CN-115503709-B - Vehicle speed control method, device, medium, equipment and vehicle

CN115503709BCN 115503709 BCN115503709 BCN 115503709BCN-115503709-B

Abstract

The present disclosure relates to a vehicle speed control method, device, medium, equipment and vehicle, the method comprising obtaining real-time status data of the vehicle; the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information, wherein the road condition information comprises current road gradient, road attachment coefficient and road rolling resistance coefficient, and the target torque is determined based on the real-time state data and is used for enabling the vehicle to be adjusted from the current vehicle speed to the expected vehicle speed. According to the technical scheme, the real-time estimated quality, the current speed, the expected speed and road condition information of the vehicle are combined, the target torque corresponding to the speed control is determined according to real-time state data such as the current road gradient, the road attachment coefficient, the road rolling resistance coefficient and the like, the speed of the vehicle can be timely and accurately controlled according to road conditions of different vehicle conditions, namely, the response speed is high, and the following performance is good.

Inventors

BAI YUHE
KUANG QI
ZHANG LIHONG
MENG MEIRONG
ZHANG GAOXIANG

Assignees

驭势（上海）汽车科技有限公司

Dates

Publication Date: 20260512
Application Date: 20221101

Claims (18)

1. A vehicle speed control method of an autonomous vehicle, comprising: acquiring real-time state data of a vehicle, wherein the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information, and the road condition information comprises current road gradient, road attachment coefficient and road rolling resistance coefficient; Determining a target torque based on the real-time status data; The target torque is used for adjusting the vehicle from the current vehicle speed to the expected vehicle speed; Determining a target torque based on the real-time status data, comprising: based on the current vehicle speed and the expected vehicle speed, generating an acceleration and deceleration control parameter by utilizing vehicle speed difference processing and PID control; acquiring the target torque based on the acceleration and deceleration control parameter and the real-time state data; wherein generating an acceleration/deceleration control parameter based on the current vehicle speed and the desired vehicle speed by using a vehicle speed difference process and PID control, comprises: Determining a real-time vehicle speed difference based on the current vehicle speed and the expected vehicle speed and by combining a vehicle running mode and a vehicle speed difference threshold; When the integral zero clearing condition of the PID control is met, a corresponding integral zero clearing instruction is generated; based on at least the real-time vehicle speed difference, performing PID control parameter adjustment and optimization to obtain a target proportion parameter, a target integral parameter and a target differential parameter; Generating the acceleration and deceleration control parameter by using a PID control algorithm based on at least the real-time vehicle speed difference, the integral zero clearing instruction, the target proportional parameter, the target integral parameter and the target derivative parameter; wherein said determining a real-time vehicle speed difference based on said current vehicle speed and said desired vehicle speed in combination with a vehicle mode of operation and a vehicle speed difference threshold comprises: when the vehicle running mode is a speed control non-ready state in a rotating speed running mode, a non-automatic driving running mode or an automatic driving mode, the real-time vehicle speed difference is 0; when the vehicle is in a speed control ready state in an automatic driving mode, if the current speed direction of the vehicle is the same as the direction of the expected speed, the real-time speed difference is equal to the expected speed minus the current speed, and if the current speed direction of the vehicle is opposite to the direction of the expected speed, the real-time speed difference is equal to the expected speed plus the current speed, and the real-time speed difference is smaller than or equal to a speed difference threshold value.
2. The method of claim 1, wherein obtaining the desired vehicle speed comprises: Acquiring a desired vehicle speed to be processed, which is output by the domain controller based on the current running requirement; And limiting and filtering the expected vehicle speed to be processed to obtain the expected vehicle speed.
3. The method of claim 1, wherein obtaining the current vehicle speed comprises: acquiring the rotating speed of a motor, the radius of wheels and the speed ratio of a vehicle; acquiring a current vehicle speed to be processed based on the motor rotation speed, the wheel radius and the vehicle speed ratio; and filtering the current vehicle speed to be processed to obtain the current vehicle speed.
4. The method of claim 1, wherein obtaining the real-time estimated quality comprises: Acquiring running state data of a vehicle, wherein the running state data comprises a current gear command, a current road gradient, a current acceleration, the current vehicle speed and a current driving torque of a motor; based on the driving state data, continuously acquiring a road rolling resistance coefficient when judging that the quality estimation triggering condition is met; The real-time estimated mass is determined based on the current vehicle speed, the current acceleration, the current road gradient, the road rolling resistance coefficient, and the current driving torque.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises, The method comprises the steps of acquiring the current road gradient, wherein the current road gradient comprises the step of acquiring the current road gradient determined by a domain controller, wherein the domain controller determines the current road gradient of the current position of a vehicle based on the current position of the vehicle and prior road gradient map information, and/or the step of determining the current road gradient based on gradient information acquired by a gradient sensor loaded by the vehicle; the road adhesion coefficient and the road rolling resistance coefficient are obtained, wherein the road adhesion coefficient and the road rolling resistance coefficient are determined by a domain controller, and the domain controller is used for identifying the material of the current road and the degree of dryness and wetness of the road surface based on an image acquisition sensor and matching the corresponding road adhesion coefficient and road rolling resistance coefficient.
6. The method according to any one of claims 1-5, wherein obtaining the target torque based on the acceleration and deceleration control parameter and the real-time state data includes: Acquiring a motor required torque by utilizing a vehicle dynamics equation based on the acceleration and deceleration control parameter, the expected vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient and the road rolling resistance coefficient; And limiting and filtering the motor required torque to obtain the target torque.
7. The method of claim 6, wherein performing parameter adjustment optimization of PID control based on at least the real-time vehicle speed difference comprises: And performing parameter adjustment optimization of PID control based on four factors, wherein the four factors comprise an expected speed and real-time speed difference, a real-time estimated mass, a current road gradient and a road attachment coefficient.
8. The method of claim 7, wherein prior to the parameter tuning optimization for PID control, the method further comprises: and when the expected vehicle speed and the actual vehicle speed meet the correction conditions, correcting the proportion parameters in the PID control.
9. The method of claim 8, wherein the correction condition comprises: The expected speed is greater than the speed threshold value, and The current vehicle speed is smaller than the expected vehicle speed, and the accumulated time length below the vehicle speed threshold value is longer than the preset time length.
10. The method of claim 7, wherein the integral clear condition comprises at least one of: condition 1, when the vehicle is in automatic mode and manual mode switching; The condition 2 is that the real-time vehicle speed difference is larger than zero and the acceleration and deceleration control parameter is smaller than zero, or the real-time vehicle speed difference is smaller than zero and the acceleration and deceleration control parameter is larger than zero; Condition 3, brake pressure command occurs; 4, gear command switching; 5, switching the working mode of the motor; Condition 6, vehicle readiness state change.
11. The method of claim 7, wherein the parameter adjustment optimization comprises a proportional parameter adjustment optimization, an integral parameter adjustment optimization, and a differential parameter adjustment optimization; the proportional parameter adjustment optimization comprises the following steps: the method comprises the steps of adjusting and optimizing based on a proportion parameter of a desired vehicle speed and a real-time vehicle speed difference, wherein the proportion parameter is in direct proportion to the desired vehicle speed, and the proportion parameter is in direct proportion to the real-time vehicle speed difference; The method comprises the steps of adjusting and optimizing proportional parameters based on real-time estimated quality, wherein the proportional parameters are in direct proportion to the real-time estimated quality; adjusting and optimizing based on a proportion parameter of the current road gradient, wherein the proportion parameter is in direct proportion to the current road gradient; and adjusting and optimizing the proportion parameter based on the road adhesion coefficient, wherein the proportion parameter is inversely proportional to the road adhesion coefficient; the integral parameter adjustment optimization comprises the following steps: adjusting and optimizing based on integral parameters of the expected vehicle speed and the real-time vehicle speed difference; adjusting and optimizing the integral parameters based on the real-time estimated quality; adjusting an optimization based on an integral parameter of a current road grade; Adjusting and optimizing the integral parameters based on the road attachment coefficients; the differential parameter adjustment optimization includes: adjusting and optimizing based on differential parameters of the expected vehicle speed and the real-time vehicle speed difference; adjusting and optimizing based on differential parameters of real-time estimated quality; adjusting the optimization based on the derivative parameter of the current road grade; and adjusting the optimization based on the differential parameters of the road attachment coefficient.
12. The method of claim 6, wherein said deriving motor demand torque using a vehicle dynamics equation comprises: The motor demand torque is calculated using the formula: Wherein T represents motor demand torque, ρ represents air density, A represents frontal area, C D represents air resistance coefficient, v represents current vehicle speed, f represents road rolling resistance coefficient, m represents real-time estimated mass, g represents gravity coefficient, i represents current road gradient, Represents the conversion coefficient of the rotating mass of the automobile after accounting the moment of inertia of the rotating mass, a represents the acceleration and deceleration control parameter, r represents the effective radius of the tyre of the vehicle, K represents the speed ratio of the vehicle, Representing the efficiency of the transmission mechanism.
13. The method of claim 6, wherein limiting and filtering the motor demand torque to obtain the target torque comprises: Limiting the motor required torque based on the maximum available torque of the vehicle, wherein the maximum available torque is determined based on the state of charge of a power battery in the vehicle, allowable charge and discharge power of the power battery, external characteristic torque of the motor, driving and feedback torque limitation of the motor and the fault condition of the whole vehicle; And filtering the limited motor required torque to obtain the target torque.
14. The method as recited in claim 1, further comprising: Transmitting the target torque to a controlled component, and adjusting a vehicle from the current vehicle speed to the desired vehicle speed based on the controlled component.
15. A vehicle speed control device for an autonomous vehicle, comprising: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring real-time state data of a vehicle, wherein the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information, and the road condition information comprises current road gradient, road attachment coefficient and road rolling resistance coefficient; A determining module for determining a target torque based on the real-time status data; The target torque is used for adjusting the vehicle from the current vehicle speed to the expected vehicle speed; the determining module is specifically configured to determine a real-time vehicle speed difference based on the current vehicle speed and the expected vehicle speed and in combination with a vehicle running mode and a vehicle speed difference threshold, generate a corresponding integral zero clearing instruction when an integral zero clearing condition of PID control is met, perform parameter adjustment optimization of PID control based on at least the real-time vehicle speed difference to obtain a target proportion parameter, a target integral parameter and a target differential parameter, generate an acceleration and deceleration control parameter based on at least the real-time vehicle speed difference, the integral zero clearing instruction, the target proportion parameter, the target integral parameter and the target differential parameter by means of a PID control algorithm, acquire the target torque based on the acceleration and deceleration control parameter and the real-time state data, and determine a real-time vehicle speed difference based on the current vehicle speed and the expected vehicle speed and in combination with a vehicle running mode and a vehicle speed difference threshold, wherein the real-time vehicle speed difference comprises 0 when the vehicle running mode is a speed control non-ready state in a rotating speed running mode, a non-automatic driving running mode or an automatic driving mode, and when the vehicle is in a speed control ready state in the automatic driving mode, and the current vehicle speed difference is equal to or smaller than the current vehicle speed difference is equal to or equal to the expected vehicle speed difference when the current vehicle speed difference is equal to the current speed difference and the current vehicle speed difference is equal to or smaller than the expected vehicle speed difference is equal to the current vehicle speed difference and the expected vehicle speed difference is equal to or equal to the real-time vehicle speed difference.
16. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for performing the steps of the method according to any one of claims 1-14.
17. An apparatus for a vehicle comprising a memory for storing processor-executable instructions, the processor for reading the executable instructions from the memory and executing the executable instructions to implement the steps of the method of any one of claims 1-14.
18. A vehicle comprising the vehicle apparatus of claim 17.

Description

Vehicle speed control method, device, medium, equipment and vehicle Technical Field The disclosure relates to the technical field of automatic driving vehicles, and in particular relates to a vehicle speed control method, a vehicle speed control device, a vehicle speed control medium, a vehicle speed control device and a vehicle. Background With the rapid development of the electric automobile industry and the improvement of the functional demands of users on electric automobiles, vehicle automatic driving systems are increasingly applied to pure electric vehicles for reducing the driving strength of drivers and improving the driving comfort, diversity and practicability. Currently, in a control method applied to an electric vehicle, the vehicle speed control method generally determines a target torque based on a current vehicle speed and an expected vehicle speed and other preset parameter values and realizes vehicle speed adjustment control, so that the vehicle speed control cannot be performed according to actual vehicle conditions, road conditions and other conditions, and the response speed of the vehicle speed control is slow and the following performance is poor. Disclosure of Invention In order to solve the technical problems described above, or at least partially solve the technical problems described above, the present disclosure provides a vehicle speed control method, apparatus, medium, device, and vehicle. The present disclosure provides a vehicle speed control method of an autonomous vehicle, comprising: acquiring real-time state data of a vehicle, wherein the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information, and the road condition information comprises current road gradient, road attachment coefficient and road rolling resistance coefficient; Determining a target torque based on the real-time status data; the target torque is used to adjust the vehicle from the current vehicle speed to the desired vehicle speed. Optionally, acquiring the desired vehicle speed includes: Acquiring a desired vehicle speed to be processed, which is output by the domain controller based on the current running requirement; And limiting and filtering the expected vehicle speed to be processed to obtain the expected vehicle speed. Optionally, acquiring the current vehicle speed includes: acquiring the rotating speed of a motor, the radius of wheels and the speed ratio of a vehicle; acquiring a current vehicle speed to be processed based on the motor rotation speed, the wheel radius and the vehicle speed ratio; and filtering the current vehicle speed to be processed to obtain the current vehicle speed. Optionally, obtaining the real-time estimated quality includes: Acquiring running state data of a vehicle, wherein the running state data comprises a current gear command, a current road gradient, a current acceleration, the current vehicle speed and a current driving torque of a motor; based on the driving state data, continuously acquiring a road rolling resistance coefficient when judging that the quality estimation triggering condition is met; The real-time estimated mass is determined based on the current vehicle speed, the current acceleration, the current road gradient, the road rolling resistance coefficient, and the current driving torque. Optionally, acquiring the current road gradient comprises acquiring the current road gradient determined by a domain controller, wherein the domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road gradient map information, and/or the domain controller determines the current road gradient based on gradient information acquired by a gradient sensor loaded by the vehicle; the road adhesion coefficient and the road rolling resistance coefficient are obtained, wherein the road adhesion coefficient and the road rolling resistance coefficient are determined by a domain controller, and the domain controller is used for identifying the material of the current road and the degree of dryness and wetness of the road surface based on an image acquisition sensor and matching the corresponding road adhesion coefficient and road rolling resistance coefficient. Optionally, determining the target torque based on the real-time status data includes: based on the current vehicle speed and the expected vehicle speed, generating an acceleration and deceleration control parameter by utilizing vehicle speed difference processing and PID control; Acquiring a motor required torque by utilizing a vehicle dynamics equation based on the acceleration and deceleration control parameter, the expected vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient and the road rolling resistance coefficient; And limiting and filtering the motor required torque to obtain the target torque. Opti