Control method, device and electronic equipment for vehicle driving

Abstract

This application provides a method, device, and electronic device for controlling vehicle movement, which can be applied to the fields of unmanned driving, autonomous driving, and driverless vehicle technology. The method includes: when the (k-1)th vehicle operating state does not meet a preset braking suppression condition or the (k-1)th excitation parameter value is greater than a preset excitation intensity threshold, processing the (k-1)th vehicle operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle mass estimate, and the (k-1)th coupling parameter estimate using an estimation model to obtain the (k-1)th wheel longitudinal force estimate; obtaining the (k-1)th model estimation error based on the (k-1)th wheel longitudinal force estimate and the actual value of the (k-1)th wheel longitudinal force; updating the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value to obtain the (k)th vehicle mass estimate and the (k)th coupling parameter estimate; and obtaining the (k)th rolling resistance coefficient based on the (k)th coupling parameter estimate and the (k)th vehicle mass estimate.

Description

Technical Field

[0001] This application relates to the fields of driverless, autonomous driving and driverless vehicle technology, and specifically to a method, device and electronic device for controlling vehicle movement. Background Technology

[0002] In the longitudinal dynamics control of heavy-duty trucks in existing autonomous driving, self-driving, and driverless vehicles, the vehicle's total mass and rolling resistance coefficient are key time-varying parameters affecting the accuracy of drive, braking, and cruise control. However, related technologies estimate the vehicle's total mass and rolling resistance coefficient with significant deviations, resulting in low longitudinal control safety. Summary of the Invention

[0003] In view of the above problems, this application provides a method, device and electronic device for controlling vehicle movement.

[0004] According to a first aspect of this application, a method for controlling vehicle driving is provided, comprising: controlling vehicle driving according to a vehicle control strategy at multiple stages, wherein the k-th control strategy at the k-th stage is determined based on the following operations: acquiring the (k-1)-th vehicle operating state, the (k-1)-th road slope parameter value, the (k-1)-th excitation parameter value, the (k-1)-th vehicle mass estimate value, and the (k-1)-th coupling parameter estimate value of the target vehicle at time (k-1), wherein the (k-1)-th excitation parameter value represents the comprehensive excitation information of the vehicle's longitudinal motion state and the road slope, the (k-1)-th coupling parameter estimate value represents the combined effect of the vehicle mass and the rolling resistance coefficient, and the (k-1)-th vehicle mass estimate value represents the total mass estimate result of the vehicle during driving at the (k-1)-th stage, where k is an integer greater than 1; and controlling vehicle driving when the (k-1)-th vehicle operating state does not meet a preset braking suppression condition or the (k-1)-th excitation parameter value is greater than a preset excitation intensity threshold. The estimation model is used to process the (k-1)th vehicle's operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle's mass estimate, and the (k-1)th coupling parameter estimate to obtain the (k-1)th wheel longitudinal force estimate. The (k-1)th model estimation error is obtained based on the difference between the (k-1)th wheel longitudinal force estimate and the actual (k-1)th wheel longitudinal force value, which is determined based on the (k-1)th vehicle's operating state. The (k-1)th vehicle's mass estimate and the (k-1)th coupling parameter estimate are updated based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value to obtain the (k)th vehicle's mass estimate and the (k)th coupling parameter estimate. The (k)th rolling resistance coefficient is obtained based on the ratio between the (k)th coupling parameter estimate and the (k)th vehicle's mass estimate. Finally, the (k)th vehicle's control strategy is determined based on the (k)th vehicle's mass estimate and the (k)th rolling resistance coefficient.

[0005] According to an embodiment of this application, the (k-1)th damping correction value is determined based on the following operations: the difference between the initial vehicle mass and the estimated (k-1)th vehicle mass is corrected according to a first damping correction coefficient to obtain a first damping correction value; the initial coupling parameter is obtained based on the product between the initial vehicle mass and the initial rolling resistance coefficient; the difference between the estimated (k-1)th coupling parameter and the estimated (k-1)th coupling parameter is corrected according to a second damping correction coefficient to obtain a second damping correction value; and the (k-1)th damping correction value is obtained based on the first and second damping correction values.

[0006] According to an embodiment of this application, updating the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value to obtain the kth vehicle mass estimate and the kth coupling parameter estimate includes: obtaining a first rate of change based on a first damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value, wherein the first rate of change characterizes the update rate of the (k-1)th vehicle mass estimate over time; obtaining a second rate of change based on a second damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value, wherein the second rate of change characterizes the update rate of the (k-1)th coupling parameter estimate over time; and updating the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate based on the first rate of change and the second rate of change to obtain the kth vehicle mass estimate and the kth coupling parameter estimate.

[0007] According to an embodiment of this application, updating the estimated value of the (k-1)th vehicle mass and the estimated value of the (k-1)th coupling parameter based on a first rate of change and a second rate of change to obtain the estimated value of the kth vehicle mass and the estimated value of the kth coupling parameter includes: obtaining the estimated value of the kth vehicle mass based on the time interval between the kth time and the (k-1)th time, the first rate of change, and the estimated value of the (k-1)th vehicle mass; and obtaining the estimated value of the kth coupling parameter based on the time interval between the kth time and the (k-1)th time, the second rate of change, and the estimated value of the (k-1)th coupling parameter.

[0008] According to an embodiment of this application, the (k-1)th vehicle operating state includes the (k-1)th motor torque, the (k-1)th transmission efficiency, and the (k-1)th wheel braking pressure; the preset braking suppression condition includes a first suppression condition or a second suppression condition, wherein the first suppression condition is that the motor torque is less than a preset torque threshold, and the second suppression condition is that the wheel braking pressure is greater than a preset pressure threshold; the true value of the longitudinal force of the (k-1)th wheel is determined based on the (k-1)th motor torque and the (k-1)th transmission efficiency.

[0009] According to an embodiment of this application, the (k-1)th vehicle operating state further includes the (k-1)th vehicle longitudinal acceleration; wherein, by processing the (k-1)th vehicle operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value using an estimation model, the (k-1)th wheel longitudinal force estimate value is obtained, including: obtaining the longitudinal composite acceleration based on the (k-1)th vehicle longitudinal acceleration, gravitational acceleration, and a sine value determined based on the (k-1)th road slope parameter value; obtaining the vertical gravity component based on the gravitational acceleration and a cosine value determined based on the (k-1)th road slope parameter value; and obtaining the (k-1)th wheel longitudinal force estimate value based on the product between the longitudinal composite acceleration and the (k-1)th vehicle mass estimate value, and the product between the vertical gravity component and the (k-1)th coupling parameter estimate value.

[0010] According to an embodiment of this application, the value of the (k-1)th excitation parameter is determined based on the longitudinal composite acceleration and the vertical gravity component.

[0011] According to an embodiment of this application, the method further includes: constraining the first rate of change, the second rate of change, the estimated value of the kth vehicle mass, and the estimated value of the kth coupling parameter based on a preset physical constraint range, wherein the preset physical constraint range includes a rate of change constraint range, a vehicle mass constraint range, and a coupling parameter constraint range.

[0012] A second aspect of this application provides a vehicle driving control device. The device includes a control module for controlling vehicle driving according to a multi-stage vehicle control strategy. The control module includes an acquisition submodule, a first estimation submodule, a second estimation submodule, an update submodule, a generation submodule, and a determination submodule. The acquisition submodule is used to acquire the target vehicle's (k-1)th vehicle operating state at time k-1, the (k-1)th road slope parameter value, the (k-1)th excitation parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value. The (k-1)th excitation parameter value represents the comprehensive excitation information of the vehicle's longitudinal motion state and the road slope. The (k-1)th coupling parameter estimate value represents the combined effect of the vehicle mass and the rolling resistance coefficient. The (k-1)th vehicle mass estimate value represents the total mass estimate result of the vehicle during driving at stage k-1, where k is an integer greater than 1. The first estimation submodule is used to control vehicle driving when the (k-1)th vehicle operating state does not meet the preset braking suppression condition or the (k-1)th excitation parameter value is greater than the preset value. Given an excitation intensity threshold, the estimation model processes the (k-1)th vehicle's operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle's mass estimate, and the (k-1)th coupling parameter estimate to obtain the (k-1)th wheel longitudinal force estimate. A second estimation submodule calculates the (k-1)th model estimation error based on the difference between the (k-1)th wheel longitudinal force estimate and the actual (k-1)th wheel longitudinal force value, where the actual (k-1)th wheel longitudinal force value is determined based on the (k-1)th vehicle's operating state. An update submodule calculates the (k-1)th vehicle's mass estimate and the (k)th coupling parameter estimate based on the (k-1)th damping correction value, the (k-1)th model estimation error, the (k-1)th excitation parameter value, the (k-1)th vehicle's mass estimate, and the (k-1)th coupling parameter estimate. A generation submodule calculates the (k)th rolling resistance coefficient based on the ratio between the (k)th coupling parameter estimate and the (k)th vehicle's mass estimate. A determination submodule determines the (k)th vehicle's control strategy based on the (k)th vehicle's mass estimate and the (k)th rolling resistance coefficient.

[0013] A third aspect of this application provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the methods described above.

[0014] According to the vehicle control method provided in this application, since the coupling parameters are determined based on the vehicle mass and rolling resistance coefficient, and the vehicle mass and coupling parameters are used as dual parameters to be estimated, the vehicle mass and rolling resistance coefficient are decoupled and synchronously identified within the same estimation model, eliminating the coupling estimation error caused by incorporating the rolling resistance coefficient into a fixed constant in related single-parameter schemes. By automatically pausing parameter updates when the vehicle's operating state meets the braking suppression condition or the excitation parameter value is lower than the preset excitation intensity threshold, estimation divergence and covariance expansion are reduced. A dual-parameter adaptive update rate of change is constructed based on the model estimation error, excitation parameter value, and damping correction term. When there is a lack of continuous excitation, the estimated value is slowly pulled back to the vicinity of the benchmark estimated value by correcting the damping term, achieving stable convergence of the parameter estimation process. At each stage, a vehicle control strategy is formulated based on the real-time obtained vehicle mass estimate and rolling resistance coefficient, improving the calculation accuracy of longitudinal driving force or braking force, reducing longitudinal control deviation caused by parameter estimation mismatch, and significantly improving the longitudinal control safety and comfort in scenarios such as adaptive cruise following, emergency braking, and cruise control. Attached Figure Description

[0015] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments of this application with reference to the accompanying drawings.

[0016] Figure 1 The diagram illustrates an application scenario of a vehicle control method, apparatus, and electronic device according to embodiments of this application.

[0017] Figure 2 A flowchart of a method for controlling vehicle movement according to an embodiment of this application is shown.

[0018] Figure 3 A flowchart illustrating the process of obtaining the (k-1)th damping correction value according to an embodiment of this application is shown.

[0019] Figure 4 A structural block diagram of a vehicle driving control device according to an embodiment of this application is shown.

[0020] Figure 5 A block diagram of an electronic device suitable for implementing a method of controlling vehicle driving according to an embodiment of this application is shown. Detailed Implementation

[0021] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0024] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0025] Because the target vehicle's load changes during operation due to passenger boarding / alighting, luggage loading / unloading, or towing, and because the rolling resistance coefficient changes due to alternating road materials and environmental conditions, the vehicle's mass and rolling resistance coefficient deviate from the factory calibration values. If the onboard controller always performs following control based on fixed calibration parameters, it will lead to problems such as deviations in braking force calculation, inaccurate following distance control, and fluctuations in cruising speed. Therefore, it is necessary to estimate the vehicle's mass and rolling resistance coefficient in real time to dynamically correct the longitudinal force.

[0026] Traditional parameter estimation methods often employ recursive least squares and extended Kalman filtering, which only estimate vehicle mass without considering the coupling effect of rolling resistance coefficient, resulting in insufficient model estimation accuracy. Furthermore, they fail to model and dynamically correct sensor noise, making them prone to covariance expansion and parameter drift. Additionally, the braking force model under braking conditions exhibits strong nonlinearity and large errors, which, if not effectively suppressed by related algorithms, can easily lead to estimation divergence. Moreover, the lack of rigorous stability proofs means that parameter convergence and robustness cannot be guaranteed, making it difficult to meet the high reliability and high precision control requirements of autonomous vehicles.

[0027] In view of this, embodiments of this application provide a method for controlling vehicle movement. The method includes: when the (k-1)th vehicle operating state does not meet a preset braking suppression condition or the (k-1)th excitation parameter value is greater than a preset excitation intensity threshold, processing the (k-1)th vehicle operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle mass estimate, and the (k-1)th coupling parameter estimate using an estimation model to obtain the (k-1)th wheel longitudinal force estimate; obtaining the (k-1)th model estimation error based on the (k-1)th wheel longitudinal force estimate and the actual value of the (k-1)th wheel longitudinal force; updating the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value to obtain the (k)th vehicle mass estimate and the (k)th coupling parameter estimate; and obtaining the (k)th rolling resistance coefficient based on the (k)th coupling parameter estimate and the (k)th vehicle mass estimate.

[0028] In the technical solution of this application, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0029] Figure 1 The diagram illustrates an application scenario of a vehicle control method, apparatus, and electronic device according to embodiments of this application.

[0030] like Figure 1 As shown, the application scenario 100 of the vehicle driving control method, device, and electronic device according to the embodiments of this application includes a terminal device 101, a server 102, and a network 103. The network 103 serves as a medium for providing a communication link between the terminal device 101 and the server 102. The network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables. The test server 102 may be a server providing test services. The terminal device 101 may be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0031] Server 102 can be a server that provides various services, such as a backend management server that supports websites browsed by users using terminal device 101 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0032] It should be noted that the vehicle driving control method provided in this application embodiment can generally be executed by server 102. Accordingly, the vehicle driving control device provided in this application embodiment can generally be set in server 102.

[0033] It should be understood that Figure 1 The number of terminal devices shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices and servers.

[0034] It should be noted that the sequence numbers of the operations in the following methods are for descriptive purposes only and should not be considered as indicating the execution order of the operations. Unless explicitly stated otherwise, the method does not need to be executed in the exact order shown.

[0035] Figure 2 A flowchart of a method for controlling vehicle movement according to an embodiment of this application is shown.

[0036] like Figure 2 As shown, the method includes operations S210 to S260.

[0037] In operation S210, the target vehicle's (k-1)th vehicle operating state, (k-1)th road slope parameter value, (k-1)th excitation parameter value, (k-1)th vehicle mass estimate value, and (k-1)th coupling parameter estimate value are obtained at time (k-1).

[0038] In operation S220, if the (k-1)th vehicle operating state does not meet the preset braking suppression condition or the (k-1)th excitation parameter value is greater than the preset excitation intensity threshold, the estimation model is used to process the (k-1)th vehicle operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value to obtain the (k-1)th wheel longitudinal force estimate value.

[0039] In operation S230, the estimation error of the (k-1)th model is obtained based on the difference between the estimated value of the longitudinal force of the (k-1)th wheel and the actual value of the longitudinal force of the (k-1)th wheel.

[0040] In operation S240, based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value, the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate are updated to obtain the (k)th vehicle mass estimate and the (k)th coupling parameter estimate.

[0041] In operation S250, the k-th rolling resistance coefficient is obtained based on the ratio between the estimated value of the k-th coupling parameter and the estimated value of the k-th vehicle mass.

[0042] In operation S260, the control strategy for the k-th vehicle is determined based on the estimated mass of the k-th vehicle and the rolling resistance coefficient of the k-th vehicle.

[0043] The target vehicle is a heavy vehicle in autonomous driving or driverless mode. In the adaptive cruise control scenario, it is necessary to estimate the vehicle mass and rolling resistance coefficient in real time to dynamically correct the longitudinal force demand, thereby generating a vehicle control strategy to control the longitudinal dynamic motion of the target vehicle.

[0044] The K estimation update phases include 1 to K-1 non-convergence phases and a final convergence phase. In the non-convergence phase, the vehicle mass estimate and rolling resistance coefficient are continuously iteratively updated with a large adaptive gain and distributed to the longitudinal drive, braking, and cruise controllers to generate vehicle control strategies to improve the accuracy of driving force or braking force to ensure longitudinal control safety, where k is less than K. In the convergence phase, the baseline estimate is slowly and adaptively updated in small increments to track changes in load and road conditions in real time.

[0045] In one embodiment, the convergence condition requires that the following two conditions be met simultaneously: the effective coupling parameter estimate and the vehicle mass estimate are updated ≥300 times; and the relative dispersion of the vehicle mass estimate within the nearest window is <1.5%.

[0046] At each stage, the on-board controller collects real-time signals from existing sensors on the vehicle bus at sampling intervals, without the need to add any additional sensors. Existing signals include vehicle operating status and road slope parameter values, etc.

[0047] For example, vehicle operating status can include information such as motor torque, braking pressure, vehicle speed, and longitudinal acceleration, while road slope parameter value is the pitch angle of the target vehicle on the current road.

[0048] The first vehicle mass estimate can be the factory-calibrated unloaded curb weight of the target vehicle. The first coupling parameter estimate can be determined based on the product between the calibrated unloaded curb weight and the nominal rolling resistance coefficient. The first vehicle mass estimate and the first coupling parameter estimate are the baseline estimates.

[0049] In the k-th stage, the target vehicle's (k-1)-th vehicle operating state, (k-1)-th road slope parameter value, and (k-1)-th vehicle mass estimate and (k-1)-th coupling parameter estimate value output by the model are obtained.

[0050] The (k-1)th excitation parameter value is calculated based on the longitudinal acceleration of the (k-1)th vehicle in its operating state and the (k-1)th road slope parameter value. The (k-1)th excitation parameter value represents the comprehensive excitation information of the vehicle's longitudinal motion state and the road slope.

[0051] The estimated value of the (k-1)th coupling parameter characterizes the combined effect of vehicle mass and rolling resistance coefficient.

[0052] The (k-1)th vehicle mass estimate represents the total mass estimate of the vehicle during the (k-1)th stage, where k is an integer greater than 1.

[0053] The preset braking suppression condition indicates that the target vehicle is in a driving condition dominated by nonlinear braking force. Specifically, it indicates that the percentage of motor output torque has entered the power generation feedback range or the hydraulic braking system has been activated. At this time, the braking force model is highly nonlinear and has large errors, making it unsuitable for updating the estimated value.

[0054] The (k-1)th vehicle's operating state does not meet the preset braking suppression condition, meaning that the target vehicle is in a pure driving condition or a free-slipping condition at time (k-1), the motor has not entered the generator braking and no hydraulic braking has been applied, the driving force, rolling resistance and inertial force components dominate in the longitudinal dynamics model, the model linearity assumption is valid, and the operating condition premise for updating the estimated value is met.

[0055] The preset excitation intensity threshold can be 0.15 m / s². If the value of the (k-1)th excitation parameter is less than the preset excitation intensity threshold, it means that the longitudinal dynamic excitation intensity is low. When the excitation is insufficient, the information content is too low and the estimation is invalid. Skipping the estimation update can avoid covariance expansion and parameter jumps. When the value of the (k-1)th excitation parameter is greater than or equal to the preset excitation intensity threshold, it means that the longitudinal dynamic excitation of the target vehicle at time k-1 is sufficient, the excitation parameter value reaches the identifiable level, and the excitation parameter value composed of longitudinal acceleration and road slope is sufficient to support the statistical significance estimation of vehicle mass and coupling parameters, and has the data premise for estimation update.

[0056] The estimation model is a two-parameter linear regression model of vehicle longitudinal dynamics constructed based on Newton's second law.

[0057] The estimated value of the longitudinal force of the k-1th wheel is obtained by processing the operating state of the k-1th vehicle, the road slope parameter value, the estimated value of the k-1th vehicle mass, and the estimated value of the k-1th coupling parameter using the estimation model.

[0058] In one embodiment, the true value of the longitudinal force of the (k-1)th wheel is calculated based on the motor torque, transmission ratio, transmission efficiency, and wheel radius in the (k-1)th vehicle operating state.

[0059] The estimation error of the (k-1)th model is obtained by calculating the difference between the estimated value of the longitudinal force of the (k-1)th wheel and the actual value of the longitudinal force of the (k-1)th wheel. The estimation error of the (k-1)th model represents the deviation between the actual value of the net longitudinal force of the wheel and the estimated value of the net longitudinal force of the wheel determined based on the estimation model. It is used to quantify the degree of deviation of the estimated vehicle mass and the estimated values ​​of the coupled parameters from the actual longitudinal dynamic characteristics, and serves as the driving signal for the two-parameter adaptive update law to correct the parameter estimates.

[0060] The (k-1)th damping correction value represents the damping constraint that slowly pulls the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate back to the baseline estimate when there is a lack of continuous excitation.

[0061] The (k-1)th model estimation error provides the direction and magnitude of parameter correction; the (k-1)th excitation parameter value provides a normalization scale to avoid drastic fluctuations in the update step size with the operating conditions; and the (k-1)th damping correction value provides a boundedness guarantee to prevent long-term parameter drift.

[0062] Based on the (k-1)th model estimation error and the (k-1)th excitation parameter value, the normalized gradient term is calculated. The normalized gradient term is superimposed with the (k-1)th damping correction value to obtain the (k-1)th vehicle mass estimate change rate and the (k-1)th coupling parameter estimate change rate. Based on the estimate change rate and the sampling period interval, the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate in the (k-1)th stage are recursively integrated by forward Euler discretization, so that the vehicle mass estimate and the coupling parameter estimate evolve in the direction of reducing the model estimation error, and the (k)th vehicle mass estimate and the (k)th coupling parameter estimate in the (k)th stage are obtained.

[0063] The k-th vehicle mass estimate represents the real-time estimate of the total vehicle mass after the (k-1)-th adaptive correction, and the k-th coupling parameter estimate represents the real-time estimate of the product of mass and rolling resistance coefficient after the (k-1)-th adaptive correction.

[0064] The k-th rolling resistance coefficient is a dimensionless parameter representing the rolling resistance characteristics between the tire and the road surface of the target vehicle in the k-th stage. It is obtained by decoupling the estimated value of the k-th rolling resistance coupling parameter by dividing the estimated value of the k-th vehicle mass. It is used to quantify the magnitude of the rolling resistance per unit vehicle weight, reflect the current road surface adhesion and tire rolling characteristics, and provide real-time parameter basis for road resistance compensation in the calculation of longitudinal driving force or braking force.

[0065] For example, in a fully loaded uphill start scenario, the estimated mass of vehicle k is 2200 kg, and the rolling resistance coefficient of vehicle k is 0.018. Based on this, the longitudinal controller calculates the driving torque required for starting on the slope and distributes the driving torque according to the actual fully loaded mass to avoid insufficient power and rollback on the slope caused by the unloaded calibration value. The control strategy for vehicle k includes driving torque and driving torque.

[0066] For example, in a scenario where the vehicle encounters a slippery road surface during high-speed cruising, the estimated mass of the kth vehicle is 1800 kg, and the rolling resistance coefficient of the kth vehicle is 0.028. Based on this, the longitudinal controller compensates for the additional drive torque to counteract the sudden increase in rolling resistance, maintain the set speed, and avoid the cruise speed dropping and passenger jerking. The control strategy for the kth vehicle includes compensating for the additional drive torque.

[0067] For example, in an automatic emergency braking scenario, the estimated mass of the kth vehicle is 2100 kg (fully loaded), and the rolling resistance coefficient of the kth vehicle is 0.015. Based on this, the longitudinal controller calculates the total braking force required to achieve the target deceleration, avoiding an increase in braking distance due to underestimation of mass. The control strategy for the kth vehicle includes braking force.

[0068] In the embodiments of this application, by determining the coupling parameters based on vehicle mass and rolling resistance coefficient, and treating vehicle mass and coupling parameters as dual parameters to be estimated, the vehicle mass and rolling resistance coefficient are decoupled and synchronously identified within the same estimation model, eliminating the coupling estimation error caused by incorporating the rolling resistance coefficient into a fixed constant in related single-parameter schemes. By automatically pausing parameter updates when the vehicle's operating state meets the braking suppression condition or the excitation parameter value is lower than a preset excitation intensity threshold, estimation divergence and covariance expansion are reduced. A dual-parameter adaptive update rate of change is constructed based on model estimation error, excitation parameter value, and damping correction term. In the absence of continuous excitation, the estimated value is slowly pulled back to the vicinity of the benchmark estimated value by correcting the damping term, achieving stable convergence of the parameter estimation process. At each stage, a vehicle control strategy is formulated based on the real-time obtained vehicle mass estimate and rolling resistance coefficient, improving the calculation accuracy of longitudinal driving force or braking force, reducing longitudinal control deviation caused by parameter estimation mismatch, and significantly improving the longitudinal control safety and comfort in scenarios such as adaptive cruise following, emergency braking, and cruise control.

[0069] According to an embodiment of this application, the (k-1)th vehicle operating state further includes the (k-1)th vehicle longitudinal acceleration; wherein, by processing the (k-1)th vehicle operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value using an estimation model, the (k-1)th wheel longitudinal force estimate value is obtained, including: obtaining the longitudinal composite acceleration based on the (k-1)th vehicle longitudinal acceleration, gravitational acceleration, and a sine value determined based on the (k-1)th road slope parameter value; obtaining the vertical gravity component based on the gravitational acceleration and a cosine value determined based on the (k-1)th road slope parameter value; and obtaining the (k-1)th wheel longitudinal force estimate value based on the product between the longitudinal composite acceleration and the (k-1)th vehicle mass estimate value, and the product between the vertical gravity component and the (k-1)th coupling parameter estimate value.

[0070] In one embodiment, the estimation model is as shown in formula (1):

[0071] (1).

[0072] in, Characterizing the estimated mass of the (k-1)th vehicle, Characterizing longitudinal composite acceleration, Characterizes the estimated value of the (k-1)th coupling parameter. Characterizing the vertical component of gravity, The estimated value of the longitudinal force of the (k-1)th wheel is represented.

[0073] in, , Characterizing the longitudinal acceleration of the (k-1)th vehicle, Characterizing the slope parameter value of the (k-1)th road, Characterizing gravitational acceleration, Characterizes the sine value determined based on the slope parameter value of the (k-1)th road.

[0074] in, , The cosine value is determined based on the slope parameter value of the (k-1)th road.

[0075] Longitudinal composite acceleration characterizes the combined excitation of vehicle longitudinal acceleration and road slope gravity component in the longitudinal direction, and is used to quantify the comprehensive inertial excitation intensity under driving or braking conditions in the vehicle's longitudinal dynamics system.

[0076] The vertical gravity component represents the projection of gravitational acceleration in the direction perpendicular to the road slope. It is used to quantify the gravity reference corresponding to the normal force acting on the wheel, so as to reflect the force source characteristics of rolling resistance under the normal support condition of the road surface.

[0077] According to an embodiment of this application, the value of the (k-1)th excitation parameter is determined based on the longitudinal composite acceleration and the vertical gravity component.

[0078] In one embodiment, the (k-1)th excitation parameter value As shown in formula (2):

[0079] (2).

[0080] According to an embodiment of this application, the (k-1)th vehicle operating state includes the (k-1)th motor torque, the (k-1)th transmission efficiency, and the (k-1)th wheel braking pressure; the preset braking suppression condition includes a first suppression condition or a second suppression condition, wherein the first suppression condition is that the motor torque is less than a preset torque threshold, and the second suppression condition is that the wheel braking pressure is greater than a preset pressure threshold; the true value of the longitudinal force of the (k-1)th wheel is determined based on the (k-1)th motor torque and the (k-1)th transmission efficiency.

[0081] The (k-1)th motor torque characterizes the output torque state of the drive or brake motor at time (k-1) and serves as a prerequisite for judging braking suppression conditions to identify power generation or braking conditions.

[0082] The (k-1)th transmission efficiency characterizes the degree of energy transfer loss from the motor output end to the wheel end of the power transmission system. It is used to make efficiency corrections when converting the motor output torque into the true value of the net longitudinal force of the wheel, so as to reflect the actual net driving force or braking force reaching the wheel.

[0083] The braking pressure of wheel k-1 represents the actual operating state of the hydraulic or pneumatic braking system at time k-1. It is used to determine whether the vehicle is in a mechanical braking intervention state and serves as a trigger signal for the preset braking suppression condition to pause the adaptive update of parameters.

[0084] When the motor torque is less than the preset torque threshold, it means that the drive motor has exited the drive mode and switched to the regenerative braking mode. At this time, the motor outputs negative torque to apply electrical braking torque to the wheels. The vehicle is in a nonlinear deceleration state with the superposition of energy recovery and mechanical braking or pure electrical braking. Under this condition, the estimation model has strong nonlinearity and efficiency fluctuations, and is not suitable for updating parameter estimates.

[0085] When the wheel braking pressure is greater than the preset pressure threshold, it means that the vehicle is in a deceleration state with mechanical braking intervention. Under this condition, the friction braking force and braking pressure have a nonlinear relationship and are significantly affected by temperature, wear and road surface adhesion, making it unsuitable for updating parameter estimates.

[0086] The (k-1)th vehicle's operating state does not meet the preset braking suppression condition, meaning that the (k-1)th motor torque is less than or equal to the preset torque threshold, and the (k-1)th wheel braking pressure is less than or equal to the preset pressure threshold.

[0087] For example, the preset torque threshold is −2%, and the preset pressure threshold is 0.

[0088] The true value of the longitudinal force of the (k-1)th wheel is calculated based on the (k-1)th motor torque, the (k-1)th transmission efficiency, the transmission ratio, and the wheel radius.

[0089] According to the embodiments of this application, a preset braking suppression condition is designed. When the operating state of the (k-1)th vehicle meets the preset braking suppression condition or the value of the (k-1)th excitation parameter is less than the preset excitation intensity threshold, the parameter estimation update is automatically suspended. When a braking command or braking pressure is detected, the parameter update is automatically suppressed to avoid estimation failure caused by model error and improve the robustness of the algorithm.

[0090] Figure 3 A flowchart illustrating the process of obtaining the (k-1)th damping correction value according to an embodiment of this application is shown.

[0091] like Figure 3As shown, the process includes operations S310 to S340.

[0092] In operation S310, the difference between the initial vehicle mass and the estimated mass of the (k-1)th vehicle is corrected according to the first damping correction coefficient to obtain the first damping correction value.

[0093] In operation S320, the initial coupling parameters are obtained based on the product between the initial vehicle mass and the initial rolling resistance coefficient.

[0094] In operation S330, the difference between the initial coupling parameter and the estimated value of the (k-1)th coupling parameter is corrected according to the second damping correction coefficient to obtain the second damping correction value.

[0095] In operation S340, the (k-1)th damping correction value is obtained based on the first damping correction value and the second damping correction value.

[0096] The initial vehicle mass is the preset initial mass of the whole vehicle, such as the calibrated unloaded curb weight.

[0097] The first and second damping correction factors are used to prevent parameter drift and ensure that the estimated value is bounded.

[0098] The difference between the initial vehicle mass and the estimated mass of the (k-1)th vehicle is corrected based on the first damping correction coefficient to obtain the first damping correction value.

[0099] In one embodiment, the first damping correction value As shown in formula (3):

[0100] (3).

[0101] in, Characterizing the first damping correction factor, Characterizing the estimated mass of the (k-1)th vehicle, Characterizes the initial vehicle mass.

[0102] The first damping correction value represents the regression correction amount that constrains the degree to which the estimated vehicle mass deviates from the initial vehicle mass based on the first damping correction coefficient. It is used to slowly pull the estimated vehicle mass back to near the initial vehicle mass in the absence of continuous longitudinal dynamic excitation.

[0103] For example, the initial rolling resistance coefficient can be 0.02.

[0104] The initial coupling parameters are obtained by multiplying the initial vehicle mass and the initial rolling resistance coefficient.

[0105] In one embodiment, the second damping correction value As shown in formula (4):

[0106] (4).

[0107] in, Characterizing the second damping correction factor, Characterizes the estimated value of the (k-1)th coupling parameter. Characterize the initial coupling parameters.

[0108] The second damping correction value characterizes the regression correction amount based on the second damping correction coefficient, which constrains the degree to which the estimated value of the coupling parameters deviates from the initial coupling parameters. It is used to slowly pull the estimated value of the coupling parameters back to the reference of the product of the initial vehicle mass and the initial rolling resistance coefficient in the absence of continuous longitudinal dynamic excitation.

[0109] The (k-1)th damping correction value includes the first damping correction value and the second damping correction value.

[0110] According to the embodiments of this application, a first damping correction value is obtained by correcting the difference between the initial vehicle mass and the estimated value of the (k-1)th vehicle mass based on a first damping correction coefficient, and a second damping correction value is obtained by correcting the difference between the initial coupling parameter and the estimated value of the (k-1)th coupling parameter based on a second damping correction coefficient. This ensures that under conditions where the target vehicle is traveling at a constant speed or on a long, gentle slope, where there is a lack of continuous longitudinal dynamic excitation, the parameter estimates will not continuously diverge along a single gradient direction, but will be slowly pulled back to the vicinity of the nominal reference determined by the initial vehicle mass and the initial rolling resistance coefficient. Therefore, it effectively prevents long-term parameter drift caused by sensor bias, unmodeled dynamics, or insufficient excitation parameter values, and ensures the consistency and eventual boundedness of the vehicle mass estimate and the rolling resistance coupling parameter estimate, significantly improving the robustness and estimation reliability of the algorithm under complex real-world road conditions.

[0111] According to an embodiment of this application, updating the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value to obtain the kth vehicle mass estimate and the kth coupling parameter estimate includes: obtaining a first rate of change based on a first damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value, wherein the first rate of change characterizes the update rate of the (k-1)th vehicle mass estimate over time; obtaining a second rate of change based on a second damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value, wherein the second rate of change characterizes the update rate of the (k-1)th coupling parameter estimate over time; and updating the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate based on the first rate of change and the second rate of change to obtain the kth vehicle mass estimate and the kth coupling parameter estimate.

[0112] Construct a two-parameter adaptive function to solve for the first and second rates of change.

[0113] In one embodiment, the first rate of change As shown in formula (5):

[0114] (5).

[0115] in, The first adaptive learning gain parameter represents the speed at which the estimated value is updated. The norm representing the value of the (k-1)th excitation parameter, It represents minimal positive numbers, prevents denominators of 0, and avoids low-excitation singularities. Characterizes the estimation error of the (k-1)th model.

[0116] The first rate of change is the time derivative of the estimated mass of the (k-1)th vehicle.

[0117] In one embodiment, the second rate of change As shown in formula (6):

[0118] (6).

[0119] in, Characterizes the second adaptive learning gain parameter.

[0120] The second rate of change is the time derivative of the (k-1)th coupling parameter estimate.

[0121] The mass estimate of vehicle k-1 is updated based on the first rate of change to obtain the mass estimate of vehicle k.

[0122] The estimated value of the (k-1)th coupling parameter is updated according to the second rate of change to obtain the estimated value of the kth coupling parameter.

[0123] According to an embodiment of this application, updating the estimated value of the (k-1)th vehicle mass and the estimated value of the (k-1)th coupling parameter based on a first rate of change and a second rate of change to obtain the estimated value of the kth vehicle mass and the estimated value of the kth coupling parameter includes: obtaining the estimated value of the kth vehicle mass based on the time interval between the kth time and the (k-1)th time, the first rate of change, and the estimated value of the (k-1)th vehicle mass; and obtaining the estimated value of the kth coupling parameter based on the time interval between the kth time and the (k-1)th time, the second rate of change, and the estimated value of the (k-1)th coupling parameter.

[0124] In one embodiment, the estimated mass of the kth vehicle As shown in formula (7):

[0125] (7).

[0126] in, The time interval between time k and time (k-1) is the sampling period interval.

[0127] Forward Euler discretization approximates the average trend of parameter change between adjacent sampling times using the rate of change at the current time.

[0128] In the recursive process of vehicle mass estimation, forward Euler discretization is used to multiply the first rate of change at time k-1 by the time interval between time k and time k-1 to obtain the approximate increment of vehicle mass estimation within this time interval. This increment is then superimposed on the vehicle mass estimation at time k-1 to recursively obtain the vehicle mass estimation at time k. This transforms the adaptive law in the continuous time domain into an iterative update form in the discrete time domain, enabling the algorithm to gradually correct the vehicle mass estimation at a fixed sampling period in the onboard embedded controller.

[0129] In the recursive process of estimating the coupling parameters, forward Euler discretization is also used. The second rate of change at time k-1 is multiplied by the time interval to obtain the approximate increment of the coupling parameter estimate within the sampling period. This increment is then added to the estimated value of the coupling parameter at time k-1, and the estimated value of the coupling parameter at time k is obtained recursively. In this way, the two-parameter adaptive update law is realized within the discrete time step, ensuring that the coupling parameters can gradually converge to the true value with the sampling period, which is compatible with the periodic operation mechanism of the vehicle controller.

[0130] The two-parameter adaptive update function designed in this application for calculating the first and second rates of change can stably and reliably drive the vehicle mass estimate and the coupled parameter estimate towards the true value. The verification process is as follows. The vehicle mass estimation error is selected... and coupling parameter estimation error ,in, For the true value of vehicle mass, Given the true values ​​of the coupling parameters, construct the Lyapunov positive definite energy function. ,in, >0、 Since V > 0, V ≥ 0 and is positive definite, which can be used as an energy scale to measure the convergence process of the estimate. Differentiating V along the system trajectory and substituting the two-parameter adaptive function, we can obtain... Among them, the normalized error term The constant negative effect, with the first and second damping correction values ​​providing negative directional force when estimating deviations from the nominal value, together ensures... Negative definite or negative semi-definite. According to Lyapunov's stability theorem, the parameter estimation error... , The function is uniformly bounded and asymptotically converges to zero under continuous excitation; this proves that the two-parameter adaptive function can stably and reliably drive the vehicle mass estimate and the rolling resistance coupling parameter estimate to converge to the true value.

[0131] According to an embodiment of this application, the method further includes: constraining the first rate of change, the second rate of change, the estimated value of the kth vehicle mass, and the estimated value of the kth coupling parameter based on a preset physical constraint range, wherein the preset physical constraint range includes a rate of change constraint range, a vehicle mass constraint range, and a coupling parameter constraint range.

[0132] The preset physical constraint range represents the set of physical boundaries that the algorithm needs to follow during the estimation update process. It is used to forcibly limit the vehicle mass estimate, coupling parameter estimate and their rate of change within the range that is engineering-realizable and physically interpretable, and prevent non-physical estimates from entering the vehicle longitudinal controller.

[0133] The rate of change constraint range characterizes the maximum allowable change in the estimated vehicle mass and the estimated coupling parameters within a single sampling period, used to prevent parameter abrupt changes caused by operating condition switching or signal jitter. The upper limit is determined based on the physical limits of the actual vehicle mass change rate and the rolling resistance coefficient change rate, as well as the sensor noise level calibration. For example, the maximum change in vehicle mass per step is set to no more than 10~20 kg, and the maximum change in coupling parameters per step is set to no more than 0.3~0.5 kg. If the rate of change exceeds this range, it is truncated according to the boundary value.

[0134] The vehicle mass constraint range represents the upper and lower limits of the vehicle mass estimate. The lower limit is the vehicle's unloaded curb weight (e.g., 1000 kg), and the upper limit is the vehicle's maximum permissible gross weight (e.g., 3000 kg or maximum design gross weight). If the updated vehicle mass estimate of the kth vehicle is lower than the lower limit or higher than the upper limit, then the constraint is applied to the corresponding boundary value.

[0135] The range of coupling parameter constraints represents the upper and lower limits of the estimated value of the product of vehicle mass and rolling resistance coefficient. The lower limit is determined by the product of the minimum preset vehicle mass and the minimum rolling resistance coefficient, and the upper limit is determined by the product of the maximum preset vehicle mass and the maximum rolling resistance coefficient. If the estimated value of the k-th coupling parameter exceeds the limit after updating, it is also immediately constrained to the boundary value.

[0136] According to the embodiments of this application, a joint constraint is adopted on the first rate of change, the second rate of change, the estimated value of the kth vehicle mass, and the estimated value of the kth coupling parameter based on a preset physical constraint range. This ensures that the parameter estimation update process is always limited to a physically achievable safe range. Even in extreme cases such as sensor failure, missed detection of braking conditions, or prolonged low excitation, the algorithm will not output estimated values ​​of negative vehicle mass or negative coupling parameters. This ensures that the parameters received by the vehicle longitudinal controller always have physical meaning, fundamentally improving the safety and reliability of the parameter estimation results in the vehicle embedded system.

[0137] Figure 4 A structural block diagram of a vehicle driving control device according to an embodiment of this application is shown.

[0138] like Figure 4 As shown, the vehicle driving control device 400 of this embodiment includes a control module 410, which includes an acquisition submodule 401, a first estimation submodule 402, a second estimation submodule 403, an update submodule 404, a generation submodule 405, and a determination submodule 406.

[0139] The acquisition submodule 401 is used to acquire the target vehicle's (k-1)th vehicle running state, (k-1)th road slope parameter value, (k-1)th excitation parameter value, (k-1)th vehicle mass estimate value, and (k-1)th coupling parameter estimate value at time (k-1). The (k-1)th excitation parameter value represents the comprehensive excitation information of the vehicle's longitudinal motion state and road slope. The (k-1)th coupling parameter estimate value represents the combined effect of vehicle mass and rolling resistance coefficient. The (k-1)th vehicle mass estimate value represents the total mass estimate result of the vehicle during the driving process at stage (k-1), where k is an integer greater than 1.

[0140] The first estimation submodule 402 is used to process the vehicle operating state, road slope parameter value, vehicle mass estimate and coupling parameter estimate using the estimation model when the vehicle operating state does not meet the preset braking suppression condition or the excitation parameter value is greater than the preset excitation intensity threshold, so as to obtain the longitudinal force estimate of the wheel.

[0141] The second estimation submodule 403 is used to obtain the model estimation error of the (k-1)th wheel based on the difference between the estimated value of the longitudinal force of the (k-1)th wheel and the actual value of the longitudinal force of the (k-1)th wheel. The actual value of the longitudinal force of the (k-1)th wheel is determined based on the operating state of the (k-1)th vehicle.

[0142] The update submodule 404 is used to obtain the estimated value of the kth vehicle mass and the estimated value of the kth coupling parameter based on the (k-1)th damping correction value, the (k-1)th model estimation error, the (k-1)th excitation parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value.

[0143] The generation submodule 405 is used to obtain the k-th rolling resistance coefficient based on the ratio between the estimated value of the k-th coupling parameter and the estimated value of the k-th vehicle mass.

[0144] The determination submodule 406 is used to determine the control strategy for the k-th vehicle based on the estimated mass of the k-th vehicle and the rolling resistance coefficient of the k-th vehicle.

[0145] Optionally, any multiple modules among the control module 410, acquisition submodule 401, first estimation submodule 402, second estimation submodule 403, update submodule 404, generation submodule 405, and determination submodule 406 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. Optionally, at least one of the control module 410, acquisition submodule 401, first estimation submodule 402, second estimation submodule 403, update submodule 404, generation submodule 405, and determination submodule 406 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), programmable logic array (PLA), system-on-a-chip, system-on-a-substrate, system-on-package, application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the control module 410, the acquisition submodule 401, the first estimation submodule 402, the second estimation submodule 403, the update submodule 404, the generation submodule 405, and the determination submodule 406 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0146] Figure 5 A block diagram of an electronic device suitable for implementing a method of controlling vehicle driving according to an embodiment of this application is shown.

[0147] Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0148] like Figure 5As shown, an electronic device 500 according to an embodiment of this application includes a processor 501, which can perform various appropriate actions and processes according to a program stored in ROM 502 (Read-Only Memory) or a program loaded from storage portion 508 into RAM 503 (Random Access Memory). The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0149] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 502 and / or RAM 503. It should be noted that programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in one or more memories.

[0150] In embodiments of this application, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0151] The embodiments of this application, and the method flow according to the embodiments of this application, can be implemented as a computer software program. For example, an embodiment of this application includes a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of the embodiments of this application. In the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0152] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the vehicle control driving method according to the embodiments of this application.

[0153] In embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0154] For example, in embodiments of this application, a computer-readable storage medium may include the ROM 502 and / or RAM 503 described above and / or one or more memories other than ROM 502 and RAM 503.

[0155] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this application. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the vehicle driving control method provided in the embodiments of this application.

[0156] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this application embodiment. In the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0157] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0158] In embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0159] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations are not explicitly described in this application. In particular, without departing from the spirit and teachings of this application, the features described in the various embodiments of this application can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of this application.

[0160] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, all of which should fall within the scope of this application.

Claims

1. A method for controlling vehicle movement, characterized in that, The method includes: The vehicle is controlled according to a multi-stage vehicle control strategy. The k-th control strategy in the k-th stage is determined based on the following operations: The following parameters are obtained for the target vehicle at time k-1: the (k-1)th vehicle running state, the (k-1)th road slope parameter value, the (k-1)th excitation parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value. The (k-1)th excitation parameter value represents the comprehensive excitation information of the vehicle's longitudinal motion state and the road slope. The (k-1)th coupling parameter estimate value represents the combined effect of the vehicle mass and the rolling resistance coefficient. The (k-1)th vehicle mass estimate value represents the total mass estimate result of the vehicle during the driving process at stage k-1. k is an integer greater than 1. If the (k-1)th vehicle's operating state does not meet the preset braking suppression condition or the (k-1)th excitation parameter value is greater than the preset excitation intensity threshold, the estimation model is used to process the (k-1)th vehicle's operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle's mass estimate value, and the (k-1)th coupling parameter estimate value to obtain the (k-1)th wheel longitudinal force estimate value. The estimation error of the model is obtained by the difference between the estimated value of the longitudinal force of the (k-1)th wheel and the actual value of the longitudinal force of the (k-1)th wheel. The actual value of the longitudinal force of the (k-1)th wheel is determined based on the operating state of the (k-1)th vehicle. Based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value, the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate are updated to obtain the (k)th vehicle mass estimate and the (k)th coupling parameter estimate. The rolling resistance coefficient is obtained based on the ratio between the estimated value of the k-th coupling parameter and the estimated value of the k-th vehicle mass; and The control strategy for the k-th vehicle is determined based on the estimated mass of the k-th vehicle and the rolling resistance coefficient of the k-th vehicle.

2. The method according to claim 1, characterized in that, The (k-1)th damping correction value is determined based on the following operation: Based on the first damping correction coefficient, the difference between the initial vehicle mass and the estimated value of the (k-1)th vehicle mass is corrected to obtain the first damping correction value. The initial coupling parameters are obtained by multiplying the initial vehicle mass and the initial rolling resistance coefficient. The difference between the initial coupling parameter and the estimated value of the (k-1)th coupling parameter is corrected according to the second damping correction coefficient to obtain the second damping correction value. Based on the first damping correction value and the second damping correction value, the (k-1)th damping correction value is obtained.

3. The method according to claim 2, characterized in that, The step of updating the (k-1)th vehicle mass estimate and the (k-1)th coupling parameter estimate based on the (k-1)th damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value to obtain the kth vehicle mass estimate and the kth coupling parameter estimate includes: Based on the first damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value, a first rate of change is obtained, which represents the update rate of the (k-1)th vehicle mass estimate over time. The second rate of change is obtained based on the second damping correction value, the (k-1)th model estimation error, and the (k-1)th excitation parameter value. The second rate of change characterizes the update rate of the (k-1)th coupling parameter estimate over time. Based on the first rate of change and the second rate of change, the estimated value of the (k-1)th vehicle mass and the estimated value of the (k-1)th coupling parameter are updated to obtain the estimated value of the kth vehicle mass and the estimated value of the kth coupling parameter.

4. The method according to claim 3, characterized in that, The step of updating the estimated value of the (k-1)th vehicle mass and the estimated value of the (k-1)th coupling parameter based on the first rate of change and the second rate of change to obtain the estimated value of the kth vehicle mass and the estimated value of the kth coupling parameter includes: The estimated mass of vehicle k is obtained based on the time interval between time k and time k-1, the first rate of change, and the estimated mass of vehicle k-1. The estimated value of the k-th coupling parameter is obtained based on the time interval between time k and time k-1, the second rate of change, and the estimated value of the (k-1)-th coupling parameter.

5. The method according to claim 1, characterized in that, The (k-1)th vehicle operating state includes the (k-1)th motor torque, the (k-1)th transmission efficiency, and the (k-1)th wheel braking pressure; The preset braking suppression condition includes a first suppression condition or a second suppression condition. The first suppression condition is that the motor torque is less than a preset torque threshold, and the second suppression condition is that the wheel braking pressure is greater than a preset pressure threshold. The true value of the longitudinal force of the (k-1)th wheel is determined based on the torque of the (k-1)th motor and the transmission efficiency of the (k-1)th transmission.

6. The method according to claim 1, characterized in that, The operating state of the (k-1)th vehicle also includes the longitudinal acceleration of the (k-1)th vehicle; The step of using an estimation model to process the (k-1)th vehicle operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value to obtain the (k-1)th wheel longitudinal force estimate value includes: The longitudinal composite acceleration is obtained based on the longitudinal acceleration of the (k-1)th vehicle, the gravitational acceleration, and the sine value determined based on the (k-1)th road slope parameter value. The vertical gravity component is obtained based on the gravitational acceleration and the cosine value determined based on the (k-1)th road slope parameter value; The longitudinal force estimate of the (k-1)th wheel is obtained by multiplying the longitudinal composite acceleration with the estimated mass of the (k-1)th vehicle and the vertical gravity component with the estimated coupling parameter of the (k-1)th vehicle.

7. The method according to claim 6, characterized in that, The (k-1)th excitation parameter value is determined based on the longitudinal composite acceleration and the vertical gravity component.

8. The method according to claim 3, characterized in that, The method further includes: Based on a preset physical constraint range, constraints are applied to the first rate of change, the second rate of change, the estimated value of the kth vehicle mass, and the estimated value of the kth coupling parameter. The preset physical constraint range includes a rate of change constraint range, a vehicle mass constraint range, and a coupling parameter constraint range.

9. A device for controlling vehicle movement, characterized in that, The device includes: The control module is used to control vehicle driving according to a vehicle control strategy in multiple stages. The control module includes an acquisition submodule, a first estimation submodule, a second estimation submodule, an update submodule, a generation submodule, and a determination submodule. The acquisition submodule is used to acquire the target vehicle's (k-1)th vehicle running state, (k-1)th road slope parameter value, (k-1)th excitation parameter value, (k-1)th vehicle mass estimate value, and (k-1)th coupling parameter estimate value at time (k-1). The (k-1)th excitation parameter value represents the comprehensive excitation information of the vehicle's longitudinal motion state and road slope. The (k-1)th coupling parameter estimate value represents the combined effect of vehicle mass and rolling resistance coefficient. The (k-1)th vehicle mass estimate value represents the total mass estimate result of the vehicle during the driving process at stage (k-1), where k is an integer greater than 1. The first estimation submodule is used to process the (k-1)th vehicle operating state, the (k-1)th road slope parameter value, the (k-1)th vehicle mass estimate value, and the (k-1)th coupling parameter estimate value using an estimation model when the (k-1)th vehicle operating state does not meet the preset braking suppression condition or the (k-1)th excitation parameter value is greater than the preset excitation intensity threshold, so as to obtain the (k-1)th wheel longitudinal force estimate value. The second estimation submodule is used to obtain the estimation error of the (k-1)th model based on the difference between the estimated value of the longitudinal force of the (k-1)th wheel and the actual value of the longitudinal force of the (k-1)th wheel, wherein the actual value of the longitudinal force of the (k-1)th wheel is determined based on the operating state of the (k-1)th vehicle. The update submodule is used to obtain the estimated value of the kth vehicle mass and the estimated value of the kth coupling parameter based on the (k-1)th damping correction value, the (k-1)th model estimation error, the (k-1)th excitation parameter value, the (k-1)th vehicle mass estimated value, and the (k-1)th coupling parameter estimated value. A generation submodule is used to obtain the kth rolling resistance coefficient based on the ratio between the estimated value of the kth coupling parameter and the estimated value of the kth vehicle mass; A determination submodule is used to determine the control strategy for the k-th vehicle based on the estimated mass value of the k-th vehicle and the rolling resistance coefficient of the k-th vehicle.

10. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

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