Intelligent drive-by-wire chassis comprehensive control platform

Abstract

The invention discloses an intelligent drive-by-wire chassis comprehensive control platform, and belongs to the technical field of automobile chassis control. An intelligent drive-by-wire chassis comprehensive control platform is divided into an information interaction processing layer, a state information estimation layer, a control mode management layer, a comprehensive motion control layer and a drive-by-wire execution layer, so that the drive-by-wire chassis can generalize more intelligent capabilities; comprising environment information cognitive ability, chassis fault pre-judgment ability, real-time information interaction ability, vehicle stability autonomous control ability, minimum risk state automatic control ability and failure state autonomous adjustment ability. The chassis health state assessment is used for acquiring the execution capability and the comprehensive health degree of chassis components in real time, carrying out fault definition and division based on the information, and ensuring the implementation or degradation control of an expected target through failure reconstruction control; the minimum risk control module serves as a minimum intelligent driving backup, and it is guaranteed that after an intelligent driving system fails or exceeds a design operation domain, the vehicle can achieve basic decision making, planning and control for guaranteeing safety of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile chassis control, and more specifically to an intelligent wire-controlled chassis integrated control platform. Background Art

[0002] Intelligence, electrification and lightweight are the three major development trends of automobile chassis, and are also necessary conditions for achieving higher-level intelligent driving, which has led to explosive growth in intelligent wire-controlled chassis. Among them, electrification is reflected in the degree of wire control of battery electric drive, braking, steering and suspension; while intelligence requires the chassis to be able to adapt to more intelligent functional scenarios, which requires the chassis to be able to achieve active control, self-adaptation, self-learning and OTA upgrade capabilities, which puts higher requirements on the intelligent wire-controlled chassis.

[0003] At present, the intelligence of intelligent chassis integrated control is mainly concentrated in horizontal and vertical intelligent collaborative control technology, active redundant control technology, active fault-tolerant control technology, intelligent trajectory tracking control technology and chassis intelligent fault diagnosis technology. For example: Publication No. CN112519793A discloses an intelligent wire-controlled chassis architecture based on digital twins and its active fault-tolerant method, CN117148704A discloses an intelligent chassis domain controller with hardware full redundant design, and CN115384528A discloses a centralized chassis domain control architecture and method. However, these control platforms fail to consider the overall situation, severing the coupling relationship between the subsystems and not having the continuous evolution capability of the entire platform.

[0004] In addition, the existing intelligent drive-by-wire chassis control cannot actively make emergency interventions in the face of higher-level intelligent driving failure conditions, which poses a great safety hazard. At the same time, the interaction between the existing drive-by-wire chassis platform and the intelligent driving domain is more one-way. The intelligent driving domain cannot obtain the chassis's execution capabilities and health status, and thus cannot make adjustments in advance, affecting the execution effect of the autonomous driving task. Summary of the invention

[0005] The present invention provides an integrated control platform for an intelligent drive-by-wire chassis to improve the control performance, safety and adaptability of the automobile chassis and meet the requirements of higher-level intelligent driving.

[0006] The present invention adopts the following technical solution: An intelligent wire-controlled chassis integrated control platform, comprising an information interaction processing layer, a state information estimation layer, a control mode management layer, an integrated motion control layer and a wire-controlled execution layer; The information interaction processing layer is used to receive driver operation command information, intelligent driving system related information, cloud control platform control instructions, vehicle sensor information and positioning information, and output vehicle status related information, road environment related information, driver related information to the status information estimation layer after processing, and output control command information to the control mode management layer, and also receive vehicle motion status information and chassis execution capability information output by the status information estimation layer, and feed back chassis execution capability boundary and chassis motion status to the upper perception decision layer; The state information estimation layer estimates the vehicle motion state, driving environment state, driver state and chassis health state using the acquired sensor information, and outputs the vehicle state information, road environment information, surrounding vehicle information, driver state information and fault information to the control mode management layer and the integrated motion control layer; The control mode management layer generates basic assisted driving control instructions, minimum risk control instructions and cross-system trajectory tracking control instructions, and determines the motion control instructions with priority response, and outputs them to the integrated motion control layer; the control mode management layer includes a basic assisted driving module, a minimum risk control module, a cross-system trajectory tracking module and a control mode switching module; The integrated motion control layer coordinates the operation of various chassis systems based on the input motion control command target and comprehensively considers the vehicle performance and failure conditions, and outputs control commands of the steering system, braking system, electric drive system and suspension system to the wire control execution layer for execution; The wire control execution layer is used to respond to the control request sent by the integrated motion control layer, and to feed back the fault information and execution capability of each chassis actuator to the information interaction processing layer.

[0007] In a preferred embodiment, the information interaction processing layer includes an input information processing module and an output information processing module. The input information processing module is used to receive vehicle-mounted sensor information, positioning information, actuator controller feedback information, cloud control platform output information, intelligent driving system output information and driver output information, and output relevant information to the state information prediction layer and the control mode management layer after information processing; the output information processing module is used to receive chassis motion state information, chassis fault information and chassis execution capability boundary information, and output dynamic constraint information, vehicle motion state and chassis fault information to the intelligent driving domain controller and the cockpit domain controller after dynamic calculation.

[0008] In a preferred embodiment, the above-mentioned information processing includes digital signal conversion, unit conversion, smoothing filtering, information fusion and validity judgment, wherein smoothing filtering adopts low-pass filtering, Kalman filtering or median filtering; information fusion is to fuse signals from multiple information sources to improve the accuracy of input information; validity judgment is to judge whether the input control instructions are reasonable and valid based on the dynamic model, and if they exceed the range, they will be limited.

[0009] In a preferred embodiment, the state information estimation layer includes a vehicle key parameter estimation module, a driving environment state observation module, a driver state observation module, a chassis health assessment module and a chassis fault diagnosis module. The vehicle key parameter estimation module uses a dynamic model, a kinematic model or an estimation algorithm to estimate the vehicle motion state that cannot be directly observed by the vehicle; the driving environment state observation module mainly uses the perception information input by the intelligent driving system and the vehicle motion state estimated by the chassis to observe the road environment and surrounding obstacle information of the vehicle; the driver state observation module uses the monitored driver state information to judge the driver's fatigue level and driver's concentration, and recognizes the driver's intention according to the driver's operating instructions; the chassis health state assessment module is based on the runtime state data of the key components of the chassis, and evaluates the health and executable capabilities of each chassis component by building a chassis health assessment model; the chassis fault diagnosis module locates the fault by integrating the chassis health and the fault information fed back by each chassis actuator, and divides the fault level, fault type, remaining capacity of the faulty component and processing suggestions.

[0010] In a preferred implementation scheme, the basic assisted driving module implements lane keeping function and adaptive cruise function according to input lane line information, vehicle positioning information and surrounding vehicle information; in the event of failure of the intelligent driving system, the minimum risk control module uses perception information and chassis status information to make autonomous decisions and plan a reasonable and safe trajectory, and uses a trajectory tracking control algorithm to output control instructions; the cross-system trajectory tracking control module is used to respond to the planned trajectory issued by the intelligent driving system, and output control instructions through a trajectory tracking control algorithm; the control mode switching module determines the control instructions that take priority based on the current vehicle status, road environment, driver status, control instructions of each mode and requests of each mode, arranges the control instruction response order according to the control priority and control mode conditions, and outputs the control instructions that take priority.

[0011] Preferably, the above-mentioned self-lane parking will actively plan a longitudinal trajectory that avoids collision and takes comfort into consideration based on the target information in front of the self-lane; when parking by the side of the road, it will first determine whether the rightmost lane meets the parking conditions, and then plan a lane change trajectory with the center line of the rightmost lane as a reference line.

[0012] Specifically, the above-mentioned control modes include automatic driving mode, assisted driving mode, emergency mode, cloud control mode and manual driving mode. The mode switching logic is that when the automatic driving conditions are met, it is decided whether to execute emergency control instructions or automatic driving control instructions according to whether the intelligent driving domain controller fails; when the automatic driving conditions are not met, it is decided whether to execute assisted driving control instructions or manual driving control instructions according to whether the assisted driving conditions are met; no matter what mode is currently in, when the driver takes over, it is necessary to give priority to responding to manual driving control instructions.

[0013] In a preferred embodiment, the above-mentioned integrated motion control layer includes a chassis multi-dimensional dynamic reference model module, a motion tracking control module, a failure reconstruction control module and an optimal control allocation module. The chassis multi-dimensional dynamic reference model module is used to obtain a dynamic reference model of the vehicle chassis in three dimensions, namely, lateral, longitudinal and vertical, for motion tracking control and failure reconstruction control; the motion tracking control module is used to realize roll attitude tracking control, longitudinal motion tracking control, pitch attitude tracking control and yaw stability tracking control, and seeks the optimal torque distribution with the help of the optimal control algorithm to obtain control instructions for the wire steering control system, wire brake control system, active suspension control system and wire drive control system; the failure reconstruction control module is used to identify the faulty mechanism, and use the capacity range constraints of each actuator obtained according to the failure reconstruction strategy as the constraint conditions for the optimal control allocation; the optimal control allocation module comprehensively considers the actuator constraints and the required torque issued by the failure reconstruction control module to complete the braking torque distribution, driving force distribution, yaw torque distribution, anti-pitch torque distribution and anti-roll torque distribution.

[0014] In a preferred embodiment, the above-mentioned integrated motion control layer includes a chassis multi-dimensional dynamics reference model, a collaborative control module and a failure reconstruction control module, wherein the collaborative control module can directly build an optimization problem based on the required vehicle state output by the chassis multi-dimensional dynamics reference model, with the deviation between the current vehicle state and the required state as the optimization target, and use the constraints issued by the failure reconstruction control module as control quantity constraints, and receive the vehicle performance evaluation results issued by the state information evaluation module as a collaborative control trigger signal, and finally achieve the global optimal allocation of control quantities of each chassis control system by solving the target optimization problem.

[0015] In a preferred embodiment, the above-mentioned wire-controlled execution layer includes a wire-controlled braking control system and its actuator, a wire-controlled steering control system and its actuator, an active suspension control system and its actuator, and a wire-controlled drive control system and its actuator, wherein the wire-controlled braking control system and its actuator are used to respond to braking force requests; the wire-controlled steering control system and its actuator are used to respond to wheel angle requests; the wire-controlled suspension control system and its actuator are used to respond to vertical force requests; and the wire-controlled drive control system and its actuator respond to driving force requests and braking force requests.

[0016] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following advantages: 1. The present invention divides the functions and performs modular design on the intelligent drive-by-wire chassis platform based on a layered architecture, and divides the intelligent drive-by-wire chassis integrated control platform into an information interaction processing layer, a state information estimation layer, a control mode management layer, an integrated motion control layer, and a control-by-wire execution layer, so that the drive-by-wire chassis can generalize more intelligent capabilities, including environmental information recognition capability, chassis fault prediction capability, real-time information interaction capability, vehicle stability autonomous control capability, minimum risk state automatic control capability, and failure state autonomous adjustment capability. The hierarchical structure makes the division of labor between each layer clear, supports functional expansion, and realizes standardization and generalization.

[0017] 2. Greater safety: The chassis health status assessment obtains the execution capability and comprehensive health of chassis components in real time, and defines and divides faults based on this information. Through failure reconstruction control, it can quickly reconstruct when key components break down or fail, ensuring the achievement of expected goals or degradation control; the minimum risk control module serves as the minimum intelligent driving backup, ensuring that after the intelligent driving system fails or exceeds the designed operating domain, the vehicle can implement basic decision-making, planning and control to ensure its safety, so that the vehicle is in a safe state. The chassis structure design with multiple redundant backups greatly improves the safety performance of the chassis.

[0018] 3. Higher degree of intelligence: Perception information sharing greatly broadens the environmental adaptability of the intelligent chassis. The state information prediction layer can realize vehicle-to-vehicle and vehicle-road state recognition and environmental perception, provide strong support for vehicle dynamic control in all scenarios, and improve the vehicle's intelligence level; the comprehensive motion control layer is based on vehicle state recognition and environmental perception results, and realizes multi-dimensional collaborative control for dynamic adjustment of target weights in all scenarios and all working conditions, adaptively adjusts and manages vehicle motion status, and broadens the motion boundary; the control mode management layer combines input information, real-time supervision and allocation of chassis control rights, realizes multi-mode and multi-channel chassis intelligent control, and ensures smooth transition and safe response of the entire control process; by moving the application layer function of the wire control execution component upward and integrating it into the intelligent wire control chassis platform, the intelligence level of the wire control component can be further improved based on platform information, while realizing function iteration and remote upgrade. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a structural block diagram of embodiment 1 of the present invention.

[0020] Figure 2 This is a structural block diagram of the information interaction processing layer of Example 1 of the present invention.

[0021] Figure 3 This is a logic diagram for evaluating the chassis health status according to the first embodiment of the present invention.

[0022] Figure 4 This is a minimum risk control flow chart of an embodiment of the present invention.

[0023] Figure 5 This is a control model switching flow chart of embodiment 1 of the present invention. DETAILED DESCRIPTION

[0024] The specific implementation of the present invention is described below with reference to the accompanying drawings. In order to fully understand the present invention, many details are described below, but for those skilled in the art, the present invention can be implemented without these details. For well-known components, methods and processes, they are not described in detail below.

[0025] Embodiment 1 An intelligent wire-controlled chassis integrated control platform, referring to Figure 1 , including information interaction processing layer 1, state information estimation layer 2, control mode management layer 3, integrated motion control layer 4 and wire control execution layer 5. Information interaction processing layer 1 is used to receive driver operation command information, intelligent driving system related information, cloud control platform control command, vehicle sensor information and positioning information, and output vehicle state related information, road environment related information, driver related information to the state information estimation layer and control command information to the control mode management layer after processing. It also receives vehicle motion state information and chassis execution capability information output by the state information estimation layer, and feeds back chassis execution capability boundary and chassis motion state to the upper perception decision layer.

[0026] like Figure 2As shown, the information interaction processing layer 1 includes an input information processing module 11 and an output information processing module 12. The input information processing module 11 is used to receive vehicle-mounted sensor information, positioning information, actuator controller feedback information, cloud control platform output information, intelligent driving system output information and driver output information, and output relevant information to the state information prediction layer and the control mode management layer after information processing. The output information processing module 12 is used to receive chassis motion state information, chassis fault information and chassis execution capability boundary information, and output dynamic constraint information, vehicle motion state and chassis fault information to the intelligent driving domain controller and the cockpit domain controller after dynamic calculation. The intelligent driving domain can apply dynamic constraint information and vehicle motion state information to trajectory planning and trajectory tracking, and the cockpit domain can display chassis fault information on the driving screen.

[0027] The input information processing module 11 pre-processes the input information, including digital signal conversion, unit conversion, smoothing filtering, information fusion and validity judgment. Smoothing filtering can adopt common filtering methods such as low-pass filtering, Kalman filtering or median filtering; information fusion is to fuse signals from multiple information sources to improve the accuracy of input information; validity judgment is to judge whether the input control instruction is reasonable and effective based on the dynamic model. If it exceeds the range, it will be subject to certain limiting processing.

[0028] The vehicle-mounted sensor information input to the information processing module 11 includes wheel speed sensor information, steering wheel angle|torque sensor information, accelerator pedal position sensor information, brake pedal position information, wheel-side motor torque sensor information, brake pressure sensor information, tire pressure sensor information, IMU inertial navigation sensor information, camera information, millimeter wave radar information, laser radar information, etc. Among them, radar information and camera information are output to the information interaction processing layer after being processed by the perception fusion of the intelligent driving system, and the IMU inertial navigation sensor is used to obtain the position and attitude information of the vehicle, including the attitude angle, acceleration and angular velocity information of the three axes of the vehicle.

[0029] The actuator controller feedback information of the input information processing module 11 includes steering mechanism fault information and execution status, braking mechanism fault information and execution status, suspension mechanism fault information and execution status, and battery electric drive system fault information and execution status. Among them, the battery electric drive system information includes the maximum energy recovery capacity, the maximum braking capacity that the motor can provide, battery and motor fault information, battery status, and motor execution status.

[0030] The intelligent driving system output information of the input information processing module 11 includes the decision-making planning controller state, intelligent driving lateral and longitudinal control instructions (expected acceleration and expected front wheel turning angle), perception fusion information (surrounding dynamic and static obstacle information), lane line information, road information, navigation map and positioning fusion information and planning trajectory information, etc.

[0031] The driver output information of the input information processing module 11 includes driver operation instruction information and driver perception camera information, wherein the driver perception camera information is used to identify the driver's posture information and judge the driver's takeover ability.

[0032] The output information processing module 12 uses the obtained dynamic model parameters, driving environment status information, driver status information and chassis executable capability boundaries to solve the dynamic model and obtain corresponding dynamic boundary conditions, including longitudinal acceleration and deceleration boundaries, lateral acceleration and deceleration boundaries and yaw angular velocity boundaries, etc., which are used for trajectory planning and tracking control of the intelligent driving system, and forward the chassis motion status information (vehicle speed, acceleration, yaw angular velocity, roll angle, etc.) to the intelligent driving system; at the same time, the obtained chassis fault information and driver fatigue level information are forwarded to the cockpit domain for fault information display and early warning, where the chassis fault information includes steering system, braking system, suspension system, electric drive system and sensor fault information.

[0033] The state information prediction layer 2 is used to estimate and predict the vehicle motion state, driver state, driving environment and chassis abnormality based on the received rich sensor information and other domain information, and output the results to the comprehensive motion control layer and the control mode management layer for vehicle cooperative control, failure reconstruction control, basic auxiliary driving control, cross-system trajectory tracking control and minimum risk control. The state information prediction layer 2 includes a vehicle key parameter estimation module 21, a driving environment state observation module 22, a driver state observation module 23, a chassis health assessment module 24 and a chassis fault diagnosis module 25.

[0034] Among them, the vehicle key parameter estimation module 21 can estimate the chassis motion state information in real time based on the received sensor information and dynamic model, using parameter estimation algorithms such as least squares method and particle filtering, wherein the dynamic model includes tire model, suspension dynamics model, vehicle force analysis model, steering dynamics model, etc., and the chassis motion state information includes vehicle lateral, longitudinal and vertical speeds, vehicle lateral, longitudinal and vertical accelerations, yaw angular velocity, roll angle, front wheel turning angle, center of mass roll angle, driving and braking force, lateral force, etc.; the chassis dynamic parameters can also be dynamically updated through online parameter identification, and the chassis dynamic parameters include vehicle mass, tire lateral stiffness, suspension damping and stiffness, moment of inertia, etc.; the estimated vehicle motion state parameters can also be used to analyze and evaluate chassis performance, and chassis performance includes handling stability, power, economy and comfort, etc. The handling stability assessment can be carried out based on the fuzzy comprehensive evaluation model according to the information such as roll angle, yaw angular velocity, sideslip angle and lateral acceleration; the comfort assessment can be carried out based on the lateral and vertical acceleration, longitudinal acceleration and longitudinal jerk; the power economy can be comprehensively analyzed and evaluated based on the information such as driving force, braking force and energy recovery efficiency; the smoothness can be evaluated by vertical acceleration and suspension dynamic deflection.

[0035] The driving environment state observation module 22 analyzes and observes the vehicle's environment based on the received vehicle motion state information, dynamic parameters, surrounding obstacle information, navigation map and positioning fusion information, lane line information, road information and other related information, and uses the observed environmental information for active collaborative control, so that the chassis has the ability to predict. Among them, the driving environment state observation module can estimate the road adhesion coefficient based on the tire rotation radius, vehicle speed and tire rolling angular velocity; use the information after sensor fusion to judge and output the road information such as the road slope, road bumps, road roughness, road curvature, road congestion within a certain range in front of the vehicle; can judge the collision risk target according to the collision risk assessment logic; can obtain the type of road ahead, the intersection ahead, the status of the traffic light ahead, the traffic sign ahead and other information based on the navigation map and positioning information.

[0036] The driver status observation module 23 is to evaluate the driver's fatigue level and identify the driver's intention by integrating the driver's camera information and vehicle status information, and use the results to perform early warning and control mode switching. The driver's camera information refers to the collected driver's facial information, and the driver's fatigue level is judged by facial feature recognition technology; the vehicle status information includes steering wheel angle and torque, vehicle acceleration, vehicle speed, vehicle line-crossing information, and distance from the front vehicle, etc. The steering wheel angle and vehicle acceleration can be used to directly judge whether the driver is active, and the driver's attention is distracted by logic such as whether the vehicle is speeding, whether it crosses the line, and whether the distance to the front vehicle is too close. Among them, the driver's intention recognition is to make a comprehensive judgment based on the monitored driver's action signal, vehicle motion state information, surrounding vehicle information and road information to complete the recognition of the driver's intention. The driver's intention includes lane change overtaking intention, braking intention, acceleration intention and steering intention.

[0037] like Figure 3 As shown, the chassis health assessment module 24 uses the running status fed back by the chassis components to build a chassis component health status assessment model to assess the health status or executable capability of each component, and outputs the health status of each component to the fault diagnosis module for fault arbitration. Among them, the motion status fed back by each component includes information such as equipment execution, efficiency, voltage, current, temperature or pressure. The construction of the chassis component health status assessment model can be built using a data-driven method. Here, the chassis component health status assessment model is built based on the hidden Markov model. First, the sensor data of each component throughout its life cycle is collected, and then the collected sensor information is feature extracted and dimension reduced. The processed data is used as the training data of the hidden Markov model for offline training. Finally, the sensor information collected at each moment is input into the trained hidden Markov model to obtain the health status of each component; the current performance of each component can also be predicted by establishing a digital twin model that simulates the performance degradation of each chassis component, and the prediction results are compared with the real-time monitoring information of each component to evaluate the health status of each chassis component.

[0038] The chassis fault diagnosis module 25 analyzes and judges the chassis fault information fed back by the controllers of various chassis components, the execution status of each component, and the health status information of each component issued by the chassis health status assessment module, and classifies the fault severity, and outputs fault information such as fault level, fault location, remaining execution capability of the fault component, and processing suggestions to the failure reconstruction control module and the output signal processing module for failure control and screen display. Among them, the fault level is divided into three levels. The first level fault means that the fault severity is low, which does not affect the execution capability of the component and no warning is issued; the second level fault means that the fault severity is moderate, and the component still has a certain execution capability, and a warning is required; the third level fault means that the fault severity is high, and the component basically loses its execution capability, and the component is immediately shut down and a warning is issued.

[0039] The control mode management layer 3 is mainly used to generate assisted driving control instructions, backup emergency control instructions and backup automatic driving control instructions, and comprehensively consider vehicle information, environmental information and driver information to decide the current priority response control instructions. The control mode management layer 3 includes a basic assisted driving module 31, a minimum risk control module 32, a cross-system trajectory tracking control module 33 and a control mode switching module 34.

[0040] Among them, the basic assisted driving module 31 can be used to realize assisted driving functions such as adaptive cruise control, lane keeping function and emergency braking function, receive information such as surrounding dynamic and static obstacles, lane line information and self-vehicle posture information, and the adaptive cruise control takes the vehicle in front of the self-lane or the cruising speed as the target, and makes decisions and plans the required acceleration control instructions; the lane keeping function takes the center line of the lane as the target, and uses the tracking control algorithm to output the required front wheel angle control instructions; the emergency braking function uses the predicted routes of pedestrians, bicycles, motorcycles and vehicles and the predicted route of the self-vehicle to perform collision risk assessment, determine whether to trigger emergency braking and output the required deceleration instruction.

[0041] like Figure 4As shown, the minimum risk control module 32 is an emergency control behavior triggered under the failure condition of the intelligent driving domain. It can actively decide and plan the self-lane parking trajectory or the side parking trajectory, and use the tracking control algorithm to realize the horizontal and vertical tracking of the trajectory, and output the expected deceleration command and the expected front wheel steering angle control command. Among them, the self-lane parking will actively plan the longitudinal trajectory that avoids collision and takes comfort into consideration according to the target information in front of the self-lane; the side parking will give priority to judging whether the rightmost lane meets the parking conditions (such as whether there are other vehicles in the rightmost lane interfering with the self-vehicle, whether the lane line is stable, etc.), and then plan the lane change trajectory with the center line of the rightmost lane as the reference line (you can use a fitting curve such as a quintic polynomial curve or a Bezier curve to generate multiple lateral trajectories, consider the lateral acceleration, lateral acceleration and collision to calculate the cost values ​​of multiple trajectories, and select the lane change trajectory based on the optimal cost).

[0042] The cross-system trajectory tracking control module 33 receives the planned trajectory information issued by the intelligent driving system, and uses the tracking control algorithm to output the expected acceleration control instructions and the expected front wheel steering control instructions.

[0043] like Figure 5 As shown, the control mode switching module 34 determines the control instructions that need to be responded to first at the current moment based on the current vehicle state, road environment, driver state, various mode control instructions, and various mode requests, and needs to make good control instruction transitions when the control mode is switched. The control modes include automatic driving mode, assisted driving mode, emergency mode, cloud control mode, and manual driving mode. The mode switching logic is that when the automatic driving conditions are met, it is determined whether to execute emergency control instructions or automatic driving control instructions based on whether the intelligent driving domain controller fails; when the automatic driving conditions are not met, it is determined whether to execute assisted driving control instructions or manual driving control instructions based on whether the assisted driving conditions are met; no matter what mode is currently in, when the driver takes over, it is necessary to respond to manual driving control instructions first.

[0044] The above-mentioned integrated motion control layer 4 is mainly used to realize the coordinated control between various components based on the input motion control instructions and chassis fault information, taking into account vehicle performance such as stability, comfort, economy, handling and reliability, and adopting a multi-objective optimal control method to output each wheel vertical force control instruction, front and rear wheel angle control instruction, each wheel braking force control instruction and driving force control instruction, so as to achieve better chassis performance. The integrated motion control layer 4 includes a chassis multi-dimensional dynamics reference model module 41, a failure reconstruction control module 42 and a motion tracking control module 43.

[0045] Among them, the chassis multi-dimensional dynamics reference model 41 uses a dynamic model covering three dimensions of horizontal, vertical and vertical to convert the input control instructions (desired front wheel steering instructions and desired acceleration and deceleration instructions) into desired vehicle motion states such as roll angle, pitch angle, yaw angular velocity, center of mass roll angle and longitudinal acceleration.

[0046] The motion tracking control module 43 tracks the pitch angle, roll angle, yaw angular velocity, center of mass sideslip angle and longitudinal acceleration through roll attitude tracking control, pitch attitude tracking control, yaw stability tracking control and longitudinal motion tracking control, and outputs the anti-roll demand torque, anti-pitch demand torque, additional yaw demand torque, driving demand torque and braking demand torque to the optimal control distribution. This module receives the expected yaw rate, expected sideslip angle of the center of mass, expected pitch angle, expected roll angle and expected acceleration and other vehicle states output by the chassis multi-dimensional dynamics reference model, takes the deviation between the expected vehicle state and the current vehicle state as input, and calls the roll attitude tracking control, pitch attitude tracking control, yaw stability tracking control and longitudinal motion tracking control to calculate the required torque. The roll attitude tracking control combines the roll angle deviation and yaw motion compensation to calculate the anti-roll torque. The pitch attitude tracking control combines the pitch angle deviation and longitudinal motion compensation to calculate the anti-pitch torque. The yaw stability tracking control calculates the additional yaw torque according to the yaw rate deviation and the sideslip angle deviation of the center of mass. The longitudinal motion tracking control calculates the driving torque and braking torque according to the acceleration deviation. The failure reconstruction control module 42 receives chassis fault information and designs a drive system failure control strategy, a steering system failure control strategy and a brake system failure control strategy to output the constraint range of each wheel driving and braking force and the steering angle constraint range of the front and rear wheels to the optimal control allocation module 44. The control strategy for drive system failure is to use other motors to continue to provide driving force when some motors fail, and to compensate for the yaw torque to avoid yaw instability. The failure conditions of the electric drive system are divided into single motor failure, coaxial dual motor failure, same-side dual motor failure, opposite-side dual motor failure, three-motor failure and all failure. Under different conditions, the driving force constraint range of each wheel needs to be output; the control strategy for steering system failure is to achieve differential steering by providing different longitudinal forces on the left and right sides when the steering system fails. At this time, the constraint range of the front and rear wheel angles will be output; the control strategy for braking system failure is to use other wheel-end brakes and motor braking to continue to provide braking force when some wheel-end brakes fail, and it is necessary to compensate for the yaw torque. The failure conditions of the wire control brake system are divided into single brake failure, coaxial dual brake failure, same-side dual brake failure, opposite-side dual brake failure, three brake failure and all brake failure. Among them, when all brakes fail, the braking force is provided by the motor braking, and the braking force constraint range of each wheel-end brake is output.

[0047] The optimal control distribution module 44 comprehensively considers the actuator constraints and required torque issued by the failure reconstruction control module to complete the distribution of braking torque, driving force, yaw moment, anti-pitch moment and anti-roll moment. Based on the multi-optimization objectives of vehicle handling, stability, economy and comfort, the distribution of each required torque is completed by constructing and solving the optimization problem, and the anti-roll moment and anti-pitch moment are distributed to the active suspension control system for execution, the additional yaw moment is distributed to the wire steering control system, the wire brake control system and the wire drive control system for execution, the driving torque is distributed to the wire drive control system for execution, and the braking torque is distributed to the wire brake control system and the wire drive control system for execution, and finally the required vertical force of each wheel-end active suspension, the required braking force of each wheel-end braking system, the required driving force of each wheel-end drive system and the required turning angle of the front and rear wheels are output.

[0048] The above-mentioned wire control execution layer 5 is used to respond to the control requests issued by the integrated motion control layer 4, including the wire control steering control system 51 and its actuator, the wire control braking control system 52 and its actuator, the active suspension control system 53 and its actuator and the wire control drive control system 54 and its actuator. The control requests include braking force requests, driving force requests, vertical force requests and front and rear wheel angle requests.

[0049] Among them, the wire-controlled steering control system is used to respond to wheel angle control requests, output steering motor torque and additional direction angle requests to the steering motor for execution. In addition, it can also expand active emergency avoidance control (AES), feel control, road condition adaptation, load adaptation and four-wheel angle control capabilities.

[0050] The brake-by-wire control system is used to respond to braking force requests and output wheel-end braking torque to the brake actuator for execution. In addition, it can also expand capabilities such as emergency braking, deceleration control, parking braking and active anti-lock braking.

[0051] The active suspension control system is used to respond to vertical force requests and output wheel-end damping to the suspension actuator for execution. In addition, it can also expand capabilities such as road surface adaptive damping control, road surface adaptive stiffness control and road surface adaptive vehicle height control.

[0052] The drive-by-wire control system is used to respond to braking force requests and driving force requests, output wheel-end motor driving torque and braking torque to the drive motor for execution. In addition, it can also expand capabilities such as drive anti-skid control.

[0053] Embodiment 2 The difference between this embodiment and the first embodiment lies in the composition of the integrated motion control layer. The integrated motion control layer of this embodiment includes a chassis multi-dimensional dynamics reference model, a collaborative control module, and a failure reconstruction control module. Among them, the chassis multi-dimensional dynamics reference model and the failure reconstruction control module are the same as those of the first embodiment. The collaborative control module can directly build an optimization problem based on the required vehicle state (roll angle, pitch angle, yaw angular velocity, center of mass roll angle, and longitudinal acceleration, etc.) output by the chassis multi-dimensional dynamics reference model, with the deviation between the current vehicle state and the required state as the optimization target, and use the constraints issued by the failure reconstruction control module as the control quantity constraints, and receive the vehicle performance evaluation results issued by the state information evaluation module as the collaborative control trigger signal. Finally, the global optimal allocation of the control quantities of each chassis control system is achieved by solving the target optimization problem.

[0054] The collaborative control module includes stability collaborative control, comfort collaborative control, smoothness collaborative control and longitudinal motion collaborative control. Among them, stability collaborative control involves lateral-longitudinal-vertical coordination, with the vehicle's yaw angular velocity, center of mass sideslip angle and roll angle as the representation of the vehicle's yaw stability and roll stability and set as the optimization target. At the same time, the control amount of each wire control subsystem is considered as the optimization target, and the constraints consider the vehicle state constraints and the actuator output constraints and their change constraints. The solver is called to solve the above optimization problems to obtain the optimal steering angle request of the front and rear wheels, the optimal driving force request and the optimal braking force request of each wheel, and the optimal vertical force request of each wheel.

[0055] The coordination between the longitudinal motion cooperative control design braking system and the electric drive system is mainly electro-hydraulic composite braking control. The vehicle's economy can be optimized to a large extent through the optimal allocation of motor braking and hydraulic braking. Among them, the electro-hydraulic composite braking control takes longitudinal acceleration and energy recovery efficiency as optimization targets, constrains the actuator output and vehicle state, and obtains the most economical braking force distribution by solving the above optimization problems.

[0056] Comfort cooperative control involves longitudinal-vertical coordination. Through active suspension and wire-controlled brake systems, the pitch motion of the vehicle can be optimized to a large extent to improve comfort. Comfort cooperative control takes pitch angle and longitudinal acceleration as optimization targets, and converts tire normal force, suspension and tire dynamic deflection into suspension control quantity constraints, and then solves to obtain the optimal control quantity of active suspension control system and wire-controlled brake system.

[0057] The smoothness cooperative control designs the longitudinal-vertical coordination, and can optimize the vehicle vibration and improve the smoothness of the vehicle through the active suspension and wire-controlled brake system. The smoothness cooperative control establishes a multi-objective optimization function with suspension dynamic deflection, vertical acceleration and longitudinal acceleration as optimization targets, and obtains the optimal control quantity of the active suspension control system and wire-controlled brake system by solving the optimization problem.

[0058] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. An intelligent wire-controlled chassis integrated control platform, characterized by: It includes information interaction processing layer, state information estimation layer, control mode management layer, integrated motion control layer and wire control execution layer; The information interaction processing layer is used to receive driver operation command information, intelligent driving system related information, cloud control platform control instructions, vehicle sensor information and positioning information, and output vehicle status related information, road environment related information, driver related information to the status information estimation layer after processing, and output control command information to the control mode management layer, and also receive vehicle motion state information and chassis execution capability information output by the status information estimation layer, and feed back chassis execution capability boundary and chassis motion state to the upper perception decision layer; The state information estimation layer estimates the vehicle motion state, driving environment state, driver state and chassis health state using the acquired sensor information, and outputs vehicle state information, road environment information, surrounding vehicle information, driver state information and fault information to the control mode management layer and the integrated motion control layer; The control mode management layer generates basic assisted driving control instructions, minimum risk control instructions and cross-system trajectory tracking control instructions, and determines the motion control instructions with priority response, and outputs them to the integrated motion control layer; the control mode management layer includes a basic assisted driving module, a minimum risk control module, a cross-system trajectory tracking module and a control mode switching module; The integrated motion control layer coordinates the operation of various chassis systems based on the input motion control command target and comprehensively considers the vehicle performance and failure conditions, and outputs control commands of the steering system, braking system, electric drive system and suspension system to the wire control execution layer for execution; The control-by-wire execution layer is used to respond to the control request sent by the integrated motion control layer, and to feed back the fault information and execution capability of each actuator of the chassis to the information interaction processing layer.

2. The intelligent wire-controlled chassis integrated control platform according to claim 1, characterized in that: The information interaction processing layer includes an input information processing module and an output information processing module. The input information processing module is used to receive vehicle-mounted sensor information, positioning information, actuator controller feedback information, cloud control platform output information, intelligent driving system output information and driver output information, and output relevant information to the state information prediction layer and the control mode management layer after information processing; the output information processing module is used to receive chassis motion state information, chassis fault information and chassis execution capability boundary information, and output dynamic constraint information, vehicle motion state and chassis fault information to the intelligent driving domain controller and the cockpit domain controller after dynamic calculation.

3. The intelligent wire-controlled chassis integrated control platform as claimed in claim 2, characterized in that: The information processing includes digital signal conversion, unit conversion, smoothing filtering, information fusion and validity judgment, among which smoothing filtering adopts low-pass filtering, Kalman filtering or median filtering; information fusion is to fuse signals from multiple information sources to improve the accuracy of input information; validity judgment is to judge whether the input control instructions are reasonable and valid based on the dynamic model. If they exceed the range, they will be limited.

4. The intelligent wire-controlled chassis integrated control platform according to claim 1, characterized in that: The state information estimation layer includes a vehicle key parameter estimation module, a driving environment state observation module, a driver state observation module, a chassis health assessment module and a chassis fault diagnosis module. The vehicle key parameter estimation module uses a dynamic model, a kinematic model or an estimation algorithm to estimate the vehicle motion state that cannot be directly observed by the vehicle; the driving environment state observation module mainly uses the perception information input by the intelligent driving system and the vehicle motion state estimated by the chassis to observe the road environment and surrounding obstacle information of the vehicle; the driver state observation module uses the monitored driver state information to judge the driver's fatigue level and driver's concentration, and recognizes the driver's intention according to the driver's operating instructions; the chassis health state assessment module is based on the runtime state data of the key components of the chassis, and evaluates the health and executable capabilities of each chassis component by building a chassis health assessment model; The chassis fault diagnosis module locates the fault based on the chassis health and the fault information fed back by each chassis actuator, and classifies the fault level, fault type, remaining capacity of the faulty component and handling suggestions.

5. The intelligent wire-controlled chassis integrated control platform according to claim 1, characterized in that: The basic assisted driving module realizes lane keeping function and adaptive cruise function according to the input lane line information, vehicle positioning information and surrounding vehicle information; when the intelligent driving system fails, the minimum risk control module uses the perception information and chassis status information to autonomously decide and plan a reasonable and safe parking trajectory in the own lane or pull-over parking trajectory, and uses the trajectory tracking control algorithm to output control instructions; the cross-system trajectory tracking control module is used to respond to the planned trajectory issued by the intelligent driving system, and output control instructions through the trajectory tracking control algorithm; the control mode switching module determines the control instructions of priority response based on the current vehicle status, road environment, driver status, each mode control instruction and each mode request, arranges the control instruction response order according to the control right priority and control mode conditions, and outputs the control instructions of priority response.

6. The intelligent wire-controlled chassis integrated control platform as claimed in claim 5, characterized in that: The self-lane parking will actively plan a longitudinal trajectory that avoids collision and takes comfort into consideration based on the target information in front of the self-lane; when parking by the side of the road, it will first determine whether the rightmost lane meets the parking conditions, and then plan a lane change trajectory with the center line of the rightmost lane as the reference line.

7. The intelligent wire-controlled chassis integrated control platform as claimed in claim 5, characterized in that: The control modes include automatic driving mode, assisted driving mode, emergency mode, cloud control mode and manual driving mode. The mode switching logic is that when the automatic driving conditions are met, whether the emergency control instructions or the automatic driving control instructions are executed is determined based on whether the intelligent driving domain controller fails; when the automatic driving conditions are not met, whether the assisted driving control instructions or the manual driving control instructions are executed is determined based on whether the assisted driving conditions are met; no matter what mode is currently in, manual driving control instructions must be responded to first when the driver takes over.

8. The intelligent wire-controlled chassis integrated control platform according to claim 1, characterized in that: The integrated motion control layer includes a chassis multi-dimensional dynamics reference model module, a motion tracking control module, a failure reconstruction control module and an optimal control allocation module. The chassis multi-dimensional dynamics reference model module is used to obtain a dynamics reference model of the vehicle chassis in three dimensions, namely, lateral, longitudinal and vertical, for motion tracking control and failure reconstruction control; the motion tracking control module is used to implement roll attitude tracking control, longitudinal motion tracking control, pitch attitude tracking control and yaw stability tracking control, and seeks the optimal torque distribution with the help of an optimal control algorithm to obtain control instructions for a wire-controlled steering control system, a wire-controlled brake control system, an active suspension control system and a wire-controlled drive control system; The failure reconstruction control module is used to identify the faulty mechanism and use the capacity range constraints of each actuator obtained according to the failure reconstruction strategy as the constraint conditions for the optimal control distribution; the optimal control distribution module comprehensively considers the actuator constraints and required torque issued by the failure reconstruction control module to complete the braking torque distribution, driving force distribution, yaw moment distribution, anti-pitch moment distribution and anti-roll moment distribution.

9. The intelligent wire-controlled chassis integrated control platform according to claim 1, characterized in that: The comprehensive motion control layer includes a chassis multi-dimensional dynamics reference model, a collaborative control module and a failure reconstruction control module, wherein the collaborative control module can directly build an optimization problem based on the required vehicle state output by the chassis multi-dimensional dynamics reference model, with the deviation between the current vehicle state and the required state as the optimization target, use the constraint conditions issued by the failure reconstruction control module as the control quantity constraint, receive the vehicle performance evaluation results issued by the state information evaluation module as the collaborative control trigger signal, and finally achieve the global optimal allocation of the control quantities of each chassis control-by-wire system by solving the target optimization problem.

10. The intelligent wire-controlled chassis integrated control platform according to claim 1, characterized in that: The wire-controlled execution layer includes a wire-controlled brake control system and its actuator, a wire-controlled steering control system and its actuator, an active suspension control system and its actuator, and a wire-controlled drive control system and its actuator. The wire-controlled brake control system and its actuator are used to respond to braking force requests; the wire-controlled steering control system and its actuator are used to respond to wheel angle requests; the wire-controlled suspension control system and its actuator are used to respond to vertical force requests; and the wire-controlled drive control system and its actuator respond to driving force requests and braking force requests.

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