CN-122009150-A - Vehicle control method, device, electronic apparatus, storage medium, and program product

CN122009150ACN 122009150 ACN122009150 ACN 122009150ACN-122009150-A

Abstract

The present invention relates to the field of vehicle control technology, and in particular, to a vehicle control method, apparatus, electronic device, storage medium, and program product. The method comprises the steps of predicting a first predicted centroid side deflection angle corresponding to the current moment of a target vehicle based on a first preset method, predicting a second predicted centroid side deflection angle corresponding to the current moment of the target vehicle based on a second preset method, fusing the first predicted centroid side deflection angle and the second predicted centroid side deflection angle to obtain a final predicted centroid side deflection angle corresponding to the current moment of the target vehicle, calculating a target additional yaw moment corresponding to the current moment of the target vehicle based on the final predicted centroid side deflection angle, and controlling the target vehicle based on the target additional yaw moment. The side slip, oversteering or understeering of the vehicle can be quickly restrained, and the lateral stability, the driving safety and the control smoothness of the vehicle under complex working conditions are obviously improved. Therefore, the requirements of high accuracy and high robustness of vehicle lateral stability control under complex roads and limit conditions are met.

Inventors

Chen Diyin
CHENG JIAN
YU HUILI
ZENG QINGQIANG

Assignees

重庆长安汽车股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260410

Claims (10)

1. A vehicle control method, characterized in that the method comprises: predicting a first predicted centroid slip angle corresponding to the current moment of the target vehicle based on a first preset method; Predicting a second predicted centroid slip angle corresponding to the current moment of the target vehicle based on a second preset method; Fusing the first predicted centroid slip angle and the second predicted centroid slip angle to obtain a final predicted centroid slip angle corresponding to the current moment of the target vehicle; Calculating a target additional yaw moment corresponding to the current moment of the target vehicle based on the final predicted centroid side deviation angle; the target vehicle is controlled based on the target additional yaw moment.
2. The method of claim 1, wherein predicting a first predicted centroid slip angle corresponding to a current time of the target vehicle based on a first preset method comprises; establishing an initial state equation based on a two-degree-of-freedom vehicle dynamics model by taking a centroid slip angle and a yaw rate corresponding to the target vehicle as state quantities, wherein the centroid slip angle is a core quantity to be estimated; taking the lateral acceleration and the yaw rate corresponding to the target vehicle as observables, and constructing an initial observation equation based on a two-degree-of-freedom vehicle dynamics model; The first predicted centroid slip angle is calculated based on the initial state equation and the initial observation equation.
3. The method of claim 2, wherein the calculating the first predicted centroid slip angle based on the initial state equation and the initial observation equation comprises: Performing first-order Taylor expansion on the initial state equation and the initial observation equation based on a forward Euler method to obtain a discretized state equation and a discretized observation equation: initializing the discretization state equation and the discretization observation equation; And predicting to obtain the first predicted centroid slip angle based on the initialized discretized state equation and the discretized observation equation.
4. The method of claim 3, wherein predicting the first predicted centroid slip angle based on the initialized discretized state equation and discretized observation equation comprises: Based on the initialized discretization state equation and discretization observation equation, predicting to obtain a first initial centroid slip angle and a priori error covariance matrix corresponding to the current moment; Calculating Kalman gain based on the prior error covariance matrix, the observation equation jacobian matrix corresponding to the observation equation and the noise covariance; and correcting the first initial centroid slip angle based on the Kalman gain to obtain the first predicted centroid slip angle.
5. The method of claim 1, wherein predicting a second predicted centroid slip angle corresponding to the current time of the target vehicle based on a second preset method comprises: Acquiring a kinematic equation corresponding to the target vehicle; deriving a kinematic differential equation of a centroid slip angle corresponding to the target vehicle based on the kinematic equation; discretizing the kinematic differential equation by adopting a forward Euler method to obtain a discrete kinematic equation; and calculating the second predicted centroid slip angle based on the discrete kinematic equation.
6. The method of claim 1, wherein the fusing the first predicted centroid slip angle and the second predicted centroid slip angle to obtain a final predicted centroid slip angle corresponding to the current time of the target vehicle comprises: Acquiring the corresponding lateral acceleration of the target vehicle; according to the lateral acceleration, determining weights corresponding to the first predicted centroid slip angle and the second predicted centroid slip angle respectively; And fusing the first predicted centroid slip angle and the second predicted centroid slip angle based on weights respectively corresponding to the first predicted centroid slip angle and the second predicted centroid slip angle to obtain the final predicted centroid slip angle corresponding to the current moment of the target vehicle.
7. The method of claim 1, wherein calculating a target additional yaw moment corresponding to the current time of the target vehicle based on the final predicted centroid slip angle comprises: acquiring a target centroid slip angle and a target yaw rate at the current moment corresponding to the target vehicle; acquiring the actual yaw rate of the target vehicle at the current moment; calculating a centroid slip angle difference between the final predicted centroid slip angle and the target centroid slip angle, and a yaw rate difference between the actual yaw rate and the target yaw rate; Constructing an initial sliding mode surface corresponding to a preset sliding mode control algorithm based on the centroid side deviation angle difference value and the yaw rate difference value; calculating an initial index approach rate based on the initial sliding mode surface; acquiring a preset sliding die surface width corresponding to the initial sliding die surface; correcting the initial index approach rate based on the preset sliding mode surface width to obtain a target index approach rate; and calculating the target additional yaw moment corresponding to the current moment of the target vehicle based on the target index approach rate.
8. The method of claim 7, wherein the calculating the target additional yaw moment corresponding to the current time of the target vehicle based on the target index approach rate comprises: The basic expression corresponding to the target additional yaw moment is calculated based on the target index approach rate, wherein the basic expression comprises unknown real parameters, and the unknown real parameters represent functions of tire cornering stiffness and center-of-mass-to-axle distance; calculating a parameter self-adaptive law corresponding to the unknown real parameter; Substituting the parameter self-adaptive law into the basic expression corresponding to the target additional yaw moment, and calculating to obtain the target additional yaw moment.
9. The method of claim 8, wherein the calculating the parameter adaptation law corresponding to the unknown real parameter comprises: acquiring a parameter estimation value corresponding to the unknown real parameter; Calculating to obtain a parameter estimation error based on the unknown real parameter and the parameter estimation value; constructing a parameter self-adaptive law corresponding to the parameter estimation error; constructing a Lyapunov function based on the initial sliding mode surface and the parameter estimation error; verifying whether the parameter adaptive law meets the stability requirement of a target vehicle based on the Lyapunov function; And if the parameter self-adaptive law meets the stability requirement of the target vehicle, obtaining the parameter self-adaptive law corresponding to the unknown real parameter.
10. A vehicle control apparatus, characterized in that the apparatus comprises: the first prediction module is used for predicting a first predicted centroid slip angle corresponding to the current moment of the target vehicle based on a first preset method; The second prediction module is used for predicting a second predicted centroid slip angle corresponding to the current moment of the target vehicle based on a second preset method; The fusion module is used for fusing the first predicted centroid side deflection angle and the second predicted centroid side deflection angle to obtain a final predicted centroid side deflection angle corresponding to the current moment of the target vehicle; the calculation module is used for calculating a target additional yaw moment corresponding to the current moment of the target vehicle based on the final predicted centroid slip angle; and a control module for controlling the target vehicle based on the target additional yaw moment.

Description

Vehicle control method, device, electronic apparatus, storage medium, and program product Technical Field The present invention relates to the field of vehicle control technology, and in particular, to a vehicle control method, apparatus, electronic device, storage medium, and program product. Background With the rapid development of the automobile industry and the increasing complexity of road environment, dangerous phenomena such as oversteer, sideslip and the like are extremely easy to occur to the vehicle under the extreme working conditions such as emergency avoidance and the like, and the life safety of drivers and passengers is seriously threatened, so that the lateral stability control of the vehicle has become the research focus and the core direction in the active safety field of the automobile. Among various vehicle body state variables representing the lateral stability of a vehicle, the yaw rate and the centroid slip angle are the most critical two parameters, and can accurately reflect the lateral motion gesture and the steady state of the vehicle, so that core input is provided for the design of a control algorithm, wherein the yaw rate can be directly acquired through an on-board Inertial Measurement Unit (IMU) sensor, the centroid slip angle is difficult to directly measure due to the special physical characteristics, a special estimation algorithm is required to be designed, and the estimation performance of the yaw rate and the centroid slip angle directly determine the lateral stability control effect. At present, in the centroid slip angle estimation algorithm, the scheme based on the kinematic model and the dynamic model is most widely applied, but has obvious limitations, and is difficult to consider the accuracy and the robustness under all working conditions. Therefore, the requirements of high accuracy and high robustness of the lateral stability control of the vehicle on complex roads and under extreme conditions cannot be met, and corresponding solutions are needed to be proposed. Disclosure of Invention The invention provides a vehicle control method, a device, electronic equipment, a storage medium and a program product, which are used for solving the problem that the prior art cannot meet the requirements of high accuracy and high robustness of vehicle lateral stability control under complex roads and extreme working conditions. The invention provides a vehicle control method, which comprises the steps of predicting a first predicted centroid side deflection angle corresponding to the current moment of a target vehicle based on a first preset method, predicting a second predicted centroid side deflection angle corresponding to the current moment of the target vehicle based on a second preset method, fusing the first predicted centroid side deflection angle and the second predicted centroid side deflection angle to obtain a final predicted centroid side deflection angle corresponding to the current moment of the target vehicle, calculating a target additional yaw moment corresponding to the current moment of the target vehicle based on the final predicted centroid side deflection angle, and controlling the target vehicle based on the target additional yaw moment. According to the vehicle control method provided by the embodiment of the application, the first predicted centroid side deflection angle corresponding to the current moment of the target vehicle is predicted based on the first preset method. And predicting a second predicted centroid slip angle corresponding to the current moment of the target vehicle based on a second preset method. And fusing the first predicted centroid side deflection angle and the second predicted centroid side deflection angle to obtain a final predicted centroid side deflection angle corresponding to the current moment of the target vehicle. The advantages of high accuracy of linear working conditions and high robustness of nonlinear working conditions are complemented, so that the centroid slip angle can be stably and accurately output in the whole working condition range, and the overall reliability of state estimation is improved. Based on the final predicted centroid side deflection angle, calculating a target additional yaw moment corresponding to the current moment of the target vehicle, and taking a more reliable vehicle state as input, so that the additional yaw moment is calculated more accurately and is more suitable for the real stable requirement of the vehicle, and excessive or insufficient control caused by estimated deviation is avoided. The target vehicle is controlled based on the target additional yaw moment. The side slip, oversteering or understeering of the vehicle can be quickly restrained, and the lateral stability, the driving safety and the control smoothness of the vehicle under complex working conditions are obviously improved. Therefore, the requirements of high accuracy and high robustness of vehicle lateral stabili