given a speed reference value, the speed reference value is subtracted from a speed estimation value of a previous sampling period to obtain an error, wherein the speed reference value is a target speed of the vehicle, and the speed estimation value is an estimated speed of the previous sampling period calculated based on a trapezoidal rule numerical model;

[0009] Inputting the error into a discrete proportional-integral controller to obtain a discrete control quantity;

[0010] Input the control quantity into the trapezoidal rule numerical model, and obtain the speed estimation value of the next sampling period through recursive calculation. The trapezoidal rule numerical model is obtained by discretizing the continuous-time vehicle dynamics model using the trapezoidal rule. According to a vehicle speed estimation method based on a numerical model provided by the present application, the discrete proportional-integral controller is constructed by discretizing the continuous-time proportional-integral controller. The discrete proportional-integral controller adjusts the instantaneous influence of the error through the proportional gain and accumulates the error history value through the integral gain, enabling the control quantity calculation to be iteratively calculated in a discrete-time format to correct the steady-state error of the vehicle speed.

[0011] According to a vehicle speed estimation method based on a numerical model provided by the present application, the control output of the discrete proportional-integral controller satisfies the following calculation formula:

[0012]

[0013] where, u w (k) is the control quantity, K p is the proportional gain, K i is the integral gain, T is the sampling period, e(k) is the error, is the accumulated error.

[0014] According to a vehicle speed estimation method based on a numerical model provided by the present application, the continuous-time vehicle dynamics model is established based on the power input, inertial characteristics, and environmental resistance of the vehicle, and describes the longitudinal dynamic behavior of the vehicle with a first-order nonlinear ordinary differential equation for calculating the dynamic change of the vehicle speed. Among them, the power input includes the driving torque and the braking torque, and the environmental resistance includes the air resistance and the rolling resistance.

[0015] According to a vehicle speed estimation method based on a numerical model provided by the present application, it further includes: simplifying the first-order nonlinear ordinary differential equation to obtain a unary quadratic equation about the steady-state speed of the vehicle, specifically including:

[0016] Normalize the driving torque and the braking torque into control quantities;

[0017] Normalize the dynamic system parameters into different coefficients to simplify the first-order nonlinear ordinary differential equation;

[0018] Since the directions of the air resistance and the rolling resistance are related to the speed direction, the sign function is used to approximately calculate through the following calculation formula:

[0019]

[0020] where, v c is the vehicle speed, and ε is a preset extremely small quantity;

[0021] When the vehicle speed reaches a steady state, a quadratic equation in one variable with respect to the vehicle speed is obtained, and the quadratic equation in one variable is solved to obtain the steady-state speed of the vehicle.

[0022] According to a vehicle speed estimation method based on a numerical model provided by the present application, the steady-state speed is used to optimize the adjustment parameters of the discrete proportional-integral controller to correct the steady-state error of the vehicle speed.

[0023] According to a vehicle speed estimation method based on a numerical model provided by the present application, when the control quantity is input into the trapezoidal rule numerical model, the speed estimation value of the next sampling period is obtained through recursive calculation. The trapezoidal rule numerical model is obtained by discretizing the continuous-time vehicle dynamics model using the trapezoidal rule, and includes:

[0024] Based on the simplified first-order nonlinear ordinary differential equation, discretization processing is performed using the trapezoidal rule to construct a trapezoidal rule numerical model, and the speed estimation value of the next sampling period is obtained through recursive calculation. Among them, the trapezoidal rule establishes a numerical discretization equation based on the current state of the vehicle speed and the state of the next sampling period, calculates the state change amount in combination with the dynamic system parameters, and obtains the speed estimation value that meets the physical constraints by solving the recurrence equation.

[0025] In a second aspect, the present application provides an electronic device, and the electronic device includes:

[0026] One or more processors; and a memory storing computer program instructions, and when the computer program instructions are executed, the processors execute the vehicle speed estimation method based on a numerical model as described in any one of the above.

[0027] In a third aspect, the present application provides a computer-readable storage medium, on which computer programs / instructions are stored, and when the computer programs / instructions are executed by a processor, the vehicle speed estimation method based on a numerical model as described in any one of the above is implemented.

[0028] In a fourth aspect, the present application provides a computer program product, including computer programs / instructions, and when the computer programs / instructions are executed by a processor, the vehicle speed estimation method based on a numerical model as described in any one of the above is implemented.

[0029] A vehicle speed estimation method, device, medium and program product based on a numerical model provided by the present application obtains an error by subtracting a speed reference value from the speed estimation value of the previous sampling period. The speed reference value is the target speed of the vehicle, and the speed estimation value is the estimated speed of the previous sampling period calculated based on the trapezoidal rule numerical model. The error is input into a discrete proportional-integral controller to obtain a discrete control quantity. The control quantity is input into the trapezoidal rule numerical model, and the speed estimation value of the next sampling period is obtained through recursive calculation. The trapezoidal rule numerical model is obtained by discretizing the continuous-time vehicle dynamics model using the trapezoidal rule. The present application constructs a numerical model based on the trapezoidal rule, which only depends on the power input of the vehicle (such as traction force or braking force) and can realize online estimation of vehicle speed without a speed signal. Compared with traditional methods based on sensor measurement or state observers, the present application can provide reliable vehicle speed estimation results in the case of speed signal loss or sensor failure, thus effectively ensuring the safe and stable operation of the vehicle control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other embodiments can be obtained based on these drawings.

[0031] Figure 1 It is a schematic flowchart of the vehicle speed estimation method based on the numerical model provided by the embodiment of the present application;

[0032] Figure 2 It is a schematic structural diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the drawings, details are as follows.

[0034] It should be noted that those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict. Unless otherwise defined, the technical terms or scientific terms involved in this application should have the ordinary meaning understood by those with ordinary skills in the technical field to which this application belongs. The words such as "a", "an", "one kind", "the" and the like involved in this application do not indicate a limitation in quantity and can represent a singular or plural number. The terms "including", "comprising", "having" and any variations thereof involved in this application are intended to cover non-exclusive inclusion; the terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0035] Referring to Figure 1 as shown, this application provides a vehicle speed estimation method based on a numerical model, including:

[0036] Step S1: Given a speed reference value, subtract the speed reference value from the speed estimation value of the previous sampling period to obtain an error, where the speed reference value is the target speed of the vehicle, and the speed estimation value is the estimated speed of the previous sampling period calculated based on the trapezoidal rule numerical model.

[0037] Specifically, in the vehicle dynamics model, the change of vehicle speed is jointly affected by multiple factors, including traction force, braking force, air resistance, rolling resistance, etc. These factors will affect the motion state of the vehicle over time. To accurately simulate the dynamic behavior of the vehicle, the continuity of speed change and real-time feedback must be considered.

[0038] In this application, the trapezoidal rule is used to discretize the continuous-time vehicle dynamics model to obtain the trapezoidal rule numerical model. The calculation of the trapezoidal rule numerical model is carried out on discrete sampling periods, rather than based on continuous time points. Therefore, the vehicle speed estimation is updated in a discrete time series, rather than evolving on a continuous time axis. The given speed reference value (the target speed of the vehicle) represents the ideal state that the vehicle should reach. The speed estimation value of the previous sampling period represents the estimated speed of the previous sampling period calculated based on the trapezoidal rule numerical model. By calculating the error between the two, the deviation between the current state (actual speed) and the target speed (reference speed) of the vehicle can be reflected in real time. At this time, the error calculation reflects the dynamic state of the vehicle under the current control input (such as acceleration, braking, driving environment).

[0039] The speed reference value of the vehicle is the speed that the control system hopes the vehicle to reach, which can be input by the driver (such as cruise control) or given by the path planning algorithm. By giving the speed reference value, an expected target can be provided for the motion state of the vehicle, facilitating the adjustment of the vehicle control system. Therefore, by setting the target speed, the control system of the vehicle can adjust the control input (such as traction or braking force) in real time to keep the vehicle within the expected speed range and avoid deviating too fast or too slow.

[0040] Exemplarily, a speed reference value v r (k) is given, and the difference between this speed reference value and the speed estimation value v c (k) in the previous sampling period is calculated to obtain the error as:

[0041]

[0042] where k is the sampling time index, representing the number of sampling calculations; t is the physical time, representing the actual driving time of the vehicle; and T is the sampling period.

[0043] Step S2: Input the error into the discrete proportional-integral controller to obtain the discrete control quantity.

[0044] In this embodiment, the control output of the discrete proportional-integral controller satisfies the following calculation formula:

[0045]

[0046] where u w (k) is the control quantity, K p is the proportional gain, K i is the integral gain, T is the sampling period, e(k) is the error, is the cumulative error.

[0047] In practical applications, the driver of the vehicle expects to maintain a certain driving state by controlling the speed of the vehicle. This control process is similar to a continuous-time proportional-integral controller (PI controller). By continuously adjusting the control input, the PI controller can adjust the acceleration or braking of the vehicle to make it approach the target speed. The form of the continuous-time PI controller is as follows:

[0048]

[0049] where u w is the output of the PI controller; e(t) = v r (t) - v c (t) is the error, representing the error between the reference speed and the currently estimated vehicle speed; K p is the proportional gain of the PI controller; K iis the proportional gain of the PI controller; is the integral term of the error, representing the accumulation of the error from time 0 to the current moment t, and τ is the integration variable.

[0050] In practical applications, digital systems cannot directly process continuous-time PI controllers, so they need to be discretized, that is, converting the continuous-time control system into a discrete-time system suitable for digital computing. To convert a continuous-time PI controller into a discrete-time controller (discrete proportional-integral controller), the continuous-time formula of the control system needs to be discretized. Discretization is usually achieved by sampling the input and output signals of the controller and converting them into control quantities at discrete time steps. In a discrete-time control system, the sampling period determines the update frequency of the control system, that is, the control system samples and calculates the control quantity every sampling period. Assuming the sampling period is T, the discretized PI controller can be expressed as:

[0051]

[0052] u w (k) is the control quantity at the current sampling period k of discrete time, representing the adjustment quantity based on the current error and historical errors, usually corresponding to the control input of the vehicle; e(k) is the error at the current sampling period k, defined as the difference between the speed reference value v r (k) and the speed estimation value v c (k) at the previous sampling period; K p and K i are the proportional gain and integral gain respectively; the integral term represents the accumulation of errors, that is, the sum of all errors from the initial state to the current sampling period.

[0053] In a discrete-time control system, each update of the controller can be regarded as an iteration, that is, the process of calculating the control quantity each time. In a digital system, the state of the system (such as vehicle speed) is sampled at discrete time points. After each sampling, the system is updated according to the input and output at the current moment. For example, k = 0 may represent the initial state, k = 1 represents the first sampling period, and so on. Therefore, k in the discrete control system represents both the number of iterations, that is, the number of times the controller is updated, and the sample index, that is, the number of periods from the start of the system operation to the current sampling period. Through this index, the system can update the control quantity at discrete sampling periods and adjust the state of the system.

[0054] In this embodiment, the discrete proportional-integral controller is constructed by discretizing a continuous-time proportional-integral controller. The discrete proportional-integral controller adjusts the instantaneous impact of the error through the proportional gain and accumulates the historical error values through the integral gain, enabling the control quantity calculation to be iteratively calculated in a discrete-time format to correct the steady-state error of the vehicle speed.

[0055] Specifically, the proportional control K p e(k) is proportional to the error and directly responds to the current error. By adjusting the proportional gain K p , the sensitivity of the controller to the immediate error can be changed. The integral control integrates the historical values of the error and can eliminate long-term steady-state errors. By adjusting the integral gain K i , the controller can eliminate persistent steady-state errors. Especially when the sensor signals are unstable or the vehicle dynamics change greatly, the estimation accuracy can be maintained. By combining proportional control and integral control, the PI controller can avoid excessive changes in the control input, smoothly transition the vehicle speed to the target speed, and prevent violent acceleration and deceleration.

[0056] Therefore, through the implementation of the discretized controller, real-time calculations can be performed on discrete sampling periods to adapt to the digital computing environment. Ensure that the vehicle speed estimation process responds in real time, suitable for embedded systems or applications that require real-time calculations. By dynamically adjusting the control quantity, the PI controller can more accurately track the target speed, reduce errors, and thus improve the accuracy of vehicle speed estimation.

[0057] The trapezoidal rule numerical model is usually established during the system design phase and stored in the control system in a parameterized manner for subsequent calls for real-time calculation and update. Therefore, before step S3, it also includes: constructing the trapezoidal rule numerical model.

[0058] In this embodiment, the continuous-time vehicle dynamics model is established based on the vehicle's power input, inertial characteristics, and environmental resistance, and describes the longitudinal dynamic behavior of the vehicle with a first-order nonlinear ordinary differential equation, which is used to calculate the dynamic change of the vehicle speed. Among them, the power input includes driving torque and braking torque, and the environmental resistance includes air resistance and rolling resistance. Specifically:

[0059] Newton's second law describes the relationship between the motion of an object and the forces acting on it. For a vehicle, the motion of the vehicle is not only affected by ground friction forces (such as traction force, braking force, etc.), but also affected by environmental resistances such as air resistance and rolling resistance. Assuming that the vehicle is driving on a dry, flat, and hard straight road, the vehicle speed v cis the only state variable and also the output of the system. The continuous-time vehicle dynamics model is modeled as a continuous-time single-input single-output first-order nonlinear system. This means that the change in vehicle speed (output) depends on the current speed (state) and the input control (such as traction torque or braking torque). This relationship is nonlinear and is represented by a differential equation with nonlinear terms (such as air resistance, rolling resistance, etc.). The dynamic behavior of the vehicle is represented by a first-order nonlinear ordinary differential equation (ODE) as follows:

[0060]

[0061] where m is the vehicle mass, g is the acceleration due to gravity, ρ air is the air density, A is the frontal area of the vehicle, C d is the aerodynamic drag coefficient, μ1 is the rolling resistance coefficient related to the longitudinal force and road surface conditions, μ = μ2g, μ2 is the viscous friction coefficient or damping coefficient, τ wheel is the driving torque acting on the driving wheels, τ brake is the braking torque, r wheel is the wheel radius.

[0062] In this embodiment, it further includes: simplifying the first-order nonlinear ordinary differential equation to obtain a quadratic equation in one variable about the steady-state speed of the vehicle, specifically including:

[0063] Normalize the driving torque and the braking torque into control variables;

[0064] Normalize the dynamic system parameters into different coefficients to simplify the first-order nonlinear ordinary differential equation;

[0065] Since the directions of the air resistance and the rolling resistance are related to the direction of the speed, the sign function is used to approximately calculate through the following calculation formula:

[0066]

[0067] where v c is the vehicle speed, and ε is a preset extremely small quantity;

[0068] When the vehicle speed reaches the steady state, a quadratic equation in one variable about the vehicle speed is obtained, and the quadratic equation in one variable is solved to obtain the steady-state speed of the vehicle.

[0069] Specifically, normalize the driving torque and the braking torque into control variables, that is, define the control variable as Divide both sides of the equation of the first-order nonlinear ordinary differential equation by the vehicle mass m to obtain:

[0070]

[0071] Define the coefficient a = μ2g, d = μ1g, Then the first-order nonlinear ordinary differential equation can be simplified to:

[0072]

[0073] Since the directions of air resistance and rolling resistance are opposite to the velocity direction, the sign function sgn(v c ) is used to ensure that the acting directions of these forces change with the velocity direction. When v c > 0 (driving forward), the air resistance and rolling resistance are negative and resist the motion; when v c < 0 (reversing), the directions of air resistance and rolling resistance change. Therefore, the relationships between the parameters b, d and v c are:

[0074] b = b′sgn(v c ), d = d′sgn(v c )

[0075] To be closer to the actual situation, we define: where ε is a preset extremely small quantity.

[0076] When the vehicle reaches the steady state, that is a quadratic equation about v c is obtained, and the steady-state velocity can be solved as:

[0077]

[0078] Therefore, it can be proved that the relationship between the vehicle velocity and the control quantity is nonlinear.

[0079] In this embodiment, the steady-state velocity is used to optimize the adjustment parameters of the discrete proportional-integral controller to correct the steady-state error of the vehicle velocity.

[0080] The core objective of the control system is to make the vehicle reach the target velocity v r , so it is necessary to calculate the steady-state error: e ∞ = v r - v c (∞). If the steady-state error e ∞ ≠ 0, it means that there is still an error in the system at the steady state, and it may be necessary to adjust the adjustment parameters of the controller (such as the PI control gain). If the error is large, the integral gain K i can be adjusted as needed to eliminate the steady-state deviation. If the error is too small, it means that the vehicle velocity is already close to the target velocity. At this time, too large a control input is not required to continue adjusting the vehicle speed, and the system should be adjusted to a lower control input so that the vehicle runs at a constant speed. For example, reducing the application of traction force (acceleration) or braking force can effectively reduce the working intensity of the power system, thereby reducing energy waste.

[0081] In this embodiment, step S3: Input the control quantity into the trapezoidal rule numerical model, and obtain the speed estimation value of the next sampling period through recursive calculation. The trapezoidal rule numerical model is obtained by discretizing the continuous-time vehicle dynamics model using the trapezoidal rule, and specifically includes:

[0082] Based on the simplified first-order nonlinear ordinary differential equation, use the trapezoidal rule for discretization processing, construct the trapezoidal rule numerical model, and obtain the speed estimation value of the next sampling period through recursive calculation. Among them, the trapezoidal rule establishes a numerical discretization equation based on the current state of the vehicle speed and the state of the next sampling period, calculates the state change amount in combination with the dynamic system parameters, and obtains the speed estimation value that conforms to the physical constraints by solving the recursive equation.

[0083] Specifically, since the dynamic characteristics of the vehicle are described by differential equations, and the computer cannot directly solve continuous differential equations, numerical methods (such as the trapezoidal rule) are required for discretization. The trapezoidal rule is a numerical integration method used to convert a continuous-time differential equation into a discrete-time recursive formula. Starting from the simplified first-order nonlinear ordinary differential equation and using the trapezoidal rule for discretization processing, the recursive equation of the trapezoidal rule numerical model is obtained as follows:

[0084]

[0085] In the formula,

[0086] This equation is formally a quadratic equation, and solving this quadratic equation usually gives two solutions (two roots). According to the requirements of the physical model, only one solution is reasonable and satisfies the actual physical condition constraints. Therefore, select the root that conforms to the actual physical conditions as the speed estimation value of the next sampling period, as follows:

[0087]

[0088] Therefore, using the simplified continuous-time vehicle dynamics model, high-efficiency online speed estimation is achieved after discretization processing using the trapezoidal rule. Compared with traditional methods such as complex state observers or Kalman filters, the calculation complexity of the vehicle estimation method in this application is significantly reduced, making it suitable for real-time applications. At the same time, the trapezoidal rule considers two consecutive discrete sampling periods, and can approximate the true solution more accurately. When the sampling step size is reasonably selected, the estimation accuracy of the numerical model can be closer to the true value.

[0089] In summary, a vehicle speed estimation method based on a numerical model provided by the present application obtains an error by subtracting a speed reference value from the speed estimation value of the previous sampling period. The speed reference value is the target speed of the vehicle, and the speed estimation value is the estimated speed of the previous sampling period calculated based on the trapezoidal rule numerical model. The error is input into a discrete proportional-integral controller to obtain a discrete control quantity. The control quantity is input into the trapezoidal rule numerical model, and the speed estimation value of the next sampling period is obtained through recursive calculation. The trapezoidal rule numerical model is obtained by discretizing the continuous-time vehicle dynamics model using the trapezoidal rule. The present application realizes online estimation of vehicle speed by constructing a numerical model based on the trapezoidal rule, relying only on the power input of the vehicle (such as traction force or braking force) without the need for a speed signal. Compared with traditional methods based on sensor measurement or state observers, the present application can provide reliable vehicle speed estimation results in the case of speed signal loss or sensor failure, thus effectively ensuring the safe and stable operation of the vehicle control system.

[0090] Those skilled in the art can understand that in the above method of the specific embodiment, the writing order of each step does not mean a strict execution order that constitutes any limitation on the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0091] In addition, some embodiments of the present application also provide an electronic device. The electronic device can be various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and so on. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices.

[0092] The electronic device includes: one or more processors; and a memory storing computer program instructions, which when executed cause the processors to execute the vehicle speed estimation method based on a numerical model provided by any one or more of the above embodiments. Figure 2 An exemplary structural diagram of the electronic device is disclosed. As Figure 2As shown, the electronic device includes: one or more processors 1101, a memory 1102, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component is interconnected using different buses and can be mounted on a common motherboard or otherwise installed as required. The processor can process instructions executed within the electronic device, including instructions stored in the memory or on the memory for displaying graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories if needed. Similarly, multiple electronic devices can be connected, with each device providing part of the necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Among them, the components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0093] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103, and the output device 1104 can be connected by a bus or other means. Figure 2 Taking connection by bus as an example.

[0094] The input device 1103 can receive input digital or character information and generate key signal inputs related to the user settings and function controls of the electronic device, such as input devices like a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 1104 can include a display device, an auxiliary lighting device (such as an LED), and a haptic feedback device (such as a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0095] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device for displaying information to the user (such as a cathode ray tube (CRT, Cathode- Raya (Tube) or an LCD monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball), through which a user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0096] In the embodiments of the present application, a computer program / instructions is stored on a computer-readable medium. When the computer program / instructions is executed by a processor, the method for estimating vehicle speed based on a numerical model provided in any one or more of the above embodiments is implemented. The computer-readable medium can be included in the electronic device described in the above embodiments; or it can exist separately without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0097] The memory 1102 can be used as a non-transitory computer-readable storage medium for storing non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided in any one or more of the above embodiments of the present application.

[0098] The memory 1102 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 1102 can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 1102 optionally includes a memory remotely set relative to the processor 1101, and these remote memories can be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0099] It should be noted that more specific examples of computer-readable storage media may include, but are not limited to: electrical connections with 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, optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0100] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0101] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can 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 can be connected to an external computer (e.g., by connecting through the Internet using an Internet service provider).

[0102] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. For example, an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device can be used. In some embodiments, the software program of this application can be executed by a processor to implement the above steps or functions. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, or a floppy disk and similar devices. Additionally, some steps or functions of this application can be implemented using hardware, for example, as a circuit that cooperates with a processor to execute each step or function.

[0103] The computer program product provided by the embodiments of the present application includes one or more computer programs / instructions. When the computer programs / instructions are executed by a processor, they wholly or partly generate the processes or functions described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0104] The flowcharts or block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions marked in the blocks may occur in an order different from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0105] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily make changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims. The above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A vehicle speed estimation method based on a numerical model, characterized in that, Including: Given a speed reference value, the difference between the speed reference value and the speed estimate of the previous sampling period is calculated to obtain an error, where the speed reference value is the target speed of the vehicle, and the speed estimate is the estimated speed of the previous sampling period calculated based on the trapezoidal rule numerical model; The error is input into a discrete proportional-integral controller to obtain a discrete control quantity; The control quantity is input into the trapezoidal rule numerical model, and the speed estimate of the next sampling period is obtained through recursive calculation. The trapezoidal rule numerical model is obtained by discretizing the continuous-time vehicle dynamics model using the trapezoidal rule.

2. The vehicle speed estimation method based on a numerical model according to claim 1, wherein The discrete proportional-integral controller is constructed by discretizing the continuous-time proportional-integral controller. The discrete proportional-integral controller adjusts the instantaneous influence of the error through the proportional gain and accumulates the error history value through the integral gain, enabling the control quantity calculation to be iteratively calculated in a discrete-time format to correct the steady-state error of the vehicle speed.

3. The method for estimating vehicle speed based on a numerical model according to claim 1 or 2, characterized in that The control output of the discrete proportional-integral controller satisfies the following calculation formula: where u w (k) is the control quantity, K p is the proportional gain, K i is the integral gain, T is the sampling period, e(k) is the error, is the cumulative error.

4. The method for estimating vehicle speed based on a numerical model according to claim 1, characterized in that, The continuous-time vehicle dynamics model is established based on the vehicle's power input, inertial characteristics, and environmental resistance, and describes the longitudinal dynamics behavior of the vehicle with a first-order nonlinear ordinary differential equation, which is used to calculate the dynamic change of the vehicle speed. Among them, the power input includes driving torque and braking torque, and the environmental resistance includes air resistance and rolling resistance.

5. The vehicle speed estimation method based on a numerical model according to claim 4, characterized in that Also including: Simplify the first-order nonlinear ordinary differential equation to obtain a quadratic equation in one variable about the steady-state speed of the vehicle, specifically including: Normalize the driving torque and braking torque into control quantities; Normalize the dynamic system parameters into different coefficients to simplify the first-order nonlinear ordinary differential equation; Since the directions of air resistance and rolling resistance are related to the speed direction, the sign function is used to approximately calculate through the following calculation formula: where v c is the vehicle speed and ε is a preset extremely small quantity; When the vehicle speed reaches the steady state, a quadratic equation in one variable about the vehicle speed is obtained, and the quadratic equation in one variable is solved to obtain the steady-state speed of the vehicle.

6. The vehicle speed estimation method based on a numerical model according to claim 5, wherein The steady-state speed is used to optimize the adjustment parameters of the discrete proportional-integral controller to correct the steady-state error of the vehicle speed.

7. The vehicle speed estimation method based on a numerical model according to claim 5, wherein The step of inputting the control quantity into the trapezoidal rule numerical model and obtaining the speed estimate of the next sampling period through recursive calculation. The trapezoidal rule numerical model is obtained by discretizing the continuous-time vehicle dynamics model using the trapezoidal rule, includes: Based on the simplified first-order nonlinear ordinary differential equation, discretize it using the trapezoidal rule to construct a trapezoidal rule numerical model, and obtain the speed estimate of the next sampling period through recursive calculation. The trapezoidal rule establishes a numerical discrete equation based on the current state and the state of the next sampling period of the vehicle speed, calculates the state change amount in combination with the dynamic system parameters, and obtains a speed estimate that conforms to physical constraints by solving the recursive equation.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; and a memory storing computer program instructions, which when executed, cause the processor to execute the numerical model-based vehicle speed estimation method according to any one of claims 1-7.

9. A computer-readable storage medium having computer programs / instructions stored thereon, characterized in that, When the computer program / instructions are executed by a processor, they implement the numerical model-based vehicle speed estimation method according to any one of claims 1-7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, they implement the numerical model-based vehicle speed estimation method according to any one of claims 1-7.

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