Vehicle speed control method, device, medium, apparatus, and vehicle

Abstract

The present disclosure relates to a vehicle speed control method, device, medium, equipment and vehicle, the method comprising: obtaining real-time state data of the vehicle; the real-time state data comprising real-time estimated mass, current vehicle speed, desired vehicle speed and road condition information; the road condition information comprising: current road slope, road adhesion coefficient and road rolling resistance coefficient; determining a target torque based on the real-time state data; the target torque being used to adjust the vehicle from the current vehicle speed to the desired vehicle speed. In the technical solution of the present disclosure, the target torque corresponding to the vehicle speed control is determined in combination with the real-time estimated mass, current vehicle speed, desired vehicle speed and road condition information, such as real-time state data such as current road slope, road adhesion coefficient and road rolling resistance coefficient, which can timely and accurately control the vehicle speed for different vehicle conditions and road conditions, i.e. the response speed is fast and the followability is good.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous vehicle technology, and in particular to a vehicle speed control method, device, medium, equipment, and vehicle. Background Technology

[0002] With the rapid development of the electric vehicle industry and the increasing demands of users for electric vehicle functions, autonomous driving systems are being increasingly applied to pure electric vehicles to reduce the driving intensity of drivers and improve driving comfort, versatility, and practicality.

[0003] Currently, in the control methods applied to electric vehicles, the vehicle speed control method usually determines the target torque and realizes vehicle speed adjustment control based on the current vehicle speed, the desired vehicle speed, and other pre-set parameter values. This results in the inability to control the vehicle speed according to the actual vehicle condition, road condition, and other conditions, leading to slow response speed and poor following performance of the vehicle speed control. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this disclosure provides a vehicle speed control method, device, medium, equipment and vehicle.

[0005] This disclosure provides a method for controlling the speed of an autonomous vehicle, including:

[0006] Acquire real-time vehicle status data; the real-time status data includes real-time estimated mass, current vehicle speed, desired vehicle speed, and road condition information; the road condition information includes: current road gradient, road adhesion coefficient, and road rolling resistance coefficient.

[0007] Based on the real-time status data, the target torque is determined;

[0008] The target torque is used to adjust the vehicle from the current speed to the desired speed.

[0009] Optionally, obtaining the desired vehicle speed includes:

[0010] Obtain the desired vehicle speed to be processed, output by the domain controller based on the current driving requirements;

[0011] The desired vehicle speed is obtained by limiting and filtering the desired vehicle speed.

[0012] Optionally, obtaining the current vehicle speed includes:

[0013] Obtain motor speed, wheel radius, and vehicle speed ratio;

[0014] Based on the motor speed, the wheel radius, and the vehicle speed ratio, the current vehicle speed to be processed is obtained;

[0015] The current vehicle speed to be processed is filtered to obtain the current vehicle speed.

[0016] Optionally, obtaining the real-time estimated quality includes:

[0017] Acquire vehicle driving status data; the driving status data includes the current gear command, current road gradient, current acceleration, current vehicle speed, and current driving torque of the motor;

[0018] Based on the driving status data, if the mass estimation trigger condition is met, the road rolling resistance coefficient is further obtained.

[0019] 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.

[0020] Optionally, obtaining the current road slope includes: obtaining the current road slope determined by the domain controller; wherein the domain controller determines the current road slope at the current location of the vehicle based on the vehicle's current location and prior road slope map information; and / or, the domain controller determines the current road slope based on slope information collected by a slope sensor mounted on the vehicle.

[0021] Obtaining the road adhesion coefficient and the road rolling resistance coefficient includes: obtaining the road adhesion coefficient and the road rolling resistance coefficient determined by the domain controller; wherein the domain controller identifies the material of the current road and the dryness and wetness of the road surface based on the image acquisition sensor, and matches the corresponding road adhesion coefficient and road rolling resistance coefficient.

[0022] Optionally, determining the target torque based on the real-time status data includes:

[0023] Based on the current vehicle speed and the desired vehicle speed, acceleration and deceleration control parameters are generated using vehicle speed difference processing and PID control.

[0024] Based on the acceleration / deceleration control parameters, the desired vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient, and the road rolling resistance coefficient, the required motor torque is obtained using the vehicle dynamics equations.

[0025] The required torque of the motor is limited and filtered to obtain the target torque. Optionally, based on the current vehicle speed and the desired vehicle speed, acceleration and deceleration control parameters are generated using vehicle speed difference processing and PID control, including:

[0026] Based on the current vehicle speed and the desired vehicle speed, and in combination with the vehicle operating mode and the vehicle speed difference threshold, the real-time vehicle speed difference is determined.

[0027] When the integral clearing condition of PID control is met, a corresponding integral clearing instruction is generated;

[0028] Based on four factors, the parameters of PID control are adjusted and optimized to obtain the target proportional parameter, target integral parameter, and target derivative parameter. The four factors include: the difference between the expected vehicle speed and the real-time vehicle speed, the real-time estimated mass, the current road gradient, and the road adhesion coefficient.

[0029] Based on at least the real-time vehicle speed difference, the integral zeroing command, the target proportional parameter, the target integral parameter, and the target derivative parameter, the acceleration / deceleration control parameters are generated using a PID control algorithm.

[0030] Optionally, before performing parameter adjustment and optimization for PID control, the method further includes:

[0031] When the desired vehicle speed and the actual vehicle speed meet the correction conditions, the proportional parameter in the PID control is corrected.

[0032] Optionally, the correction conditions include:

[0033] The desired vehicle speed is greater than the vehicle speed threshold; and

[0034] The current vehicle speed is less than the desired vehicle speed, and the cumulative duration of the speed being below the speed threshold is greater than the preset duration.

[0035] Optionally, determining the real-time speed difference based on the current vehicle speed and the desired vehicle speed, combined with the vehicle operating mode and a speed difference threshold, includes:

[0036] When the vehicle is in a speed-controlled operation mode, a non-automatic operation mode, or an automatic operation mode where speed control is not ready, the real-time vehicle speed difference is 0.

[0037] When the vehicle is in the speed control preparation state in autonomous driving mode, if the direction of the current vehicle speed is the same as the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed minus the current vehicle speed; if the direction of the current vehicle speed is opposite to the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed plus the current vehicle speed; and in both cases, the real-time speed difference is less than or equal to the speed difference threshold.

[0038] Optionally, the condition for clearing the integral includes at least one of the following conditions:

[0039] Condition 1: When the vehicle is switching between automatic and manual modes;

[0040] Condition 2: The real-time speed difference is greater than zero, and the acceleration / deceleration control parameter is less than zero; or the real-time speed difference is less than zero, and the acceleration / deceleration control parameter is greater than zero.

[0041] Condition 3: Braking pressure command is issued;

[0042] Condition 4: Gear shift command;

[0043] Condition 5: Motor operating mode switching;

[0044] Condition 6: Change in vehicle readiness status.

[0045] Optionally, the parameter adjustment and optimization includes proportional parameter adjustment and optimization, integral parameter adjustment and optimization, and derivative parameter adjustment and optimization;

[0046] The adjustment and optimization of the proportional parameters include:

[0047] The optimization is based on the proportional parameter adjustment of the difference between the desired vehicle speed and the real-time vehicle speed; wherein, the proportional parameter is directly proportional to the desired vehicle speed and directly proportional to the difference between the real-time vehicle speed.

[0048] The optimization is based on the adjustment of the proportional parameter of the real-time estimated quality; where the proportional parameter is directly proportional to the real-time estimated quality.

[0049] The proportional parameters are adjusted and optimized based on the current road slope; the proportional parameters are directly proportional to the current road slope.

[0050] And optimization of proportional parameters based on road adhesion coefficient; wherein, the proportional parameters are inversely proportional to the road adhesion coefficient;

[0051] The integration parameter adjustment and optimization includes:

[0052] Optimize the integral parameters based on the difference between the desired vehicle speed and the real-time vehicle speed;

[0053] Optimization of integral parameters based on real-time quality estimation;

[0054] Optimize integral parameters based on the current road slope;

[0055] And optimization of integral parameters based on road adhesion coefficient;

[0056] The differential parameter adjustment and optimization includes:

[0057] Optimization based on differential parameter adjustment of the difference between desired vehicle speed and real-time vehicle speed;

[0058] Optimization based on real-time quality estimation of differential parameters;

[0059] Optimize the differential parameters based on the current road slope;

[0060] And optimization of differential parameters based on road adhesion coefficient.

[0061] Optionally, obtaining the required torque of the motor using the vehicle dynamics equations includes:

[0062] Calculate the required torque of the motor using the following formula:

[0063]

[0064] Where T represents the required torque of the motor, ρ represents the air density, A represents the frontal area, and C represents the frontal area. D δ represents the air resistance coefficient, v represents the current vehicle speed, f represents the road rolling resistance coefficient, m represents the real-time estimated mass, g represents the gravity coefficient, i represents the current road slope, δ represents the vehicle rotational mass conversion factor after taking into account the rotational mass inertia torque, a represents the acceleration / deceleration control parameter, r represents the effective radius of the vehicle's tires, K represents the vehicle speed ratio, and η represents the transmission mechanical efficiency.

[0065] Optionally, the required torque of the motor is limited and filtered to obtain the target torque, including:

[0066] The required torque of the motor is limited based on the maximum available torque of the vehicle; the maximum available torque is determined based on the state of charge of the power battery in the vehicle, the allowable charging and discharging power of the power battery, the external characteristic torque of the motor, the driving and regenerative torque limits of the motor, and the fault conditions of the whole vehicle.

[0067] The motor torque requirement obtained after the limitation is filtered to obtain the target torque.

[0068] Optionally, the method further includes:

[0069] The target torque is transmitted to a controlled component, which then adjusts the vehicle from the current speed to the desired speed.

[0070] This disclosure also provides a speed control device for an autonomous vehicle, comprising:

[0071] The acquisition module is used to acquire real-time status data of the vehicle; the real-time status data includes real-time estimated mass, current vehicle speed, expected vehicle speed, and road condition information; the road condition information includes: current road gradient, road adhesion coefficient, and road rolling resistance coefficient.

[0072] The determination module is used to determine the target torque based on the real-time status data;

[0073] The target torque is used to adjust the vehicle from the current speed to the desired speed.

[0074] This disclosure also provides a computer-readable storage medium storing a computer program for performing the steps of any of the above methods.

[0075] This disclosure also provides an automotive device, including: a processor; a memory for storing processor-executable instructions; the processor being configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of any of the above methods.

[0076] This disclosure also provides a vehicle including any of the aforementioned vehicle equipment.

[0077] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0078] The vehicle speed control method disclosed herein includes acquiring real-time vehicle status data; the real-time status data includes real-time estimated mass, current vehicle speed, desired vehicle speed, and road condition information; the road condition information includes: current road slope, road adhesion coefficient, and road rolling resistance coefficient; based on the real-time status data, a target torque is determined; the target torque is used to adjust the vehicle from the current speed to the desired speed. In the technical solution of this disclosure, by combining the vehicle's real-time estimated mass, current vehicle speed, desired vehicle speed, and road condition information, such as the current road slope, road adhesion coefficient, and road rolling resistance coefficient, to determine the target torque corresponding to vehicle speed control, timely and accurate speed control can be achieved for different vehicle and road conditions, i.e., fast response speed and good following performance. Attached Figure Description

[0079] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0080] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 This is a schematic diagram of the structure of a vehicle provided in an embodiment of the present disclosure;

[0082] Figure 2 A schematic flowchart of a vehicle speed control method provided in an embodiment of this disclosure;

[0083] Figure 3 A schematic flowchart of another vehicle speed control method provided in this embodiment of the present disclosure;

[0084] Figure 4 A schematic diagram illustrating the generation process of vehicle acceleration and deceleration control parameters in a vehicle speed control method provided in this disclosure embodiment;

[0085] Figure 5 This is a schematic diagram of the structure of a vehicle speed control device provided in an embodiment of the present disclosure;

[0086] Figure 6 This is a schematic diagram of another vehicle speed control device provided in an embodiment of the present disclosure. Detailed Implementation

[0087] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0088] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0089] The vehicle speed control method for autonomous vehicles provided in this disclosure can be applied to the field of vehicle control technology for pure electric vehicles, such as realizing longitudinal speed control of a pure electric autonomous tractor. Vehicle speed control for autonomous vehicles is generally divided into lateral control and longitudinal control; lateral control may include identifying curvature and turning control, while longitudinal control may include controlling the vehicle speed during travel. The autonomous driving domain controller, through perception, planning, and decision-making, can transmit a desired vehicle speed to the vehicle controller; the vehicle controller, combining the desired vehicle speed, current speed, real-time estimated mass, and road condition information, determines the target torque corresponding to the speed adjustment; the vehicle actuator, such as the motor controller, based on the target torque and through power control, achieves closed-loop following control of the vehicle speed. This method considers the impact of real-time estimated mass, vehicle condition, and road condition changes on speed control, resulting in good following performance, stable following applicable to different scenarios, fast response, and improved energy utilization.

[0090] In some embodiments, based on the aforementioned real-time estimated mass, vehicle condition, and road condition information, the parameters of the PID control in vehicle speed control are further corrected and optimized. This enables stable speed following and minimal overshoot, resulting in smooth driving without sudden acceleration or deceleration, and good ride comfort. For example, in response to external disturbances, such as passing speed bumps or hitting pebbles, timely adjustments can be made to ensure the current vehicle speed quickly catches up with the desired speed, providing timely following.

[0091] For example, Figure 1This is a schematic diagram of a vehicle structure provided for an embodiment of the present disclosure, illustrating the system architecture of an electric unmanned automated tractor. (Refer to...) Figure 1 The vehicle 01 may include: a drive motor, a reducer, a differential, drive wheels, a power battery, a high-voltage distribution box, a DC-DC converter, a lead-acid battery, a vehicle control unit (VCU), a battery management system (BMS), a motor control unit (MCU), an autonomous driving domain control unit (DCU) system, an electric power steering system (EPS), an electronic parking brake system (EPB), an electronic hydraulic brake system (EHB), a body control system (BCM), a tire pressure monitoring system (TPMS), an instrument cluster system (ICM), a towing bolt, a towing bolt controller (ATB), and a trailer, etc. The above structures are connected by mechanical connections (shown as bold solid lines in the figure), electrical connections (shown as dashed lines in the figure), or bus connections (shown as thin solid lines in the figure).

[0092] The technical solutions provided in this disclosure are mainly based on vehicle controllers, motor controllers, and autonomous driving domain controllers. For example, refer to... Figure 2 S11, S12, and S13 are shown: The autonomous driving domain controller determines the vehicle's desired driving speed (i.e., desired vehicle speed), current road gradient, road adhesion coefficient, and road rolling resistance coefficient through perception, planning, and decision-making, and transmits them to the vehicle controller; The vehicle controller combines speed (including current vehicle speed and desired vehicle speed) recognition processing, acceleration and deceleration control, real-time mass estimation, torque calculation, limiting, and filtering processing, and outputs the target torque to the motor controller through real-time parameter adjustment of the speed closed-loop control; The motor controller receives the target torque and drives the motor based on it, enabling the vehicle to track the desired vehicle speed, and responding quickly, stably, and effectively to track the desired vehicle speed.

[0093] The control method provided in this disclosure mainly consists of steps executed by the vehicle controller, such as the acquisition, identification and processing of real-time parameters, and the output of the target torque.

[0094] The following description, in conjunction with the accompanying drawings, provides an exemplary account of the vehicle speed control method, apparatus, medium, equipment, and vehicle provided in the embodiments of this disclosure.

[0095] For example, Figure 3 This is a flowchart illustrating another vehicle speed control method provided in an embodiment of this disclosure, which can be executed by the vehicle controller. (Refer to...) Figure 3 The method may include the following steps:

[0096] S110, Obtain real-time vehicle status data.

[0097] In this embodiment of the disclosure, the real-time status data is relevant data used to characterize the real-time vehicle condition and road condition, and may include real-time estimated mass, current vehicle speed, expected vehicle speed and road condition information; the road condition information includes: current road gradient, road adhesion coefficient and road rolling resistance coefficient.

[0098] The real-time estimated mass is determined based on the vehicle's current real-time state, taking into account actual vehicle and road conditions, rather than a pre-set fixed constant value. Therefore, applying this real-time estimated mass to the vehicle speed control method facilitates accurate and timely speed tracking.

[0099] The current vehicle speed is the actual speed of the vehicle in its current driving state, and the desired vehicle speed is the target vehicle speed determined based on the vehicle control requirements. The purpose of vehicle speed control is to make the actual vehicle speed follow the desired vehicle speed.

[0100] Among them, road condition information refers to the actual road conditions of the vehicle in the current scenario, which may include information that affects vehicle speed control, such as the current road slope, road adhesion coefficient, and road rolling resistance coefficient, so as to achieve accurate calculation of the target torque and thus timely and accurate control of vehicle speed.

[0101] The following text will illustrate the methods for obtaining various real-time status data.

[0102] S120: Determine the target torque based on real-time status data.

[0103] The target torque is used to adjust the vehicle from its current speed to the desired speed in order to achieve speed following.

[0104] In this embodiment, the target torque is determined based on real-time status data related to vehicle and road conditions, which is highly accurate. This enables timely and accurate speed control for different vehicle and road conditions, resulting in a fast response speed, good following ability, small speed overshoot, smooth operation, and good smoothness.

[0105] In some embodiments, obtaining the desired vehicle speed may specifically include:

[0106] Obtain the desired vehicle speed to be processed, output by the domain controller based on the current driving requirements;

[0107] The desired vehicle speed is then limited and filtered to obtain the desired vehicle speed.

[0108] In this embodiment, the current driving demand can be associated with user settings, road conditions, and other data. Road conditions may include whether the road is clear or whether there are obstacles. The domain controller performs planning control based on the current driving demand and outputs the desired vehicle speed to be processed to the vehicle controller via the intelligent driving CAN bus. Correspondingly, the vehicle controller receives the desired vehicle speed to be processed and performs limiting and filtering processing to obtain the desired vehicle speed. It is understood that the desired vehicle speed to be processed can be the desired vehicle speed after being limited and filtered by the domain controller. After being transmitted to the vehicle controller, it undergoes secondary limiting and filtering processing to eliminate unreasonable data and ensure that the vehicle speed changes continuously within a preset range to ensure smooth driving.

[0109] In some embodiments, obtaining the current vehicle speed may specifically include:

[0110] Obtain motor speed, wheel radius, and vehicle speed ratio;

[0111] Based on motor speed, wheel radius, and vehicle speed ratio, obtain the current vehicle speed to be processed;

[0112] The current vehicle speed is filtered to obtain the current vehicle speed.

[0113] In this embodiment of the disclosure, the current vehicle speed corresponds to the linear velocity of the wheels. The vehicle controller determines the current vehicle speed to be processed based on the acquired motor speed, wheel radius, and vehicle speed ratio (i.e., transmission ratio), and then performs filtering to eliminate inaccurate data, thereby obtaining a more accurate current vehicle speed.

[0114] In other implementations, the current vehicle speed can be determined based on changes in the vehicle's location information and time; or other methods can be used to determine the current vehicle speed, which are not limited here.

[0115] In some embodiments, obtaining real-time estimated quality may specifically include:

[0116] Acquire vehicle driving status data; the driving status data may include the current gear command, current road gradient, current acceleration, current vehicle speed, and current drive torque of the motor;

[0117] Based on driving status data, when it is determined that the mass estimation trigger condition is met, the road rolling resistance coefficient is further obtained;

[0118] The real-time estimated mass is determined based on the current vehicle speed, current acceleration, current road gradient, road rolling resistance coefficient, and current driving torque.

[0119] In this embodiment of the disclosure, the real-time mass of the vehicle is estimated based on a program algorithm, thereby eliminating the need for additional sensors, thus avoiding the need to occupy vehicle space and increase vehicle investment costs, and facilitating widespread use. At the same time, the mass estimation can be combined with the actual conditions of the vehicle and the road, and the accuracy of the mass estimation is high, which is conducive to timely speed following.

[0120] For example, refer to Figure 2 This step can be executed by the vehicle mass estimation module in the vehicle controller. Specifically, it can be combined with vehicle and road condition recognition, vehicle longitudinal dynamics model calculation, calculated mass limit processing, updating and averaging the mass stored in the controller and the limited mass, and average mass Kalman filtering to achieve real-time estimation of vehicle mass. This allows for a more accurate estimation of vehicle mass, improving the accuracy of vehicle mass estimation. It is particularly beneficial for accurately estimating the mass of electric unmanned automated tractors with large mass variations, providing the necessary conditions for parameter adjustment for precise speed control, and thus facilitating timely speed following.

[0121] In some embodiments, obtaining the current road slope may specifically include obtaining the current road slope determined by the domain controller.

[0122] Specifically, the domain controller determines the current road slope based on the vehicle's current location and prior road slope map information; and / or, the domain controller determines the current road slope based on slope information collected by the slope sensors installed on the vehicle.

[0123] In this embodiment of the disclosure, the current road slope (represented by i in this document) is collected, identified and processed based on the domain controller, and then transmitted to the vehicle controller.

[0124] Specifically, the domain controller can determine the slope information of the vehicle's current location based on the vehicle's current location and the pre-measured (i.e., prior) road slope map information, that is, determine the current road slope; the current road slope is sent by the domain controller to the vehicle controller through the intelligent driving CAN bus; correspondingly, the vehicle controller obtains the current road slope.

[0125] Alternatively, the vehicle can be equipped with a slope sensor, which collects the slope information of the current road and transmits it to the domain controller. Based on the received slope information, the domain controller determines the current road slope and sends it to the vehicle controller via the intelligent driving CAN bus. Correspondingly, the vehicle controller obtains the current road slope.

[0126] This setting allows for convenient and accurate acquisition of the current road gradient, which is beneficial for accurate estimation of vehicle weight.

[0127] In some embodiments, obtaining the road adhesion coefficient and the road rolling resistance coefficient may specifically include obtaining the road adhesion coefficient and the road rolling resistance coefficient determined by the domain controller.

[0128] The domain controller identifies the material and dryness / wetness of the current road surface based on image acquisition sensors, and matches the corresponding road adhesion coefficient and road rolling resistance coefficient.

[0129] In this embodiment of the disclosure, the domain controller identifies and processes the road adhesion coefficient and the road rolling resistance coefficient, and transmits them to the vehicle controller.

[0130] Specifically, the domain controller can identify road conditions through image acquisition components, such as cameras, and by applying image recognition processing technology, determine the current road material and the dryness / wetness of the road surface, matching the corresponding road adhesion coefficient and road rolling resistance coefficient. For example, the current road material may include asphalt pavement, concrete pavement, gravel pavement, icy pavement, etc. The matching relationship between the road adhesion coefficient and road rolling resistance coefficient and the material and dryness / wetness can be obtained experimentally and stored for later use in this step. This road adhesion coefficient and road rolling resistance coefficient can be sent by the domain controller to the vehicle controller via the intelligent driving CAN bus for application in vehicle speed control, enabling timely and effective speed control for different road conditions.

[0131] In some embodiments, reference Figure 2 The vehicle controller can also be equipped with a vehicle speed recognition and processing module, a vehicle acceleration and deceleration control module, an autonomous driving torque calculation module, an autonomous driving torque limiting module, and an autonomous driving torque filtering module. The vehicle speed recognition and processing module is used to identify and process the current vehicle speed and the desired vehicle speed (see above for details). The vehicle acceleration and deceleration control module is used to generate acceleration and deceleration control parameters. The autonomous driving torque calculation module is used to generate the required motor torque. The autonomous driving torque limiting module and the autonomous driving torque filtering module are used to comprehensively limit and filter the required motor torque to obtain the target torque. An illustrative example is provided below.

[0132] In some embodiments, determining the target torque based on real-time status data includes:

[0133] Based on the current vehicle speed and the desired vehicle speed, acceleration and deceleration control parameters are generated using vehicle speed difference processing and PID control.

[0134] Based on acceleration and deceleration control parameters, desired vehicle speed, current vehicle speed, real-time estimated mass, current road gradient, and road rolling resistance coefficient, the required torque of the motor is obtained using vehicle dynamics equations.

[0135] The required torque of the motor is limited and filtered to obtain the target torque.

[0136] In this embodiment, based on real-time state data representing the current state of the vehicle, acceleration and deceleration control parameters are first generated using speed difference processing and PID control, thus obtaining the motion parameters for adjusting from the current vehicle speed to the desired vehicle speed. Then, the desired vehicle speed, current vehicle speed, real-time estimated mass, current road gradient, and road rolling resistance coefficient are combined—that is, the parameters representing the current vehicle and road conditions are incorporated into the vehicle dynamics equations—to obtain the required motor torque. Finally, the required motor torque is limited and filtered to ensure that the obtained target torque is within a reasonable range that allows for smooth speed changes. Therefore, by incorporating real-time parameters corresponding to vehicle and road conditions into vehicle speed control, effective speed following can be achieved with minimal overshoot and good smoothness.

[0137] In some embodiments, Figure 4 This disclosure provides a schematic diagram of a process for generating vehicle acceleration / deceleration control parameters in a vehicle speed control method. The parameters can be generated by... Figure 2 The vehicle acceleration / deceleration control module shown in the image is executed. Combined with... Figure 2 and Figure 4 The vehicle acceleration and deceleration control module generates acceleration and deceleration control parameters that may include: speed difference processing, integral control zeroing, proportional base correction, PID parameter adjustment and optimization, PID control, and PID control output limitation.

[0138] For example, the vehicle acceleration / deceleration control module can output vehicle acceleration / deceleration control parameters and PID parameters (including proportional parameter Kp, integral parameter Ki, and derivative parameter Kd) based on the vehicle speed closed-loop PID control method. Specifically, the PID parameters can be obtained by looking up tables based on parameters such as desired vehicle speed, real-time vehicle speed difference (i.e., the difference between desired vehicle speed and current vehicle speed), real-time estimated mass, current road gradient, and road adhesion coefficient, so as to achieve real-time flexible adjustment for different vehicle and road conditions.

[0139] In some embodiments, based on the current vehicle speed and the desired vehicle speed, acceleration / deceleration control parameters are generated using speed difference processing and PID control, including:

[0140] Based on the current vehicle speed and the desired vehicle speed, and in combination with the vehicle operating mode and the vehicle speed difference threshold, the real-time vehicle speed difference is determined.

[0141] When the integral clearing condition of PID control is met, a corresponding integral clearing instruction is generated;

[0142] Based on four factors, the parameters of PID control are adjusted and optimized to obtain the target proportional parameter, target integral parameter, and target derivative parameter. The four factors include: the difference between the expected vehicle speed and the real-time vehicle speed, the real-time estimated mass, the current road gradient, and the road adhesion coefficient.

[0143] Based on at least the real-time vehicle speed difference, integral zeroing command, target proportional parameter, target integral parameter, and target derivative parameter, the PID control algorithm is used to generate acceleration and deceleration control parameters.

[0144] In this embodiment of the disclosure, the real-time vehicle speed difference and related parameters of PID control are first determined. Then, the PID control algorithm is used with the real-time vehicle speed difference, integral clearing command and PID parameters (including target proportional parameter, target integral parameter and target derivative parameter, as well as other conventional parameters of PID control, which are not described in detail here) as inputs. The control output is realized through the PID control algorithm, that is, the acceleration and deceleration control parameters (i.e. acceleration and deceleration command) are obtained.

[0145] For example, the PID control algorithm can be expressed as:

[0146]

[0147] Among them, K p K is the scaling factor, i.e., the target scaling parameter. i K represents the integration coefficients, i.e., the target integration parameters. d Here, e(k) represents the differential coefficients, i.e., the target differential parameters; e(k) represents the real-time vehicle speed difference at the k-th sampling, which can be expressed in km / h; and a(k) represents the output at the k-th sampling, expressed in m / s. 2 When its value is negative, it corresponds to a deceleration command; when its value is positive, it corresponds to an acceleration command.

[0148] In some embodiments, the PID control output limit specifically includes:

[0149] 1. The control commands output by the PID controller should not exceed the designed maximum acceleration / deceleration limits.

[0150] 2. When the vehicle is not in a Ready state, the control output is limited to 0;

[0151] 3. When the vehicle is in a non-driving gear, the control output is limited to 0;

[0152] IV. When the electronic parking brake (EPB) is in the non-released state, the limit control output is 0.

[0153] In this embodiment of the disclosure, by limiting the output of PID control, the acceleration and deceleration control parameters are kept within a reasonable range of the maximum available acceleration and deceleration limit and are consistent with the current real-time state of the vehicle, thereby facilitating effective control of the vehicle speed.

[0154] For example, Figure 4 In the diagram, a0 represents the acceleration / deceleration control parameters generated by the PID control, and a represents the acceleration / deceleration control parameters after the PID control output is limited. When the PID control output is not limited, a0 is used as the output a.

[0155] In some embodiments, before performing parameter adjustment and optimization for PID control, the method may further include:

[0156] When the desired vehicle speed and the actual vehicle speed meet the correction conditions, the proportional parameter in the PID control is corrected.

[0157] In this embodiment of the disclosure, by modifying the proportional parameter in the PID control, the PID control can be kept within an appropriate error control range, which is beneficial for achieving vehicle speed following control, with timely response and good effectiveness.

[0158] In some embodiments, the correction conditions include:

[0159] The desired vehicle speed is greater than the vehicle speed threshold; and the current vehicle speed is less than the desired vehicle speed, and the cumulative duration of the speed being below the vehicle speed threshold is greater than the preset duration.

[0160] In this embodiment, the vehicle speed threshold is a threshold indicating that the vehicle speed is too high, and the preset duration is a threshold indicating that the duration is too long. Based on this, when the desired vehicle speed is greater than the vehicle speed threshold, that is, when the current vehicle speed cannot keep up with the desired vehicle speed and the cumulative duration exceeds the preset duration, the proportional parameter is corrected and adjusted by the output parameters of the PID control algorithm.

[0161] For example, this situation typically occurs when the vehicle is starting and the vehicle load increases. By adjusting the parameters, the vehicle can start quickly, thus improving the problem of slow vehicle start-up.

[0162] In some embodiments, the real-time vehicle speed difference is determined based on the current vehicle speed and the desired vehicle speed, combined with the vehicle operating mode and a vehicle speed difference threshold, including:

[0163] When the vehicle is in the speed control ready state under the speed operation mode, non-autonomous driving mode or autonomous driving mode, the real-time vehicle speed difference is 0.

[0164] When the vehicle is in the speed control preparation state in autonomous driving mode, if the direction of the current vehicle speed is the same as the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed minus the current vehicle speed. If the direction of the current vehicle speed is opposite to the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed plus the current vehicle speed. In both cases, the real-time speed difference is less than or equal to the speed difference threshold.

[0165] In this embodiment of the disclosure, vehicle speed difference processing is performed, that is, the difference between the desired vehicle speed and the current vehicle speed (i.e., the actual vehicle speed) is calculated. Specifically, the real-time vehicle speed difference (in terms of v) is generally... e (represented by) equals the expected vehicle speed (in v) D Subtract the current speed (represented by v) from the speed of the vehicle, that is:

[0166] v e =v D -v (indicates the sign of the direction)

[0167] Furthermore, it is understandable that the real-time speed difference does not exceed the positive or negative threshold, meaning it remains within a reasonable range of speed control.

[0168] Specifically, when the actual driving direction of the vehicle is consistent with the driving command direction, that is, when the direction of the current vehicle speed is consistent with the direction of the desired vehicle speed, the real-time speed difference is:

[0169] v e =v D -v (without direction, numerical calculation only)

[0170] Furthermore, when the actual driving direction of the vehicle is inconsistent with the driving command direction, that is, when the direction of the current vehicle speed is inconsistent with the direction of the desired vehicle speed, the real-time speed difference is:

[0171] v e =v D +v (without direction, numerical calculation only)

[0172] Furthermore, when the vehicle is in RPM mode, this speed control method is not activated, and the real-time speed difference is set to 0, i.e., v e =0; When the vehicle is in non-autonomous driving mode, automatic speed control is not performed, that is, this speed control method is not activated, and v is also 0. e =0; and when the vehicle is not Ready, v e =0.

[0173] Therefore, corresponding real-time vehicle speed differences were obtained for various different situations.

[0174] In some embodiments, the vehicle speed difference v used in PID integral control eKi Difference from real-time vehicle speed v e The following relationship must be satisfied:

[0175] When the acceleration / deceleration control parameter output by the PID control (the acceleration / deceleration control parameter before the PID limit output) is greater than the maximum allowable acceleration (i.e., the positive value corresponding to the maximum usable acceleration / deceleration limit range), and the real-time vehicle speed difference v e When the value is greater than 0, the speed difference v used for integral control is... eKi=0, otherwise v eKi =v e .

[0176] When the acceleration / deceleration control parameter output by the PID control is less than the minimum allowable acceleration (i.e., the negative value corresponding to the maximum usable acceleration / deceleration limit range), and the vehicle speed difference ve is less than 0, the vehicle speed difference v used for integral control... eKi =0, otherwise v eKi =v e .

[0177] In this way, the value of the speed difference used for PID integral control is limited, thus meeting the requirements for safe vehicle speed control.

[0178] In some embodiments, resetting the integral to zero allows the speed control corresponding to the current cycle to restart, avoiding the influence of the previous cycle on the speed control of this cycle. Exemplarily, the condition for resetting the integral to zero includes at least one of the following conditions:

[0179] Condition 1: When the vehicle is switching between automatic and manual modes;

[0180] Condition 2: The real-time speed difference is greater than zero, and the acceleration / deceleration control parameter is less than zero; or the real-time speed difference is less than zero, and the acceleration / deceleration control parameter is greater than zero.

[0181] Condition 3: Braking pressure command is issued;

[0182] Condition 4: Gear shift command;

[0183] Condition 5: Motor operating mode switching;

[0184] Condition 6: Change in vehicle readiness status.

[0185] In this embodiment of the disclosure, when any of the above conditions are met, it indicates that a new vehicle speed control cycle has been switched to. At this time, the PID integral is cleared to zero. In this way, when the new vehicle speed control cycle starts, the integral is started from 0 again, avoiding the influence of the previous cycle on the vehicle speed control in this cycle, so as to make the vehicle speed control more accurate, thereby ensuring timely response and fast following.

[0186] In some embodiments, parameter adjustment and optimization may specifically include: proportional parameter adjustment and optimization, integral parameter adjustment and optimization, and derivative parameter adjustment and optimization.

[0187] This allows for the adjustment and optimization of the proportional, integral, and derivative parameters in PID control, thereby reducing PID control errors and achieving precise vehicle speed control.

[0188] In some embodiments, the scaling parameter adjustment and optimization includes adjusting four related factors to comprehensively output a reasonable target scaling parameter. This step may specifically include:

[0189] The optimization is based on the proportional parameter adjustment of the difference between the desired vehicle speed and the real-time vehicle speed; wherein, the proportional parameter is directly proportional to the desired vehicle speed and directly proportional to the difference between the real-time vehicle speed.

[0190] The optimization is based on the adjustment of the proportional parameter of the real-time estimated quality; where the proportional parameter is directly proportional to the real-time estimated quality.

[0191] The proportional parameters are adjusted and optimized based on the current road slope; the proportional parameters are directly proportional to the current road slope.

[0192] And the proportional parameter adjustment and optimization based on the road adhesion coefficient; wherein the proportional parameter is inversely proportional to the road adhesion coefficient.

[0193] In this embodiment, for each relevant factor, the corresponding proportional parameter can be obtained by looking up a table; then, the target proportional parameter is obtained by combining the weight coefficients of the four parts.

[0194] For example, regarding the optimization of the proportional parameter based on the difference between the desired vehicle speed and the real-time vehicle speed: the desired vehicle speed and the real-time vehicle speed difference can be used as inputs to a two-dimensional table, and the proportional parameter of this factor can be output. For example, the proportional parameter corresponding to different desired vehicle speeds and real-time vehicle speed differences can be obtained through experimental calibration. The larger the desired vehicle speed, the larger the proportional parameter; the larger the real-time vehicle speed difference, the larger the proportional parameter, thus facilitating effective control of vehicle speed.

[0195] For example, regarding the optimization of proportional parameters based on real-time estimated quality: the real-time estimated quality can be used as input to a one-dimensional table, and the proportional parameters of this factor can be output. For example, the proportional parameters corresponding to different real-time estimated qualities can be obtained through experimental calibration. The larger the real-time estimated quality, the larger this proportional parameter, which is beneficial for effective control of vehicle speed.

[0196] For example, regarding the optimization of proportional parameters based on the current road slope: the current road slope can be used as input to a one-dimensional table, and the proportional parameters of this factor can be output. For example, the proportional parameters corresponding to different current road slopes can be obtained through experimental calibration. The larger the current road slope, the larger this proportional parameter, which is beneficial for effective control of vehicle speed.

[0197] For example, regarding the optimization of proportional parameters based on the road adhesion coefficient: the road adhesion coefficient can be used as input to a one-dimensional table, and the proportional parameters of this factor can be output. For example, the proportional parameters corresponding to different road adhesion coefficients can be obtained through experimental calibration. The smaller the road adhesion coefficient, the smaller the proportional parameter, which is beneficial for effective control of vehicle speed.

[0198] Therefore, by combining vehicle and road condition information to adjust and optimize proportional parameters, PID control can be achieved for different vehicle and road conditions, which helps to reduce speed control errors, achieve precise speed regulation, and keep up with traffic.

[0199] In some embodiments, the optimization of integral parameters includes adjusting four related factors to comprehensively output a reasonable target integral parameter, which may specifically include:

[0200] Optimize the integral parameters based on the difference between the desired vehicle speed and the real-time vehicle speed;

[0201] Optimization of integral parameters based on real-time quality estimation;

[0202] Optimize integral parameters based on the current road slope;

[0203] And the integral parameter adjustment and optimization based on the road adhesion coefficient.

[0204] In this embodiment, for each relevant factor, the corresponding proportional parameter can be obtained by looking up a table; then, the target integral parameter is obtained by combining the weight coefficients of the four parts.

[0205] For example, regarding the adjustment and optimization of integral parameters based on the difference between desired vehicle speed and real-time vehicle speed: the desired vehicle speed and the difference between real-time vehicle speed can be used as input to a two-dimensional table, and the integral parameters of this factor can be output. For example, the integral parameters corresponding to different differences between desired vehicle speed and real-time vehicle speed can be obtained through experimental calibration.

[0206] For example, regarding the adjustment and optimization of integral parameters based on real-time estimated quality: the real-time estimated quality can be used as input to a one-dimensional table, and the integral parameters of this factor can be output. For example, the integral parameters corresponding to different real-time estimated qualities can be obtained through experimental calibration.

[0207] For example, for adjusting and optimizing the integral parameters based on the current road slope: the current road slope can be used as input to a one-dimensional table, and the integral parameters of this factor can be output. For example, the integral parameters corresponding to different current road slopes can be obtained through experimental calibration.

[0208] For example, regarding the adjustment and optimization of integral parameters based on the road adhesion coefficient: the road adhesion coefficient can be used as input to a one-dimensional table, and the integral parameters of this factor can be output. For example, the integral parameters corresponding to different road adhesion coefficients can be obtained through experimental calibration.

[0209] Therefore, by combining vehicle and road condition information to adjust and optimize integral parameters, PID control can be achieved for different vehicle and road conditions, which helps to reduce speed control errors, achieve precise speed regulation, and keep up with traffic.

[0210] In some embodiments, the optimization of differential parameters includes adjusting four related factors to comprehensively output a reasonable target differential parameter, which may specifically include:

[0211] Optimization based on differential parameter adjustment of the difference between desired vehicle speed and real-time vehicle speed;

[0212] Optimization based on real-time quality estimation of differential parameters;

[0213] Optimize the differential parameters based on the current road slope;

[0214] And optimization of differential parameters based on road adhesion coefficient.

[0215] In this embodiment, for each relevant factor, the corresponding differential parameter can be obtained by looking up a table; then, the target integral parameter is obtained by combining the weight coefficients of the four parts.

[0216] For example, optimization is performed on the differential parameters based on the difference between the desired vehicle speed and the real-time vehicle speed. The desired vehicle speed and the difference between the real-time vehicle speed are used as input to a two-dimensional table, and the differential parameters of this factor are output. For example, the differential parameters corresponding to different desired vehicle speeds and real-time vehicle speed differences can be obtained through experimental calibration.

[0217] For example, optimization is performed on the differential parameters based on the real-time estimated quality. The real-time estimated quality is taken as input to a one-dimensional table, and the differential parameters of this factor are output. For example, the differential parameters corresponding to different real-time estimated qualities can be obtained through experimental calibration.

[0218] For example, optimization is performed on the differential parameters based on the current road slope. The current road slope is used as input to a one-dimensional table, and the differential parameters of this factor are output. For example, the differential parameters corresponding to different current road slopes can be obtained through experimental calibration.

[0219] For example, optimization is performed on the differential parameters based on the road adhesion coefficient. The road adhesion coefficient is used as input to a one-dimensional table, and the differential parameters of this factor are output. For example, the differential parameters corresponding to different road adhesion coefficients can be obtained through experimental calibration.

[0220] Therefore, by combining vehicle and road condition information to adjust and optimize differential parameters, PID control can be achieved for different vehicle and road conditions, which helps to reduce speed control errors, achieve precise speed regulation, and keep up with traffic.

[0221] In some embodiments, the required torque of the motor is obtained using vehicle dynamics equations, which may specifically include:

[0222] Calculate the required torque of the motor using the following formula:

[0223]

[0224] Where T represents the required torque of the motor, ρ represents the air density, A represents the frontal area, and C represents the frontal area. D δ represents the air resistance coefficient, v represents the current vehicle speed, f represents the road rolling resistance coefficient, m represents the real-time estimated mass, g represents the gravity coefficient, i represents the current road slope, δ represents the vehicle rotational mass conversion factor after taking into account the rotational mass inertia torque, a represents the acceleration / deceleration control parameter, r represents the effective radius of the vehicle's tires, K represents the vehicle speed ratio, and η represents the transmission mechanical efficiency.

[0225] In this embodiment of the disclosure, the autonomous driving torque calculation module in the vehicle controller can be used to calculate the desired vehicle speed v. D The system takes the current vehicle speed v, real-time estimated mass m, acceleration / deceleration control parameter a, current road slope i, and road rolling resistance coefficient f as inputs. Through vehicle dynamics equations, it calculates the required motor torque (i.e., the motor torque command). This enables the calculation of motor torque demand based on vehicle and road conditions, improving the accuracy of torque calculation for various scenarios. This, in turn, enhances the timeliness and accuracy of speed control, leading to timely response and effective following.

[0226] Specifically, based on the force analysis of the vehicle, the formula for calculating the required torque of the motor is derived as follows:

[0227] The resistance experienced by a vehicle includes wind resistance F. w Rolling resistance F f Slope resistance F i and acceleration resistance F j The vehicle's driving balance equation is:

[0228] F = F w +F f +F i +F j (1)

[0229] Where F represents the tangential reaction force exerted by the ground on the driving wheel, i.e., the driving force.

[0230] Furthermore, wind resistance F wCalculate using the following formula:

[0231]

[0232] Where: ρ represents air density, which can take a value of 1.2258 Ns. 2 m -4 A represents the windward area, which can be expressed in square meters (m²). 2 ); C D represents the air drag coefficient; v is the air velocity relative to the vehicle, which is numerically equal to the current vehicle speed.

[0233] Rolling resistance F f Calculate using the following formula:

[0234] F f =f·m F g·cos(θ) (3)

[0235] Where: f represents the drag coefficient corresponding to the road rolling resistance, which corresponds to the result of the DCU's image acquisition component identifying and analyzing the road surface; m F represents the real-time estimated mass, with its physical unit being kilograms (kg), ultimately calculated by the VCU; g represents the gravitational acceleration, with its physical unit being meters per square meter (m / s²). 2 ); θ represents the road angle, its physical unit is radians (rad), and its relationship with the current road slope i is θ = arctan(i); based on this, equation (3) can be transformed into:

[0236] F f =f·m F g·cos(arctan(i)) (4)

[0237] For example, when the current vehicle speed is 0, the rolling resistance F f It is 0.

[0238] Slope resistance F i Calculate using the following formula:

[0239] F i =m F g·sin(θ) (5)

[0240] Combining the conversion between road angle and current road slope mentioned above, equation (5) can be transformed into:

[0241] F i =m F g·sin(arctan(i)) (6)

[0242] Acceleration resistance F j Calculate using the following formula:

[0243] F j =δm F a (7)

[0244] Where: a represents the current acceleration output by the vehicle speed closed-loop control; δ represents the vehicle rotational mass conversion factor after taking into account the rotational mass inertia torque.

[0245] Substituting and rearranging equations (1), (2), (4), (6), and (7) above, the resulting vehicle driving balance equation is:

[0246]

[0247] Meanwhile, the formula for calculating the required motor torque to achieve cruise control is as follows:

[0248]

[0249] Where T represents the required torque of the motor, r represents the effective radius of the tire, and its physical unit can be meters (m); K represents the transmission ratio; and η represents the mechanical efficiency of the transmission system.

[0250] Combining equations (8) and (9), we can obtain the final formula for calculating the required torque of the motor:

[0251]

[0252] In some embodiments, limiting and filtering the required torque of the motor to obtain the target torque includes:

[0253] The required torque of the motor is limited based on the maximum available torque of the vehicle (including the maximum available torque for drive and the maximum available torque for regenerative braking). The maximum available torque is determined based on the state of charge (SOC) of the power battery in the vehicle, the allowable charging and discharging power of the power battery, the external characteristic torque of the motor (including peak torque and continuous torque), the drive and regenerative torque limits of the motor, and the fault conditions of the whole vehicle.

[0254] The motor torque requirement obtained after the constraint is filtered to obtain the target torque.

[0255] In this embodiment of the disclosure, by limiting the motor's required torque, it can be restricted to a reasonable range and the available power of the power battery can be fully utilized, thereby improving energy utilization efficiency while effectively controlling vehicle speed. Furthermore, by filtering the limited motor's required torque, abrupt changes can be avoided, making the target torque change smoothly, thus resulting in better vehicle speed control smoothness.

[0256] In some embodiments, the method further includes:

[0257] The target torque is transmitted to the controlled components, which then adjust the vehicle from its current speed to the desired speed.

[0258] In this embodiment of the disclosure, the controlled component may be a motor controller. The vehicle controller sends the target torque to the motor controller; correspondingly, the motor controller receives the target torque and controls the motor to operate, so that the vehicle can be controlled to change speed from the current speed to the desired speed.

[0259] The vehicle speed control method provided in this disclosure combines real-time status data related to vehicle and road conditions to achieve vehicle speed control with minimal overshoot. It can stably, effectively, and quickly respond to the desired vehicle speed in various scenarios. During deceleration, energy can be recovered through motor braking energy feedback (electric braking) to improve the vehicle's energy utilization rate and increase driving range. Moreover, the control logic is clear and concise, which can mitigate the impact of slope changes and load changes on the vehicle's automatic driving, resulting in faster vehicle speed response, improved responsiveness, smoother operation, and better ride comfort, thus improving the vehicle's maneuverability, comfort, and safety.

[0260] Based on the above embodiments, this disclosure also provides a vehicle speed control device for autonomous vehicles, which can execute the steps of any of the above methods to achieve the corresponding beneficial effects.

[0261] For example, Figure 5 This is a schematic diagram of a vehicle speed control device provided in an embodiment of this disclosure. (Refer to...) Figure 5 The device 40 includes: an acquisition module 41 for acquiring real-time status data of the vehicle; the real-time status data includes real-time estimated mass, current vehicle speed, desired vehicle speed, and road condition information; the road condition information includes: current road gradient, road adhesion coefficient, and road rolling resistance coefficient; and a determination module 42 for determining a target torque based on the real-time status data; the target torque is used to adjust the vehicle from the current speed to the desired speed.

[0262] In some embodiments, the acquisition module 41 is used to acquire the desired vehicle speed, including: acquiring the desired vehicle speed to be processed output by the domain controller based on the current driving demand; performing limitation and filtering processing on the desired vehicle speed to be processed to acquire the desired vehicle speed.

[0263] In some embodiments, the acquisition module 41 is used to acquire the current vehicle speed, including: acquiring the motor speed, wheel radius and vehicle speed ratio; acquiring the current vehicle speed to be processed based on the motor speed, wheel radius and vehicle speed ratio; and performing filtering processing on the current vehicle speed to be processed to acquire the current vehicle speed.

[0264] In some embodiments, the acquisition module 41 is used to acquire real-time estimated mass, including: acquiring vehicle driving status data; the driving status data includes the current gear command, current road slope, current acceleration, current vehicle speed, and current driving torque of the motor; based on the driving status data, when it is determined that the mass estimation trigger condition is met, continue to acquire the road rolling resistance coefficient; based on the current vehicle speed, current acceleration, current road slope, road rolling resistance coefficient, and current driving torque, determine the estimated mass.

[0265] In some embodiments, the acquisition module 41 is used to acquire the current road slope, including: acquiring the current road slope determined by the domain controller; wherein the domain controller determines the current road slope of the current location of the vehicle based on the current location of the vehicle and prior road slope map information; and / or, the domain controller determines the current road slope based on the slope information collected by the slope sensor installed on the vehicle.

[0266] In some embodiments, the acquisition module 41 is used to acquire the road adhesion coefficient and the road rolling resistance coefficient, including: acquiring the road adhesion coefficient and the road rolling resistance coefficient determined by the domain controller; wherein the domain controller identifies the material of the current road and the dryness and wetness of the road surface based on the image acquisition sensor, and matches the corresponding road adhesion coefficient and road rolling resistance coefficient.

[0267] In some embodiments, the determining module 42 is used to determine the target torque based on real-time state data, including: generating acceleration and deceleration control parameters based on the current vehicle speed and the desired vehicle speed using vehicle speed difference processing and PID control; obtaining the motor demand torque based on the acceleration and deceleration control parameters, the desired vehicle speed, the current vehicle speed, the real-time estimated mass, the current road slope, and the road rolling resistance coefficient using vehicle dynamics equations; and limiting and filtering the motor demand torque to obtain the target torque.

[0268] In some embodiments, the determining module 42 is used to generate acceleration and deceleration control parameters based on the current vehicle speed and the desired vehicle speed, using vehicle speed difference processing and PID control, including: determining the real-time vehicle speed difference based on the current vehicle speed and the desired vehicle speed, combined with the vehicle operating mode and the vehicle speed difference threshold; generating a corresponding integral zeroing instruction when the integral zeroing condition of PID control is met; adjusting and optimizing the PID control parameters based on four factors to obtain the target proportional parameter, the target integral parameter, and the target derivative parameter; the four factors include: the difference between the desired vehicle speed and the real-time vehicle speed, the real-time estimated mass, the current road gradient, and the road adhesion coefficient; and generating acceleration and deceleration control parameters using a PID control algorithm based on at least the real-time vehicle speed difference, the integral zeroing instruction, the target proportional parameter, the target integral parameter, and the target derivative parameter.

[0269] In some embodiments, the determining module 42 is used to correct the proportional parameter in the PID control before performing parameter adjustment and optimization of the PID control, and when the expected vehicle speed and the actual vehicle speed meet the correction conditions.

[0270] In some embodiments, the correction conditions include: the desired vehicle speed is greater than a vehicle speed threshold; and the current vehicle speed is less than the desired vehicle speed, and the cumulative duration of the speed being below the vehicle speed threshold is greater than a preset duration.

[0271] In some embodiments, the determining module 42 is used to determine the real-time vehicle speed difference based on the current vehicle speed and the desired vehicle speed, combined with the vehicle operating mode and the vehicle speed difference threshold, including: when the vehicle operating mode is a speed operation mode, a non-autonomous driving operation mode, or a speed control non-preparation state under autonomous driving mode, the real-time vehicle speed difference is 0; when the vehicle is in a speed control preparation state under autonomous driving mode, if the direction of the current vehicle speed is the same as the direction of the desired vehicle speed, the real-time vehicle speed difference is equal to the desired vehicle speed minus the current vehicle speed; if the direction of the current vehicle speed is opposite to the direction of the desired vehicle speed, the real-time vehicle speed difference is equal to the desired vehicle speed plus the current vehicle speed; and all of these conditions are met by the real-time vehicle speed difference being less than or equal to the vehicle speed difference threshold.

[0272] In some embodiments, the integral zeroing condition includes at least one of the following conditions: Condition 1: when the vehicle is switching between automatic and manual modes; Condition 2: the real-time vehicle speed difference is greater than zero and the acceleration / deceleration control parameter is less than zero; or the real-time vehicle speed difference is less than zero and the acceleration / deceleration control parameter is greater than zero; Condition 3: a braking pressure command is generated; Condition 4: a gear command is switched; Condition 5: the motor operating mode is switched; Condition 6: the vehicle's ready state changes.

[0273] In some embodiments, the determining module 42 is used to perform parameter adjustment and optimization, including proportional parameter adjustment and optimization, integral parameter adjustment and optimization, and derivative parameter adjustment and optimization.

[0274] In some embodiments, the determining module 42 is used to perform proportional parameter adjustment optimization, including: proportional parameter adjustment optimization based on the difference between the desired vehicle speed and the real-time vehicle speed; wherein the proportional parameter is directly proportional to the desired vehicle speed and the proportional parameter is directly proportional to the real-time vehicle speed difference; proportional parameter adjustment optimization based on the real-time estimated quality; wherein the proportional parameter is directly proportional to the real-time estimated quality; proportional parameter adjustment optimization based on the current road slope; wherein the proportional parameter is directly proportional to the current road slope; and proportional parameter adjustment optimization based on the road adhesion coefficient; wherein the proportional parameter is inversely proportional to the road adhesion coefficient.

[0275] In some embodiments, the determining module 42 is used to perform integral parameter adjustment and optimization, including: integral parameter adjustment and optimization based on the difference between the desired vehicle speed and the real-time vehicle speed; integral parameter adjustment and optimization based on the real-time estimated mass; integral parameter adjustment and optimization based on the current road slope; and integral parameter adjustment and optimization based on the road adhesion coefficient.

[0276] In some embodiments, the determining module 42 is used to perform differential parameter adjustment optimization, including: differential parameter adjustment optimization based on the difference between the desired vehicle speed and the real-time vehicle speed; differential parameter adjustment optimization based on the real-time estimated mass; differential parameter adjustment optimization based on the current road slope; and differential parameter adjustment optimization based on the road adhesion coefficient.

[0277] In some embodiments, the determining module 42 is used to obtain the required motor torque using vehicle dynamics equations, including:

[0278] Calculate the required torque of the motor using the following formula:

[0279]

[0280] Where T represents the required torque of the motor, ρ represents the air density, A represents the frontal area, and C represents the frontal area. D δ represents the air resistance coefficient, v represents the current vehicle speed, f represents the road rolling resistance coefficient, m represents the real-time estimated mass, g represents the gravity coefficient, i represents the current road slope, δ represents the vehicle rotational mass conversion factor after taking into account the rotational mass inertia torque, a represents the acceleration / deceleration control parameter, r represents the effective radius of the vehicle's tires, K represents the vehicle speed ratio, and η represents the transmission mechanical efficiency.

[0281] In some embodiments, the determining module 42 is used to limit and filter the motor demand torque to obtain a target torque, including: limiting the motor demand torque based on the vehicle's maximum available torque; the maximum available torque is determined based on the state of charge of the power battery in the vehicle, the allowable charging and discharging power of the power battery, the external characteristic torque of the motor, the driving and feedback torque limits of the motor, and the fault conditions of the whole vehicle; and filtering the motor demand torque obtained after limiting to obtain the target torque.

[0282] In some embodiments, the device further includes an output module for transmitting a target torque to a controlled component, thereby adjusting the vehicle from its current speed to a desired speed based on the controlled component.

[0283] It should be noted that, Figure 5 The apparatus shown can perform the steps of any of the methods provided in the above embodiments and achieve the corresponding beneficial effects.

[0284] This disclosure also provides a computer-readable storage medium storing a computer program for performing the steps of any of the methods provided in the above embodiments, which has corresponding beneficial effects.

[0285] This disclosure also provides an automotive device. (See reference...) Figure 6 The vehicle device 40 includes: a processor 420; a memory 410 for storing executable instructions of the processor 420; the processor 420 is used to read executable instructions from the memory 410 and execute the executable instructions to implement the steps of any of the methods provided in the above embodiments, and has corresponding beneficial effects.

[0286] This disclosure also provides a vehicle that includes any of the vehicle-mounted devices provided in the above embodiments, and has corresponding beneficial effects.

[0287] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0288] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the speed of an autonomous vehicle, characterized in that, include: Obtain real-time vehicle status data; The real-time status data includes real-time estimated mass, current vehicle speed, expected vehicle speed, and road condition information. The road condition information includes: current road gradient, road adhesion coefficient, and road rolling resistance coefficient; Based on the real-time status data, the target torque is determined; The target torque is used to adjust the vehicle from the current speed to the desired speed. Based on the real-time status data, the target torque is determined, including: Based on the current vehicle speed and the desired vehicle speed, acceleration and deceleration control parameters are generated using vehicle speed difference processing and PID control. The target torque is obtained based on the acceleration / deceleration control parameters and the real-time status data; Specifically, based on the current vehicle speed and the desired vehicle speed, acceleration / deceleration control parameters are generated using speed difference processing and PID control, including: Based on the current vehicle speed and the desired vehicle speed, and in combination with the vehicle operating mode and the vehicle speed difference threshold, the real-time vehicle speed difference is determined. When the integral clearing condition of PID control is met, a corresponding integral clearing instruction is generated; Based on at least the real-time vehicle speed difference, the parameters of the PID control are adjusted and optimized to obtain the target proportional parameter, the target integral parameter, and the target derivative parameter. Based on at least the real-time vehicle speed difference, the integral clearing command, the target proportional parameter, the target integral parameter, and the target derivative parameter, the acceleration and deceleration control parameters are generated using a PID control algorithm. The step of determining the real-time speed difference based on the current vehicle speed and the desired vehicle speed, combined with the vehicle operating mode and the speed difference threshold, includes: When the vehicle is in a speed-controlled operation mode, a non-automatic operation mode, or an automatic operation mode where speed control is not ready, the real-time vehicle speed difference is 0. When the vehicle is in the speed control preparation state in autonomous driving mode, if the direction of the current vehicle speed is the same as the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed minus the current vehicle speed; if the direction of the current vehicle speed is opposite to the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed plus the current vehicle speed; and in both cases, the real-time speed difference is less than or equal to the speed difference threshold.

2. The method according to claim 1, characterized in that, Obtaining the desired vehicle speed includes: Obtain the desired vehicle speed to be processed, output by the domain controller based on the current driving requirements; The desired vehicle speed is obtained by limiting and filtering the desired vehicle speed.

3. The method according to claim 1, characterized in that, Obtaining the current vehicle speed includes: Obtain motor speed, wheel radius, and vehicle speed ratio; Based on the motor speed, the wheel radius, and the vehicle speed ratio, the current vehicle speed to be processed is obtained; The current vehicle speed to be processed is filtered to obtain the current vehicle speed.

4. The method according to claim 1, characterized in that, Obtaining the real-time estimated quality includes: Acquire vehicle driving status data; the driving status data includes the current gear command, current road gradient, current acceleration, current vehicle speed, and current driving torque of the motor; Based on the driving status data, if the mass estimation trigger condition is met, the road rolling resistance coefficient is further obtained. 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 according to claim 1, characterized in that, Obtaining the current road slope includes: obtaining the current road slope determined by the domain controller; wherein the domain controller determines the current road slope of the vehicle's current location based on the vehicle's current location and prior road slope map information; and / or, the domain controller determines the current road slope based on slope information collected by a slope sensor mounted on the vehicle. Obtaining the road adhesion coefficient and the road rolling resistance coefficient includes: obtaining the road adhesion coefficient and the road rolling resistance coefficient determined by the domain controller; wherein the domain controller identifies the material of the current road and the dryness and wetness of the road surface based on the image acquisition sensor, and matches the corresponding road adhesion coefficient and road rolling resistance coefficient.

6. The method according to any one of claims 1-5, characterized in that, Based on the acceleration / deceleration control parameters and the real-time status data, the target torque is obtained, including: Based on the acceleration / deceleration control parameters, the desired vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient, and the road rolling resistance coefficient, the required motor torque is obtained using the vehicle dynamics equations. The required torque of the motor is limited and filtered to obtain the target torque.

7. The method according to claim 6, characterized in that, Based on at least the aforementioned real-time vehicle speed difference, the parameters of the PID control are adjusted and optimized, including: Based on four factors, the parameters of PID control are adjusted and optimized to obtain the target proportional parameter, target integral parameter, and target derivative parameter. The four factors include: the difference between the expected vehicle speed and the real-time vehicle speed, the real-time estimated mass, the current road gradient, and the road adhesion coefficient.

8. The method according to claim 7, characterized in that, Before the parameter adjustment and optimization for PID control, the method further includes: When the desired vehicle speed and the actual vehicle speed meet the correction conditions, the proportional parameter in the PID control is corrected.

9. The method according to claim 8, characterized in that, The correction conditions include: The desired vehicle speed is greater than the vehicle speed threshold; and The current vehicle speed is less than the desired vehicle speed, and the cumulative duration of the speed being below the speed threshold is greater than the preset duration.

10. The method according to claim 7, characterized in that, The condition for clearing the integral includes at least one of the following conditions: Condition 1: When the vehicle is switching between automatic and manual modes; Condition 2: The real-time speed difference is greater than zero, and the acceleration / deceleration control parameter is less than zero; or the real-time speed difference is less than zero, and the acceleration / deceleration control parameter is greater than zero. Condition 3: Braking pressure command is issued; Condition 4: Gear shift command; Condition 5: Motor operating mode switching; Condition 6: Change in vehicle readiness status.

11. The method according to claim 7, characterized in that, The parameter adjustment and optimization includes proportional parameter adjustment and optimization, integral parameter adjustment and optimization, and derivative parameter adjustment and optimization; The adjustment and optimization of the proportional parameters include: The optimization is based on the proportional parameter adjustment of the difference between the desired vehicle speed and the real-time vehicle speed; wherein, the proportional parameter is directly proportional to the desired vehicle speed and directly proportional to the difference between the real-time vehicle speed. The optimization is based on the adjustment of the proportional parameter of the real-time estimated quality; where the proportional parameter is directly proportional to the real-time estimated quality. The proportional parameters are adjusted and optimized based on the current road slope; the proportional parameters are directly proportional to the current road slope. And optimization of proportional parameters based on road adhesion coefficient; wherein, the proportional parameters are inversely proportional to the road adhesion coefficient; The integration parameter adjustment and optimization includes: Optimize the integral parameters based on the difference between the desired vehicle speed and the real-time vehicle speed; Optimization of integral parameters based on real-time quality estimation; Optimize integral parameters based on the current road slope; And optimization of integral parameters based on road adhesion coefficient; The differential parameter adjustment and optimization includes: Optimization based on differential parameter adjustment of the difference between desired vehicle speed and real-time vehicle speed; Optimization based on real-time quality estimation of differential parameters; Optimize the differential parameters based on the current road slope; And optimization of differential parameters based on road adhesion coefficient.

12. The method according to claim 6, characterized in that, The process of obtaining the required torque for the motor using vehicle dynamics equations includes: Calculate the required torque of the motor using the following formula: Where T represents the required torque of the motor. ρ Represents air density, A Represents the windward area, C D Represents the air drag coefficient. v Represents the current vehicle speed. f Represents the road rolling resistance coefficient. m Represents real-time quality estimation. g Represents the gravity coefficient. i This represents the current road gradient. This represents the conversion factor for the vehicle's rotational mass after taking into account the moment of inertia caused by rotational mass. a Represents acceleration / deceleration control parameters. r Represents the effective radius of the vehicle's tires. K Represents the vehicle's speed ratio. It represents the efficiency of the transmission machinery.

13. The method according to claim 6, characterized in that, To obtain the target torque by limiting and filtering the required torque of the motor, the process includes: The required torque of the motor is limited based on the maximum available torque of the vehicle; the maximum available torque is determined based on the state of charge of the power battery in the vehicle, the allowable charging and discharging power of the power battery, the external characteristic torque of the motor, the driving and regenerative torque limits of the motor, and the fault conditions of the whole vehicle. The motor torque requirement obtained after the limitation is filtered to obtain the target torque.

14. The method according to claim 1, characterized in that, Also includes: The target torque is transmitted to a controlled component, which then adjusts the vehicle from the current speed to the desired speed.

15. A speed control device for an autonomous vehicle, characterized in that, include: The acquisition module is used to acquire real-time status data of the vehicle; The real-time status data includes real-time estimated mass, current vehicle speed, expected vehicle speed, and road condition information. The road condition information includes: current road gradient, road adhesion coefficient, and road rolling resistance coefficient; The determination module is used to determine the target torque based on the real-time status data; The target torque is used to adjust the vehicle from the current speed to the desired speed. The determining module is specifically used for: determining the real-time vehicle speed difference based on the current vehicle speed and the desired vehicle speed, combined with the vehicle operating mode and the vehicle speed difference threshold; generating a corresponding integral zeroing instruction when the integral zeroing condition of PID control is met; adjusting and optimizing the parameters of PID control based on at least the real-time vehicle speed difference to obtain the target proportional parameter, the target integral parameter, and the target derivative parameter; generating acceleration and deceleration control parameters using a PID control algorithm based on at least the real-time vehicle speed difference, the integral zeroing instruction, the target proportional parameter, the target integral parameter, and the target derivative parameter; and obtaining the target torque based on the acceleration and deceleration control parameters and the real-time status data. The determination of the real-time speed difference based on the current vehicle speed and the desired vehicle speed, combined with the vehicle operating mode and the speed difference threshold, includes: when the vehicle operating mode is a speed-controlled operating mode, a non-autonomous driving operating mode, or a speed control not-ready state under autonomous driving mode, the real-time speed difference is 0; when the vehicle is in a speed control ready state under autonomous driving mode, if the direction of the current vehicle speed is the same as the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed minus the current vehicle speed; if the direction of the current vehicle speed is opposite to the direction of the desired vehicle speed, the real-time speed difference is equal to the desired vehicle speed plus the current vehicle speed; and in all cases, the real-time speed difference is less than or equal to the speed difference threshold.

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 as described in any one of claims 1-14.

17. A vehicle-mounted device, characterized in that, include: Memory used to store processor-executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of the method as described in any one of claims 1-14.

18. A vehicle, characterized in that, Includes the vehicle equipment as described in claim 17.

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