A vehicle ride comfort optimization control system based on road condition prediction

Abstract

The application relates to the technical field of automobile chassis cooperative control, and discloses a vehicle ride comfort optimization control system based on road condition prediction, which comprises a road condition sensing and prediction module, a whole vehicle state acquisition module, a cooperative ride comfort optimization control unit and an execution layer system comprising a steering system, a braking system and a suspension system; the application realizes the timing prediction of the front road surface excitation through multi-source information fusion, takes the timing road condition excitation as a feedforward input, takes the real-time state of the vehicle as a feedback correction, constructs a multi-system cooperative optimization model with the vehicle ride comfort as an optimization target, solves the cooperative optimal control instructions of the three chassis systems, realizes the feedforward and feedback compound closed-loop control, and solves the technical problems of the ride comfort control lag, the single-system optimization bottleneck, and the disconnection between the multi-system cooperation and the ride comfort target in the prior art, greatly improves the vehicle driving and riding comfort under all working conditions, and simultaneously considers the driving safety and the handling stability.

Description

Technical Field

[0001] This invention relates to the field of automotive chassis cooperative control technology, and in particular to a vehicle ride comfort optimization control system based on road condition prediction. Background Technology

[0002] With the development of automotive intelligence, users' demands for vehicle ride comfort continue to rise, and vehicle ride smoothness has become a core indicator for measuring chassis performance. Vehicle ride smoothness is jointly determined by the coupling characteristics of the three major systems: steering, braking, and suspension. The coordinated matching of these three systems is the core of achieving ride smoothness optimization under all operating conditions.

[0003] In existing technologies, single-system feedback control schemes can only passively correct after the vehicle body has vibrated, resulting in inherent control lag and an inability to suppress road excitation at its source. Single-system feedforward schemes based on road condition recognition solve the lag problem, but only optimize the suspension system and do not consider the dynamic coupling characteristics of the three major systems, resulting in significant bottlenecks in ride comfort optimization. Multi-system collaborative schemes mostly focus on handling stability as the core objective, treating ride comfort as a secondary optimization indicator, and do not build a dedicated collaborative optimization architecture for ride comfort, thus failing to meet the comfort requirements of daily driving conditions. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a vehicle ride comfort optimization control system based on road condition prediction.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: including a road condition perception and prediction module, a vehicle status acquisition module, a cooperative smoothness optimization control unit, and an execution layer system; The road condition perception and prediction module is used to acquire road information within a preset detection range ahead of the vehicle, make temporal predictions on the road surface ahead, generate a temporal road condition excitation sequence that matches the vehicle's future driving path, and output it to the cooperative smoothness optimization control unit. The vehicle status acquisition module is used to collect the current driving status parameters and body posture parameters of the vehicle in real time, and output them to the collaborative ride comfort optimization control unit. The cooperative ride comfort optimization control unit is communicatively connected to the road condition perception and prediction module, the vehicle state acquisition module, and the execution layer system, respectively. It is used to receive the time-series road condition excitation sequence and the current state parameters of the vehicle. With the time-series road condition excitation sequence as the feedforward input and the real-time state of the vehicle as the feedback correction, and with vehicle ride comfort as the optimization target, it solves the cooperative optimal control command of the steering, braking, and suspension systems in combination with the vehicle dynamics constraints, and distributes the control command to the corresponding execution layer system. The steering control system, braking control system, and suspension control system of the execution layer system are used to receive corresponding cooperative optimal control commands, execute corresponding actions, and feed back the real-time execution status to the cooperative smoothness optimization control unit.

[0006] As a further description of the above technical solution: The road condition perception and prediction module includes a multi-source perception unit, a spatiotemporal synchronous fusion unit, a road condition time sequence prediction unit, and a working condition classification unit. The multi-source sensing unit is used to acquire multi-source raw road data within a preset detection range in front of the vehicle; The spatiotemporal synchronization fusion unit is used to perform time and space synchronization calibration on the original road data from multiple sources, complete the multi-source data fusion processing, and obtain the fused road surface feature parameters and road attribute parameters. The road condition time-series prediction unit is used to predict the vehicle's future driving trajectory within a preset time domain based on the fused parameters, combined with the vehicle's current driving state and preset driving path, and generate a time-series road condition excitation sequence that matches the driving trajectory. The condition classification unit is used to classify and identify the driving conditions ahead based on the time-series road condition excitation sequence, generate condition classification results, and output them to the cooperative ride comfort optimization control unit.

[0007] As a further description of the above technical solution: The spatiotemporal synchronization fusion unit uses a spatiotemporal synchronization calibration model to unify multi-source heterogeneous original road data to the same time reference and vehicle centroid spatial coordinate system, thereby eliminating time and spatial coordinate deviations of multi-source data.

[0008] As a further description of the above technical solution: The time-series road condition excitation sequence includes the road surface unevenness level, road surface elevation change, road curvature, road slope, and road obstacle location parameters corresponding to each discrete time step in the future preset time domain; the driving conditions identified by the working condition classification unit include speed bump driving conditions, continuous uneven road surface conditions, curve driving conditions, slope driving conditions, and emergency obstacle avoidance conditions.

[0009] As a further description of the above technical solution: The collaborative smoothness optimization and control unit includes a timing matching subunit, a multi-system collaborative optimization subunit, and an instruction allocation and closed-loop correction subunit. The timing matching subunit is used to receive the timing-sequential road condition excitation sequence, calculate the time nodes when the vehicle arrives at each road feature point ahead by combining the current driving state of the vehicle, match the step response characteristics of each execution layer system, and generate a pre-control trigger timing sequence. The multi-system collaborative optimization subunit has a built-in vehicle dynamics model and a multi-objective optimization solver. It is used to take the time-series road condition excitation sequence as the feedforward disturbance input, the vehicle ride comfort as the optimization objective, and combine the vehicle safety boundary and actuator physical limit constraints to solve for the collaborative optimal control command. The instruction allocation and closed-loop correction subunit is used to distribute the cooperative optimal control instructions to the corresponding execution layer system according to the pre-control triggering sequence, and at the same time, correct the control instructions in real time based on the execution status feedback.

[0010] As a further description of the above technical solution: The multi-system collaborative optimization subunit takes the weighted minimum value of vehicle body vertical acceleration, pitch angle acceleration, roll angle acceleration, and wheel dynamic load as the optimization objective, and obtains the optimal solution of control parameters for steering, braking, and suspension systems through a multi-objective optimization solver.

[0011] As a further description of the above technical solution: The timing matching subunit calculates the pre-control trigger time by combining the response characteristics of the execution system with the pre-control timing matching model, ensuring that each execution system completes state adjustment before the vehicle contacts the road surface excitation.

[0012] As a further description of the above technical solution: The control parameters of the steering control system include the active steering compensation angle and the steering transmission ratio; the control parameters of the braking control system include the single-wheel braking torque and the front and rear axle braking force distribution coefficient; the control parameters of the suspension control system include the suspension damping coefficient, the suspension stiffness, and the vehicle height.

[0013] As a further description of the above technical solution: The multi-source perception unit includes an in-vehicle environment perception subunit, a vehicle-road cooperative communication subunit, and a high-precision map and positioning subunit; the in-vehicle environment perception subunit includes at least one of lidar, millimeter-wave radar, and visual camera.

[0014] The present invention has the following beneficial effects: 1. In this invention, a composite control architecture of time-sequential road condition feedforward and vehicle state feedback is constructed. By accurately predicting the road surface excitation in time, the three major systems are pre-adjusted in advance before the vehicle comes into contact with the road surface excitation. This solves the problem of lag in traditional feedback control from the root, realizes active suppression of road surface excitation rather than passive correction, and improves the smoothness optimization effect.

[0015] 2. In this invention, with vehicle ride comfort as the core optimization objective, a collaborative optimization architecture for the three major systems of steering, braking, and suspension is constructed. Considering the dynamic coupling characteristics of the three major systems, the optimal matching of control parameters is achieved for different operating conditions. Through the coordinated cooperation of the braking system pre-adjusting vehicle speed, the suspension system pre-adjusting stiffness and damping, and the steering system stabilizing the driving direction, the vibration and pitch of the vehicle body are minimized, thus solving the problem of the disconnect between multi-system coordination and ride comfort objectives in the prior art.

[0016] 3. In this invention, the spatiotemporal synchronous fusion of multi-source sensing data enables accurate prediction of road conditions beyond visual range. At the same time, the pre-control timing design that matches the response characteristics of the execution system ensures the accuracy of feedforward control. With smoothness as the core optimization target and vehicle safety boundary and actuator physical limit as hard constraints, the invention maximizes smoothness while strictly ensuring vehicle driving safety and handling stability, avoiding the technical defects of sacrificing safety in pursuit of smoothness. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure in this invention; Figure 2 This is a schematic diagram of the internal structure of the road condition perception and prediction module in this invention; Figure 3 This is a schematic diagram of the internal structure of the cooperative smoothness optimization control unit in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To address the problems mentioned in the background art, this application presents a vehicle ride comfort optimization control system based on road condition prediction, including a road condition perception and prediction module, a vehicle status acquisition module, a cooperative ride comfort optimization control unit, and an execution layer system; wherein the execution layer system includes the vehicle's steering control system, braking control system, and suspension control system.

[0020] To better understand the working process of a vehicle ride comfort optimization control system based on road condition prediction according to an embodiment of this application, refer to... Figure 1 - Figure 3The following is a specific embodiment: The core function of the road condition perception and prediction module is to realize the multi-source acquisition, fusion processing and temporal prediction of road information ahead. Its internal architecture is shown in the figure, including a multi-source perception unit, a spatiotemporal synchronous fusion unit, a road condition temporal prediction unit and a working condition classification unit.

[0021] In this embodiment, the multi-source sensing unit integrates the vehicle environment sensing subunit, the vehicle-road cooperative communication subunit, and the high-precision map and positioning subunit. The preset detection range is adaptively adjusted according to the vehicle speed, ranging from 5m to 200m in front of the vehicle. The higher the vehicle speed, the larger the detection range.

[0022] The vehicle-mounted environmental perception subunit includes a 128-line lidar, a 77GHz millimeter-wave radar, and a binocular vision camera, used to collect 3D point clouds, images, and obstacle data of the road surface; the vehicle-road cooperative communication subunit is based on the 5G-V2X protocol and receives beyond-line-of-sight road information from roadside units and surrounding vehicles; the high-precision map and positioning subunit uses RTK+inertial navigation to achieve centimeter-level positioning and obtain prior information on the static attributes of the road ahead.

[0023] The spatiotemporal synchronization fusion unit is used to synchronize and calibrate multi-source heterogeneous data in terms of time and space dimensions, eliminating temporal and spatial coordinate deviations in the multi-source data. The formula is as follows: ; In the formula, The standard timestamp after synchronization For the timestamp of the sensor's raw data, For data transmission delay, This is the clock offset; The coordinates of the road surface feature points in the sensor coordinate system. Let be the external parameter matrix for rotation and translation from the sensor to the vehicle's center of mass coordinate system. These are the coordinates of the road surface feature points in the vehicle's centroid coordinate system.

[0024] By using the above formula, all multi-source data are unified to the same time reference and spatial coordinate system. Then, the data is fused by extended Kalman filtering to obtain the fused road surface feature parameters and road attribute parameters, which solves the problem of insufficient prediction accuracy caused by the heterogeneity of multi-source data.

[0025] Based on the fused parameters, the road condition timing prediction unit combines the vehicle's current driving status with the navigation preset path and uses the vehicle kinematics model to predict the vehicle's driving trajectory within the next 5 seconds, generating a timing-series road condition excitation sequence with a time step of 10ms. The sequence content includes the road surface unevenness level, road surface elevation change, road curvature, slope, and obstacle location parameters corresponding to each time step.

[0026] The driving condition classification unit classifies and identifies the driving conditions ahead based on the time-series road condition excitation sequence. In this embodiment, the driving conditions include speed bump driving conditions, continuous uneven road surface driving conditions, curve driving conditions, slope driving conditions, and emergency obstacle avoidance conditions. The driving condition classification results are then output to the cooperative ride comfort optimization control unit.

[0027] The vehicle status acquisition module is used to collect the current driving status parameters and body posture parameters of the vehicle in real time. In this embodiment, it includes a vehicle speed sensor, wheel speed sensor, six-axis IMU, steering wheel angle sensor, brake pedal opening sensor, and suspension travel sensor. All sensors have a sampling frequency of 100Hz, and the collected parameters are sent to the collaborative ride comfort optimization control unit in real time with a period of 10ms.

[0028] The driving status parameters include vehicle speed, longitudinal / lateral acceleration, yaw rate, steering wheel angle, and brake / accelerator pedal opening; the vehicle attitude parameters include vehicle roll / pitch angle, roll / pitch acceleration, vehicle vertical acceleration, and suspension travel.

[0029] The collaborative ride comfort optimization control unit is implemented using a vehicle chassis domain controller, with the Infineon TC397 as the main control chip, meeting the real-time control computing power requirements. Its internal architecture is as follows: Figure 3 As shown, it includes a timing matching subunit, a multi-system collaborative optimization subunit, and an instruction allocation and closed-loop correction subunit.

[0030] The timing matching subunit 31 is one of the core innovations in implementing feedforward control. It is used to calculate the time node when the vehicle arrives at the excitation feature point on the road ahead, match the step response characteristics of each execution system, and generate the pre-control trigger timing. The core pre-control timing matching formula is as follows: ; In the formula, To control the trigger time of the command, The time it takes for the vehicle to reach the excitation feature point on the road surface. To determine the step response time of the execution system, For safety margin.

[0031] In this embodiment, the pre-control lead time for the suspension system is 150ms, for the steering system it is 130ms, and for the braking system it is 100ms. This ensures that each actuator completes its state adjustment before the vehicle contacts the road surface, thus solving the problem of feedforward control failure caused by the lag in the response of the actuator.

[0032] The core invention of this invention is the multi-system collaborative optimization subunit, which incorporates a vehicle dynamics model and a multi-objective optimization solver based on model predictive control. It uses a time-series road condition excitation sequence as a feedforward disturbance input and vehicle ride comfort as the core optimization objective to solve for the collaborative optimal control command of the three systems.

[0033] In this embodiment, the core optimization objective function is as follows: In the formula, The vertical acceleration of the vehicle's center of mass. For pitch acceleration, This is the roll acceleration. This is the weighted value of the wheel dynamic load; The weighting coefficients are adaptively adjusted based on the working condition classification results. For example, the weights of vertical acceleration and pitch acceleration are increased in the case of speed bumps, and the weights of roll acceleration are increased in the case of curves.

[0034] The constraints for optimization include: hard constraints on actuator physical limits (such as steering angle range, braking torque range, suspension damping / stiffness range) and hard constraints on vehicle driving safety (such as longitudinal / lateral acceleration thresholds, body roll / pitch angle thresholds, and wheel slip ratio thresholds), ensuring that vehicle driving safety is strictly guaranteed while optimizing ride comfort.

[0035] The instruction distribution and closed-loop correction subunit is used to distribute the cooperative optimal control instruction to the corresponding execution system according to the pre-control triggering sequence, and at the same time receive the real-time execution status feedback of each execution system. Based on the sliding mode control law, the control instruction is corrected in real time to ensure accurate tracking of the control quantity and improve the system's anti-interference capability and robustness.

[0036] The execution layer system includes a steering control system, a braking control system, and a suspension control system. In this embodiment: The steering control system uses an active front wheel steering system (AFS) to receive control commands and adjust the active steering compensation angle and steering ratio to stabilize the driving direction and suppress steering disturbance transmission. The braking control system uses an electro-hydraulic brake-by-wire system (EHB) to receive control commands and independently control the braking torque of each wheel, pre-adjust vehicle speed and braking force distribution, and suppress vehicle pitch. The suspension control system uses an electronically controlled air suspension system, which receives control commands to adjust suspension damping, stiffness, and vehicle height, directly suppressing vehicle vibration caused by road excitation. It is the execution unit for ride comfort optimization.

[0037] The working process of this invention will be described in detail below, taking into account the driving conditions of speed bumps: The vehicle is traveling at a speed of 60 km / h. The road condition perception and prediction module identifies a speed bump 100m ahead. After spatiotemporal synchronization fusion processing, it predicts that the vehicle will reach the speed bump in 6 seconds. The condition is classified as speed bump passage condition and output to the cooperative smoothness optimization control unit.

[0038] The timing matching subunit calculates three control trigger nodes: 3s, 1s, and 0.5s before reaching the speed bump.

[0039] 3 seconds before arrival: The Co-optimization control unit sends a pre-braking command to the braking system to reduce the vehicle speed to 30km / h; sends a command to the steering system to lock the steering ratio; and sends a command to the suspension system to raise the vehicle height by 20mm to increase suspension stiffness and prevent the suspension from bottoming out.

[0040] 1 second before arrival: Send a command to the braking system to release the pre-braking and avoid the braking state from aggravating the vehicle pitch; send a command to the suspension system to adjust the front axle damping to the maximum value and the rear axle damping to the medium value to suppress vertical vibration and pitch in advance.

[0041] 0.5 seconds before arrival: Send instructions to the suspension system to optimize damping stiffness and match the timing of the front wheels contacting the speed bump; send instructions to the steering system to actively compensate for steering fluctuations caused by road impacts and prevent vibrations from being transmitted to the steering wheel.

[0042] When a vehicle passes over a speed bump, the system uses real-time vehicle status feedback to correct suspension parameters in a closed loop. The system works together to minimize vehicle vibration. With this system, the peak vertical acceleration and peak pitch acceleration of the vehicle body when passing over speed bumps are reduced, significantly improving ride comfort while fully meeting driving safety requirements.

[0043] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A vehicle ride comfort optimization control system based on road condition prediction, characterized in that, This includes a road condition perception and prediction module, a vehicle status acquisition module, a collaborative smoothness optimization control unit, and an execution layer system; The road condition perception and prediction module is used to acquire road information within a preset detection range ahead of the vehicle, make temporal predictions on the road surface ahead, generate a temporal road condition excitation sequence that matches the vehicle's future driving path, and output it to the cooperative smoothness optimization control unit. The vehicle status acquisition module is used to collect the current driving status parameters and body posture parameters of the vehicle in real time, and output them to the collaborative ride comfort optimization control unit. The cooperative ride comfort optimization control unit is communicatively connected to the road condition perception and prediction module, the vehicle state acquisition module, and the execution layer system, respectively. It is used to receive the time-series road condition excitation sequence and the current state parameters of the vehicle. With the time-series road condition excitation sequence as the feedforward input and the real-time state of the vehicle as the feedback correction, and with vehicle ride comfort as the optimization target, it solves the cooperative optimal control command of the steering, braking, and suspension systems in combination with the vehicle dynamics constraints, and distributes the control command to the corresponding execution layer system. The steering control system, braking control system, and suspension control system of the execution layer system are used to receive corresponding cooperative optimal control commands, execute corresponding actions, and feed back the real-time execution status to the cooperative smoothness optimization control unit.

2. The vehicle ride comfort optimization control system based on road condition prediction according to claim 1, characterized in that, The road condition perception and prediction module includes a multi-source perception unit, a spatiotemporal synchronous fusion unit, a road condition time sequence prediction unit, and a working condition classification unit. The multi-source sensing unit is used to acquire multi-source raw road data within a preset detection range in front of the vehicle; The spatiotemporal synchronization fusion unit is used to perform time and space synchronization calibration on the original road data from multiple sources, complete the multi-source data fusion processing, and obtain the fused road surface feature parameters and road attribute parameters. The road condition time-series prediction unit is used to predict the vehicle's future driving trajectory within a preset time domain based on the fused parameters, combined with the vehicle's current driving state and preset driving path, and generate a time-series road condition excitation sequence that matches the driving trajectory. The condition classification unit is used to classify and identify the driving conditions ahead based on the time-series road condition excitation sequence, generate condition classification results, and output them to the cooperative ride comfort optimization control unit.

3. The vehicle ride comfort optimization control system based on road condition prediction according to claim 2, characterized in that, The spatiotemporal synchronization fusion unit uses a spatiotemporal synchronization calibration model to unify multi-source heterogeneous original road data to the same time reference and vehicle centroid spatial coordinate system, thereby eliminating time and spatial coordinate deviations of multi-source data.

4. The vehicle ride comfort optimization control system based on road condition prediction according to claim 3, characterized in that, The time-series road condition excitation sequence includes the road surface unevenness level, road surface elevation change, road curvature, road slope, and road obstacle location parameters corresponding to each discrete time step in the future preset time domain; the driving conditions identified by the working condition classification unit include speed bump driving conditions, continuous uneven road surface conditions, curve driving conditions, slope driving conditions, and emergency obstacle avoidance conditions.

5. A vehicle ride comfort optimization control system based on road condition prediction according to claim 4, characterized in that, The collaborative smoothness optimization and control unit includes a timing matching subunit, a multi-system collaborative optimization subunit, and an instruction allocation and closed-loop correction subunit. The timing matching subunit is used to receive the timing-sequential road condition excitation sequence, calculate the time nodes when the vehicle arrives at each road feature point ahead by combining the current driving state of the vehicle, match the step response characteristics of each execution layer system, and generate a pre-control trigger timing sequence. The multi-system collaborative optimization subunit has a built-in vehicle dynamics model and a multi-objective optimization solver. It is used to take the time-series road condition excitation sequence as the feedforward disturbance input, the vehicle ride comfort as the optimization objective, and combine the vehicle safety boundary and actuator physical limit constraints to solve for the collaborative optimal control command. The instruction allocation and closed-loop correction subunit is used to distribute the cooperative optimal control instructions to the corresponding execution layer system according to the pre-control triggering sequence, and at the same time, correct the control instructions in real time based on the execution status feedback.

6. A vehicle ride comfort optimization control system based on road condition prediction according to claim 5, characterized in that, The multi-system collaborative optimization subunit takes the weighted minimum value of vehicle body vertical acceleration, pitch angle acceleration, roll angle acceleration, and wheel dynamic load as the optimization objective, and obtains the optimal solution of control parameters for steering, braking, and suspension systems through a multi-objective optimization solver.

7. A vehicle ride comfort optimization control system based on road condition prediction according to claim 6, characterized in that, The timing matching subunit calculates the pre-control trigger time by combining the response characteristics of the execution system with the pre-control timing matching model, ensuring that each execution system completes state adjustment before the vehicle contacts the road surface excitation.

8. A vehicle ride comfort optimization control system based on road condition prediction according to claim 7, characterized in that, The control parameters of the steering control system include the active steering compensation angle and the steering transmission ratio; the control parameters of the braking control system include the single-wheel braking torque and the front and rear axle braking force distribution coefficient; the control parameters of the suspension control system include the suspension damping coefficient, the suspension stiffness, and the vehicle height.

9. A vehicle ride comfort optimization control system based on road condition prediction according to claim 8, characterized in that, The multi-source perception unit includes an in-vehicle environment perception subunit, a vehicle-road cooperative communication subunit, and a high-precision map and positioning subunit; the in-vehicle environment perception subunit includes at least one of lidar, millimeter-wave radar, and visual camera.

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