Suspension protection method, controller, system, medium, program product and vehicle

Abstract

The invention relates to a suspension protection method, a controller, a system, a storage medium and a vehicle, and the method comprises the step of carrying out multi-stage protection control on a suspension system of the vehicle based on road condition information and vehicle motion state information. According to the technical scheme, the hierarchical protection strategy needing to be used is pre-judged according to the motion state information and the road condition information of the vehicle, execution of vehicle suspension protection is adjusted according to the impact load adaptability, complex driving environment and working condition changes can be dealt with, and stability control over the whole vehicle is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a suspension protection method, a controller, a system, a storage medium, a program product and a vehicle. BACKGROUND

[0002] At present, in the process of vehicle driving, the suspension system as a key component connecting the vehicle body and the wheels needs to bear the functions of buffering road impact, improving driving comfort, affecting vehicle control stability and safety.

[0003] In the related art, the suspension system is usually passively designed, mainly relying on material strength and rigidity to cope with impact load. In the face of sudden working conditions, the suspension system often cannot effectively distribute and transfer energy, and may cause irreversible damage to key components (such as steering knuckles, half shafts, etc.) due to exceeding the limit load, which seriously affects the safety and service life of the vehicle. SUMMARY

[0004] The embodiments of the present application provide a suspension protection method, a controller, a system, a storage medium, a program product and a vehicle to at least partially solve the above technical problems.

[0005] In order to achieve the above purpose, according to the first aspect of the present application, a suspension protection method is provided, comprising: based on the road condition information and the vehicle motion state information corresponding to the vehicle, performing multi-level protection control on the suspension system of the vehicle.

[0006] According to the second aspect of the present application, a computer readable storage medium is provided, which stores a computer program or instructions, and the computer program or instructions are executed by a processor to implement the steps of any of the methods provided by the embodiments of the present application.

[0007] According to the third aspect of the present application, a controller is also provided, which stores a computer program or instructions, and the computer program or instructions are executed by a processor to implement the steps of any of the methods provided by the embodiments of the present application.

[0008] According to the fourth aspect of the present application, a suspension protection system is also provided, which comprises the controller and a suspension device connected to the controller.

[0009] According to the fifth aspect of the present application, a computer program product is also provided, which comprises a computer program or instructions, and the computer program or instructions are executed by a processor to implement the steps of any of the methods provided by the embodiments of the present application.

[0010] According to the sixth aspect of the present application, a vehicle is also provided, which comprises the controller, or the suspension protection system, or executes the steps of any of the methods provided by the embodiments of the present application.

[0011] To sum up, through the technical solution, the motion state information and the road condition information of the vehicle are used to predictively determine the hierarchical protection strategy to be used, the execution of the vehicle suspension protection is adaptively adjusted according to the impact load, the complex driving environment and the working condition change can be coped with, and the stability control of the vehicle is facilitated.

[0012] Other features and advantages of the present application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0014] In order to more completely understand the present application and its beneficial effects, the following will be described in conjunction with the drawings, wherein the same reference numerals in the following description represent the same parts.

[0015] Figure 1 is a step flow chart of a suspension protection method provided in an exemplary embodiment of the present application;

[0016] Figure 2 is a logic diagram of a suspension protection method in an embodiment of the present application based on road condition data acquisition and fusion to perform multi-level protection;

[0017] Figure 3 is a logic diagram of a suspension protection method in an embodiment of the present application Figure 1 ;

[0018] Figure 4 is an interface display state diagram of a suspension protection method in an embodiment of the present application on the vehicle instrument panel;

[0019] Figure 5 is a logic diagram of a suspension protection method in an embodiment of the present application Figure 2 ;

[0020] Figure 6 is a logic diagram of a suspension protection method in an embodiment of the present application Figure 3 ;

[0021] Figure 7 is a logic diagram of a suspension protection method in an embodiment of the present application for cloud training of a dynamic model;

[0022] Figure 8Fig. 1 is a schematic diagram of a health state data interaction process of an electromagnetic damping unit in a suspension protection method according to an embodiment of the present application;

[0023] Figure 9 Fig. 2 is a schematic diagram of a packaging process of data from collection to transmission in a suspension protection method according to an embodiment of the present application;

[0024] Figure 10 Fig. 3 is a schematic diagram of a dynamic model cloud offline training logic in a suspension protection method according to an embodiment of the present application;

[0025] Figure 11 Fig. 4 is a structural schematic diagram of a suspension protection system according to an embodiment of the present application;

[0026] Figure 12 Fig. 5 is a schematic diagram of a vehicle architecture according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0028] In order to facilitate the understanding of the implementation provided by the embodiments of the present application, the application background of the suspension protection method provided by the embodiments of the present application is first described.

[0029] With the advancement of automobile intelligence and electrification, active safety technology has gradually become a research hotspot, especially in extreme working conditions (such as collision, overload), higher requirements are put forward for the protection ability of the suspension system. The traditional suspension system mainly relies on passive mechanical structures (such as springs and dampers) to absorb energy. In extreme working conditions of the vehicle, the traditional suspension system has exposed problems. When the vehicle encounters road step impact, the passive suspension cannot predict the load in advance, resulting in overload failure of the energy absorption device; the half shaft, steering knuckle and other components are plastically deformed after impact, with high maintenance cost; the service life of the energy absorption module cannot be quantitatively evaluated, and the user cannot replace it in time, which exists a safety hazard. Therefore, the traditional suspension lacks intelligent perception and active response ability to dynamic load, which leads to the key components being easily damaged due to overload, threatening the driving safety. Therefore, the "passive response" characteristics of the traditional suspension do not match the "dynamic protection requirements" of the complex working conditions.

[0030] Based on the problems mentioned in the foregoing background art, in the related art, a whole vehicle seven-degree-of-freedom suspension model and a vehicle four-wheel steering model are established, the rigid and flexible modes of the suspension are switched according to the real-time driving conditions of the vehicle, and the suspension parameter dynamic adjustment under multiple modes is realized based on the model predictive control (MPC) algorithm. In the rigid mode, the tire dynamic displacement and the vehicle body acceleration are comprehensively considered and combined with the road grade, and the "passive mode", "comfort mode", "safety mode" and "comprehensive mode 1" switching strategies are proposed; in the flexible mode, the "comprehensive mode 2" based on four-wheel steering is adopted, and the steady-state mode switching strategy is proposed. Although the related art can solve the "dynamic protection demand" to a certain extent, the related art still lacks dynamic learning ability for complex road conditions, resulting in large prediction error and insufficient load prediction accuracy; and the grading protection mechanism cannot be triggered quickly in the event of sudden overload, and the key components (such as half shafts) still face the risk of fracture; at the same time, the electromagnetic damping module is designed once, and the real-time monitoring of energy absorption and module life cannot be realized, and the maintenance cost is high. The intelligent suspension in the related art still cannot meet the safety requirements under complex conditions. In order to solve the above problems, in an embodiment of the present application, an intelligent suspension cooperative protection scheme based on active failure chain is provided, which intelligently predicts the load change by monitoring the road condition information and the vehicle motion state information in real time, automatically selects the best protection mode according to the load size, and realizes active safety protection.

[0031] The present application provides an intelligent suspension cooperative protection method, please refer to Figure 1 The suspension protection method provided by the embodiment of the present application includes step 100, which will be described in detail below.

[0032] The suspension protection method provided by the embodiment of the present application can be applied to various application scenarios. For example, electric vehicles, hybrid electric vehicles, autonomous vehicles, etc.

[0033] Step 100, based on the road condition information and the vehicle motion state information corresponding to the vehicle, the suspension system of the vehicle is controlled by multiple levels of protection.

[0034] Road condition information refers to data related to the surrounding road environment during vehicle driving. For example, road condition information can include road surface undulation, obstacle position, vehicle spacing, road friction coefficient, and other data related to the surrounding road environment during vehicle driving.

[0035] wherein, the road unevenness represents the vertical height variation of the road surface in front of the vehicle (such as potholes, speed bumps, bumpy road), which is used to predict the vertical impact force that the vehicle will soon suffer; the obstacle position represents the real-time position and distance of static / dynamic obstacles (such as vehicles, pedestrians, roadblocks) in front of or around the vehicle, which is used to determine whether emergency braking or avoidance is needed; the vehicle distance represents the real-time distance between the driven vehicle and the vehicle in front or on the side, which is used to evaluate the risk of collision; the road friction coefficient represents the friction characteristics between the road surface and the tire (such as wet and slippery, icy and snowy road), which is used to predict the risk of vehicle skidding.

[0036] The vehicle motion state information refers to data reflecting the driving condition of the vehicle itself. For example, the vehicle motion state information can include the vehicle speed, acceleration, vehicle body attitude, steering angle and steering rate, suspension travel, tire pressure and temperature, and other data related to the driving condition of the vehicle itself.

[0037] wherein, the vehicle speed represents the current driving speed of the vehicle, and the impact energy is larger when a collision occurs during high-speed driving; the acceleration represents the longitudinal (acceleration / braking) and lateral (steering) acceleration of the vehicle; the vehicle body attitude represents the pitch angle (forward and backward inclination), roll angle (left and right inclination), and yaw angle (rotation around the vertical axis) of the vehicle body; the steering angle and steering rate represent the steering wheel angle and steering speed, which are used to predict the steering intention of the vehicle; the suspension travel represents the compression / extension amount of the suspension spring; the tire pressure and temperature represent the internal air pressure and tread temperature of the tire.

[0038] In some embodiments, the vehicle motion state information can be dynamically obtained. For example, the electronic device of the vehicle can periodically obtain the vehicle motion state information during the driving of the vehicle. For example, the electronic device of the vehicle can periodically obtain the vehicle motion state information sent by at least one sensor. The at least one sensor includes but is not limited to a speed sensor, an acceleration sensor, a tire pressure sensor, a brake pedal sensor, an accelerator pedal sensor, etc.

[0039] In some embodiments, the road condition data during the driving of the vehicle can be dynamically obtained. For example, the electronic device of the vehicle can periodically obtain the road condition information during the driving of the vehicle. For example, the electronic device of the vehicle can periodically obtain the road condition information sent by at least one sensor. The at least one sensor includes but is not limited to a millimeter wave radar; the millimeter wave radar is used to capture the road unevenness, obstacle position and vehicle distance data in real time during the driving of the vehicle. For example, by configuring a high-precision millimeter wave radar (such as a working frequency band of 77-81 GHz), the road condition within a range of 200 meters in front of the vehicle is scanned in real time at a frequency of 10 Hz, the road unevenness, obstacle position and vehicle distance data are captured in real time, and the latest road condition data is collected and summarized every 100 ms, and the vertical height variation is quantified through a radar elevation map.

[0040] By the technical solution, the best protection mode is automatically selected by real-time monitoring of road condition information and vehicle motion state information, and multi-level protection of the vehicle suspension system is realized.

[0041] In some embodiments, based on the road condition information and the vehicle motion state information corresponding to the vehicle, multi-level protection control is performed on the suspension system of the vehicle, including:

[0042] The impact load is predicted by a dynamics model in combination with real-time road condition information and vehicle motion state information.

[0043] The suspension system of the vehicle is controlled at multiple levels according to the predicted impact load.

[0044] The dynamics model is a mathematical model for describing the forces and torques acting on the vehicle during motion, which can include the suspension system of the vehicle, the contact force between the tire and the ground, etc. By combining real-time road condition information (such as road roughness, obstacle position, etc.) and vehicle motion state information (such as vehicle speed, acceleration, vehicle attitude, etc.), the impact load that the vehicle will soon experience is quantitatively analyzed and predicted. Specifically, the dynamics model is based on real-time road condition information, road feature recognition and classification are performed by the dynamics model, and impact load distribution is calculated according to the classification results and vehicle motion state information to predict the impact load of the vehicle.

[0045] By the above scheme, the road condition information provides external environmental input during vehicle driving, and the vehicle motion information reflects the vehicle's own state. The two are fused through the dynamics model to form a complete impact load prediction logic, which provides a decision basis for multi-level protection control.

[0046] In some embodiments, real-time road condition information and vehicle motion state information during vehicle driving are collected by an intelligent chassis system, and a global environment and vehicle state model is generated by multi-source data fusion.

[0047] The global environment and vehicle state model integrates at least one of road condition information (obstacles, road roughness), vehicle motion state (speed, attitude), suspension load distribution (strain, pressure), and material health state, and can be used as an instruction basis for multi-level protection control of the suspension system of the vehicle.

[0048] In some embodiments, real-time road condition information during vehicle driving is collected by an intelligent chassis system, including:

[0049] Real-time road condition information during vehicle driving is collected by a laser radar set by an intelligent chassis system; wherein the real-time road condition information includes at least one of road roughness, obstacle position, and vehicle spacing.

[0050] In some embodiments, the vehicle motion state information during vehicle driving is collected by the intelligent chassis system, including:

[0051] The corresponding vehicle state data and vehicle motion data are collected by at least one sensor provided by the intelligent chassis system;

[0052] The collected vehicle state data and vehicle motion data are fused to generate vehicle motion state information representing a global environment and vehicle state model.

[0053] In some embodiments, the vehicle motion state information is collected by at least one sensor provided by the intelligent chassis system, including:

[0054] The vehicle motion data during vehicle motion is collected by a Micro-Electro-Mechanical Systems Inertial Measurement Unit (IMU) provided by the intelligent chassis system; wherein the vehicle motion data includes at least one of three-axis linear acceleration, three-axis angular velocity, and body attitude angle; and / or

[0055] The vehicle state data during vehicle motion is detected by a Fiber Optic Strain Gauge (FOF) provided by the intelligent chassis system; wherein the vehicle state data at least includes suspension strain distribution information; and / or

[0056] The vehicle state data during vehicle motion is detected by an Electrorheological Pressure Sensor Array (ERF) provided by the intelligent chassis system; wherein the vehicle state data includes at least one of suspension local pressure distribution information and suspension damping force; and / or

[0057] The vehicle state data during vehicle motion is detected by a material performance compensation sensor provided by the intelligent chassis system; wherein the vehicle state data at least includes at least one of suspension component material temperature, aging degree, and deformation information.

[0058] The laser radar is used to scan the road surface by emitting a laser beam to generate high-precision three-dimensional point cloud data of the vehicle's surrounding environment, and to detect the road surface undulations (such as potholes, speed bumps) and obstacle positions and three-dimensional contours (such as vehicles, pedestrians) in real time.

[0059] The vehicle motion state information at least includes three-axis linear acceleration, three-axis angular velocity, body attitude angle, suspension strain distribution, suspension local pressure distribution, suspension damping force, suspension component material temperature, aging degree, and deformation.

[0060] Inertial measurement instruments are used to measure the three-axis linear acceleration (X / Y / Z axis) and three-axis angular velocity (pitch, roll, yaw) of the vehicle in real time, and to calculate the body attitude angle (such as pitch angle, roll angle) in real time, to determine the vehicle motion state (such as emergency braking, over-bend roll), so as to obtain accurate vehicle kinematic parameters.

[0061] Optical fiber sensors are arranged on the surface of key suspension components, and distributed optical fiber strain gauges are used to monitor the strain changes on the surface of the suspension components in real time, to capture the stress state of the suspension in real time, to identify the load concentration area, and to prevent structural fatigue damage. For example, optical fibers are arranged along the length direction of the swing arm to monitor bending and torsional strain, and are arranged around the shock absorber cylinder to detect compression and tensile load.

[0062] The electrorheological fluid pressure sensing array is an intelligent sensor system combining electrorheological fluid material and pressure sensing, which can dynamically adjust the mechanical properties (such as viscosity, stiffness) of the material through an external electric field, while detecting the pressure distribution in real time. By arranging multiple sensors in an array, the pressure distribution of key suspension components (such as suspension swing arms) can be monitored in real time, and the damping force of the suspension can be dynamically adjusted through the electrorheological effect of the electrorheological material, and the local pressure distribution of the suspension system can be detected to provide real-time feedback on impact energy absorption efficiency.

[0063] The material performance compensation sensor is embedded in the key components (such as electromagnetic dampers) to monitor the temperature, deformation and aging degree of the material, dynamically compensate for the performance degradation of the material (such as the decrease of elastic modulus), and ensure the stability of long-term service performance.

[0064] Figure 2 The figure is a logic diagram of a suspension protection method based on road condition data collection and fusion in an embodiment of the present application, which shows the data flow of sensors such as millimeter wave radar and camera into the central processor, and is processed in cooperation with the vehicle motion parameters.

[0065] In some embodiments of the present specification, the intelligent chassis system realizes accurate perception and real-time response to complex working conditions by configuring multiple source sensors.

[0066] Please refer to Figure 3 , Figure 3 The figure is a logic diagram of a suspension protection method in an embodiment of the present application Figure 1 In some embodiments, the multi-level protection control of the suspension system of the vehicle according to the predicted impact load includes:

[0067] Determining the target protection control strategy required by the suspension system according to the predicted impact load;

[0068] Performing the target protection control strategy to protect the suspension system of the vehicle.

[0069] In some embodiments, in the case that the predicted impact load is lower than a first threshold L1, the target protection control strategy comprises a pneumatic emergency protection control strategy. Executing the target protection control strategy performs protection control on the suspension system of the vehicle, including:

[0070] Executing the pneumatic emergency protection control strategy adjusts the body posture and / or generates driver prompt information.

[0071] Wherein, the body posture represents the pitch angle (forward and backward inclination), roll angle (left and right inclination) and yaw angle (rotation around the vertical axis) of the vehicle body, and the adjustment of the body posture is achieved by controlling the change of the body height, suppressing the body pitch vibration, etc.

[0072] For example, the vehicle travels at 40 km / h on a continuous gravel road, and the pneumatic emergency system adjusts the body height once every 0.5 seconds, with a cumulative lifting amount of no more than 15 mm; the body pitch angle fluctuation is reduced by 30%, and the driving comfort is significantly improved.

[0073] In some embodiments, the driver prompt information includes generating yellow warning information on the vehicle instrument panel and controlling the vehicle steering wheel to vibrate slightly.

[0074] For example, a yellow warning information of "complex road conditions, please pay attention to speed control" is displayed on the vehicle instrument panel to prompt the driver of the potential risk, but no emergency operation is required. For another example, the steering wheel is controlled to vibrate slightly in a vibration mode of low frequency pulse (such as 2 Hz), lasting for 3 seconds, and the intensity is 20% of the maximum value, prompting the driver to concentrate and drive carefully.

[0075] By setting the driver prompt information with warning intensity matching the predicted impact load level, excessive interference with driving is avoided, while driving safety and stability are improved.

[0076] In some embodiments, in the case that the predicted impact load exceeds the first threshold L1 and is lower than a second threshold L2, the target protection control strategy comprises an electromagnetic damping buffer control strategy. Executing the target protection control strategy performs protection control on the suspension system of the vehicle, including:

[0077] Executing the electromagnetic damping buffer control strategy activates the electromagnetic damping components in the suspension system to dynamically absorb impact energy.

[0078] Wherein, the electromagnetic damping components are composed of electromagnetic damping buffer components and electromagnetic damping energy absorption components.

[0079] In some embodiments, executing the electromagnetic damping buffer control strategy activates the electromagnetic damping components in the suspension system, including:

[0080] The electromagnetic damping buffer control strategy is executed to activate the electromagnetic damping buffer component to dynamically respond to and buffer the load impact, and to activate the electromagnetic damping energy absorbing component to dynamically absorb the energy generated by the load impact.

[0081] Electromagnetic damping components generate controllable damping through electromagnetic effects, quickly responding to high-frequency vibrations at the initial stage of an impact. Under the electromagnetic damping protection mechanism, the components are activated to increase damping force within the instant of impact (e.g., within 10ms) to suppress severe body sway. The damping strength can also be adjusted in real time based on road conditions, for example, reducing stiffness on bumpy roads to improve comfort.

[0082] Electromagnetic damping absorbers convert impact kinetic energy into heat or other forms of energy through electromagnetic-mechanical coupling and dissipate it. Under the electromagnetic damping buffer protection mechanism, the electromagnetic damping absorber is activated to stably dissipate energy during the duration of the impact (such as long-wave vibration).

[0083] For example, when driving over a speed bump, the coil current of the electromagnetic damping buffer component rises to 5A within 5ms, and the damping force suddenly increases to 80N·s / m, reducing the body bounce amplitude (by 40%); the subsequent residual vibration dissipates energy through the eddy current disk of the electromagnetic damping energy-absorbing component, and the body shaking time is shortened to less than 1 second.

[0084] In some embodiments, executing the electromagnetic damping buffer control strategy further includes: evaluating the remaining life of the electromagnetic damping component.

[0085] The driver prompt information includes displaying prompt information on the vehicle instrument panel and / or playing prompt information through the vehicle audio system. The prompt information is used to describe the current percentage of remaining energy of the electromagnetic damping component and the remaining number of impacts that can be absorbed, so as to help the driver assess driving safety.

[0086] like Figure 4 As shown, Figure 4 This is a schematic diagram of a state display on a vehicle dashboard interface in a suspension protection method provided in this embodiment, in which the remaining life of an electromagnetic damping energy absorbing component is displayed on the vehicle dashboard interface.

[0087] In some embodiments, the remaining life of the electromagnetic damping component is assessed using a multi-field coupling damage assessment model.

[0088] Specifically, the multi-field coupling damage assessment model is defined by formula (1), and the service performance (i.e., remaining life) of the electromagnetic damping component is predicted based on the interaction of electromagnetic effect, temperature field and mechanical stress field.

[0089]

[0090] Among them, mi represents the stress ratio correlation coefficient, i.e., the damage accumulation characteristics of the material under different stress levels; β represents the temperature sensitivity coefficient, i.e., the influence of temperature change on the damage rate of the material; ΔW is the micro plastic work, and D represents the total damage value (D≥1 represents failure); N i represents the current stress cycle number; N f,i represents the failure cycle number under the corresponding stress level; T i represents the working temperature; T0 represents the reference temperature; ΔW represents the micro plastic work, i.e., the evolution of the microstructure inside the material; R represents the material constant.

[0091] In some embodiments, the driver prompt information is generated according to the evaluation result of the remaining life of the electromagnetic damping component.

[0092] The life prediction of the electromagnetic damping component is linked with the vehicle-mounted system, realizing real-time monitoring and visual display of the remaining life of the electromagnetic damping energy-absorbing module. Through data interaction with the vehicle-mounted system, the health state information of the electromagnetic damping unit can be directly fed back to the vehicle-mounted instrument, providing real-time life information for the user.

[0093] In some embodiments, in the case that the predicted impact load exceeds the first threshold L1 and is lower than the second threshold L2, further comprising: adjusting the suspension parameters according to real-time road condition information and vehicle load change information.

[0094] The suspension parameters include at least one of spring stiffness, shock absorber damping force, vehicle body height, and tilting stiffness.

[0095] By adjusting the spring stiffness, the softness and hardness of the suspension response to road impact can be controlled. By adjusting the shock absorber damping force, the energy absorption efficiency of the suspension during compression and rebound can be adjusted. By adjusting the vehicle body height, the ground clearance can be adjusted to improve passability or reduce wind resistance. By adjusting the vehicle body tilting stiffness, the left and right load distribution of the vehicle during turning can be balanced.

[0096] According to the real-time road condition information, the suspension parameters are adjusted, for example, when the vehicle is driving on a bumpy road, the spring stiffness is reduced to allow the suspension to absorb the impact more gently and reduce the body vibration; when the vehicle is driving on a curve, the outer suspension stiffness is increased to reduce the roll angle and improve the cornering stability; when the vehicle is driving at high speed in a straight line, the vehicle body height is reduced to reduce the wind resistance and improve the fuel economy.

[0097] According to the vehicle load change, the suspension parameters are adjusted, for example, when the vehicle is fully loaded with passengers (the load is increased), the spring stiffness is increased to prevent the suspension from being excessively compressed and keep the vehicle body level.

[0098] By the above scheme, the electromagnetic damping buffer protection is combined with suspension parameter adjustment, which can quickly respond to road conditions and load changes, and through multi-parameter collaborative optimization (such as stiffness, damping, height linkage), the limitations of single adjustment are avoided.

[0099] In some embodiments, in the case that the predicted impact load exceeds the second threshold L2, the target protection control strategy includes an active failure chain strategy; and performing the target protection control strategy includes:

[0100] Performing the active failure chain control strategy controls the active buckling component in the suspension system to protect the suspension system of the vehicle.

[0101] The active failure chain control strategy is a safety design that sacrifices non-critical components to protect the core components of the vehicle suspension system by actively triggering the controllable failure of specific components.

[0102] In some embodiments, the active buckling protection component includes a wedge-shaped energy guide slot module and an electromagnetic lock, wherein the wedge-shaped energy guide slot module is arranged on the surface of a suspension key component (such as a swing arm or a connecting rod); and the electromagnetic lock fixes the energy guide slot module at a preset position through electromagnetic attraction.

[0103] Performing the active failure chain control strategy controls the active buckling component in the suspension system to protect the suspension system of the vehicle, including:

[0104] Performing the active failure chain control strategy controls the electromagnetic lock to be powered off, so that the energy guide slot module is separated from the preset position to absorb and guide impact energy to protect the suspension key component.

[0105] It can be understood that, through the principle of electromagnetic attraction, the electromagnetic lock generates magnetic force when powered on to fix the protection component (such as the energy guide slot) at a preset position; after triggering the active failure strategy, the electromagnetic lock is powered off, the magnetic force disappears, and the protection component automatically separates from the preset position and enters a deformable state.

[0106] A wedge-shaped energy guide slot is designed on the surface of a suspension key component (such as a swing arm or a connecting rod); when the active failure strategy is triggered, the wedge-shaped energy guide slot preferentially deforms plastically or breaks, absorbing most of the impact energy; the deformation gradually collapses through the wedge-shaped slot structure, prolonging the impact action time and reducing the peak load; at the same time, the wedge-shaped structure transfers the remaining energy along a specific path (such as away from the drive half shaft) to a non-critical area.

[0107] In some embodiments of the present specification, the active failure chain control strategy realizes the active protection of the suspension system under extreme working conditions through the energy directional absorption of the wedge-shaped energy guide slot and the rapid modular replacement of the electromagnetic lock, effectively realizes the directional absorption and transfer of collision energy, and improves the overall safety.

[0108] In some embodiments, executing the proactive failure chain control strategy further comprises:

[0109] controlling the human-machine interaction system to generate the driver prompt information.

[0110] In some embodiments, the human-machine interaction system is a head-up display (HUD). Controlling the human-machine interaction system to generate the driver prompt information comprises controlling the head-up display to issue a sound-light warning information to remind the driver to take emergency measures.

[0111] The sound-light warning information comprises visual information and audible signal. The visual information can comprise at least one of an icon, a text, and a dynamic marker. The audible signal can comprise at least one of a hierarchical volume and a voice prompt.

[0112] The icon and the text can be used to display the impact direction (e.g., “deep pit ahead”) and the recommended operation (e.g., “slow down to 30 km / h”).

[0113] The dynamic marker can be a highlighted path line superimposed on the windshield through AR augmented reality technology to guide the driver to avoid the risk area.

[0114] The hierarchical volume can be a warning prompt sound with a playing volume of 70 dB to ensure clear warning but not excessive interference.

[0115] The voice prompt can be a broadcast prompt language (i.e., please hold the steering wheel tightly as the severe jolt is coming).

[0116] In some embodiments of the present specification, the human-machine interaction system (e.g., the HUD head-up display system) is used to connect machine intelligence and human driving decision-making through hierarchical warning, AR guidance, and execution state feedback in the proactive failure control strategy.

[0117] In some embodiments, executing the proactive failure chain control strategy further comprises:

[0118] controlling the vehicle system to record event information of executing the proactive failure chain control strategy.

[0119] The event information can comprise at least one of an event type (e.g., proactive failure chain control strategy triggering), a timestamp (e.g., triggering time), and failure information. Further, the failure information at least comprises a failed component (e.g., energy guide slot module ID), an impact load value, and a vehicle state (e.g., vehicle speed, attitude angle, etc.).

[0120] Please refer to Figure 5 , Figure 5 is a logic diagram of a suspension protection method according to an embodiment of the present application Figure 2In some embodiments, the control vehicle system further comprises:

[0121] generating a maintenance request according to the event information;

[0122] obtaining location information of the vehicle, and determining a target maintenance center of the vehicle based on the location information;

[0123] sending the maintenance request and the location information to the target maintenance center server.

[0124] Specifically, the system determines the location information of the vehicle according to the vehicle system, generates a maintenance request according to the event information, determines a target maintenance center based on the location of the vehicle and the cloud database query, and sends the maintenance request and the location information of the vehicle to the target maintenance center through a communication protocol.

[0125] In some embodiments, the maintenance request and the location information of the vehicle are transmitted using a V2X (Vehicle-to-Everything) communication protocol.

[0126] For example, a vehicle is driving at 100 km / h on a highway and suddenly encounters a road obstacle (such as a fallen tire), and the predicted impact load has exceeded the second threshold L2, triggering the failure chain control strategy: the energy absorption groove module absorbs energy, the electromagnetic lock is released, and the HUD displays a red warning "emergency maintenance required". The vehicle system records the event time, location (longitude X, latitude Y), impact force 28kN, and confirms that the vehicle is located at G15 Shenhai Expressway K1200+500. The cloud matches the nearest 4S store (5 kilometers away, with sufficient spare parts); sends a request through V2X: "replace the energy absorption groove module, vehicle location: G15 K1200+500, VIN: LSVXXXXXXX".

[0127] In some embodiments of the present specification, through the cooperation of the active failure chain control strategy, precise positioning and V2X communication, the rapid response and intelligent maintenance of the vehicle under extreme working conditions are realized, and the vehicle safety and service system under intelligent driving are linked.

[0128] In some embodiments, the active failure chain control strategy further comprises:

[0129] activating the vehicle emergency system to make the vehicle enter an emergency mode.

[0130] The vehicle entering the emergency mode ensures that the vehicle remains controllable when the suspension part fails, preventing loss of control. The vehicle emergency system can include a power system, a braking system, a steering system, an electronic stability system, and a vehicle communication system.

[0131] For example, when the vehicle enters an emergency mode, the power system is started, the throttle opening and fuel injection are adjusted by the engine control unit, the engine output power is limited to a safe range (such as 50% of the maximum power), and acceleration overload is avoided.

[0132] In some embodiments of the present specification, through the combination of failure protection and multi-system cooperative control, the stability of the vehicle and the safety of the passengers are guaranteed in extreme working conditions.

[0133] Please refer to Figure 6 , Figure 6 is a logic diagram of a suspension protection method according to an embodiment of the present application Figure 3 In the logic structure of intelligent suspension protection, the motion state information and road condition information of the vehicle are collected in real time during the operation of the vehicle, the predicted value of the dynamic model is compared with the actual sensor measurement value in real time, and feedback is fed back to the dynamic model for self-learning, so as to achieve the technical effect of continuously optimizing the accuracy of the dynamic model.

[0134] In some embodiments, the local model training of the dynamic model comprises:

[0135] The real-time collected road condition information and vehicle motion state information are processed by the dynamic model to be trained to obtain predicted impact load data;

[0136] Based on the deviation between the real impact load data measured in real time and the predicted impact load data of the dynamic model, the parameters of the dynamic model are adjusted, so that the dynamic model learns online and adapts.

[0137] The local training of the dynamic model combines the real-time road condition information and vehicle motion state information to optimize the dynamic model for predicting impact load, so as to realize real-time adaptive adjustment of the dynamic model.

[0138] In some embodiments, adjusting the parameters of the dynamic model comprises:

[0139] The parameters of the dynamic model are adjusted by using an online machine learning algorithm.

[0140] In some embodiments, the machine learning algorithm comprises a neural network or a support vector machine, which is used to establish a load prediction model and a deviation compensation model, and to perform real-time online optimization of the dynamic model.

[0141] The real-time measured real impact load data is real-time measured real impact load data (historical data) based on a high-precision force sensor.

[0142] For example, real impact load data is collected through real vehicle tests and / or sensor data synchronization, such as collecting real impact load data under various road conditions (urban, highway, off-road), directly measuring suspension force using high-precision force sensors; record the corresponding road condition information (laser radar point cloud, millimeter wave radar data) and vehicle state (IMU, FOF strain).

[0143] In the embodiments of the present application, the collected real impact load data and the dynamic model prediction data are simultaneously input into the dynamic model for self-learning, and the model parameters are trained and optimized through machine learning algorithms (such as neural network, support vector machine, etc.), so as to continuously improve the accuracy and response speed of load prediction. The trained model has strong generalization ability and can effectively adapt to load changes under different working conditions.

[0144] Please refer to Figure 7 , Figure 7 is a schematic diagram of a cloud training logic of a dynamic model in a suspension protection method according to an embodiment of the present application.

[0145] In some embodiments, the cloud model training of the dynamic model comprises:

[0146] The vehicle state data and vehicle motion data collected by the deep network training system of the cloud from multiple edge nodes are used to construct a training sample set; wherein the edge node is a vehicle-mounted edge node corresponding to the vehicle;

[0147] The deep network training system is used to train the dynamic model in combination with the training sample set and a historical working condition database, to generate optimized global model parameters;

[0148] The optimized model parameters are distributed to the edge nodes to update the local dynamic model of the edge nodes.

[0149] The historical working condition database is used to store the vehicle state data and vehicle motion data collected from actual vehicle operation.

[0150] In some embodiments, the machine learning algorithm used in the cloud training stage includes a deep neural network, a reinforcement learning algorithm, and a Kriging model, which is used for offline deep training in the cloud.

[0151] In some embodiments, the deep network training system is used to train the dynamic model in combination with the training sample set and the historical working condition database, to generate optimized global model parameters, comprising:

[0152] The deep network training system is used to train the dynamic model in combination with the training sample set and the historical working condition database, to generate a trained dynamic model;

[0153] With the strategy generation system, a hierarchical control strategy is formulated based on the trained dynamic model in combination with the training sample set;

[0154] With the real-time control unit, the hierarchical control strategy is received and converted into control signals; wherein the control signals are used to drive the PWM controller to control the corresponding components to execute the protection strategy, such as controlling the electromagnetic damping component to activate to absorb impact energy;

[0155] With the performance evaluation system, active chain failure events are automatically identified and the multi-level protection strategy is evaluated, and the evaluation results are fed back to the historical working condition database; wherein the evaluation includes control accuracy evaluation, life consumption calculation, energy efficiency score, etc.

[0156] In the embodiments of the present application, the strategy generation system is deployed on the vehicle edge node. In the embodiments of the present application, the strategy generation system is deployed on the vehicle, and a hierarchical control strategy is formulated based on the dynamic model. For specific implementation methods, please refer to the related description in the foregoing.

[0157] In some embodiments, the performance evaluation system automatically identifies active chain failure events, and predicts the remaining life of the electromagnetic damping under the combined action of various physical fields in complex working environments. The core of the model is to consider the following factors at the same time:

[0158] Mechanical stress field: stress change caused by road impact load;

[0159] Temperature field: Joule heat generated by electromagnetic damper and environmental temperature influence;

[0160] Electromagnetic field: electromagnetic force fluctuation caused by coil current change;

[0161] Material performance degradation: performance degradation caused by material fatigue;

[0162] The performance evaluation system uses a nonlinear superposition formula to express damage accumulation, i.e., a multi-field coupling damage evaluation model, to predict the service performance (i.e., the remaining life) of the electromagnetic damping component. For specific implementation methods of evaluating the remaining life of the electromagnetic damping component based on the multi-field coupling damage evaluation model, please refer to the related description in the foregoing.

[0163] In the embodiments of the present application, the life prediction of the electromagnetic damping unit is further linked with the vehicle-mounted system, realizing real-time monitoring and visual display of the remaining life of the electromagnetic damping energy absorption module.

[0164] Please also refer to Figure 8 , 9 , Figure 8Fig. 1 is a schematic diagram of a health state data interaction process of an electromagnetic damping unit in a suspension protection method according to an embodiment of the present application, Figure 9 Fig. 2 is a schematic diagram of a packaging process of data from collection to transmission in a suspension protection method according to an embodiment of the present application.

[0165] In some embodiments, the cloud-side obtained electromagnetic damping unit life prediction result is interacted with the vehicle-mounted system, including:

[0166] The strain of the electromagnetic damping unit is monitored through a strain sensor, and real-time strain data ε is output t ;

[0167] The strain data is received through a multi-field coupling damage evaluation model, and the residual life L(t) of the electromagnetic damping unit is calculated; wherein the strain data includes the electromagnetic damping working current I(t) captured by the current sensor and the temperature rise data T(t) obtained by the temperature sensor;

[0168] The calculated residual life L(t) is sent to the vehicle-mounted edge node, and the residual life L(t) is displayed to the user through the instrument life display configured by the vehicle-mounted edge node.

[0169] The vehicle-mounted edge node performs spare part demand early warning according to the residual life through a maintenance system.

[0170] In the embodiments of the present application, through data interaction between the cloud side and the vehicle-mounted system, the health state information of the electromagnetic damping unit can be directly fed back to the vehicle-mounted instrument, providing real-time life information for the user, which can help the user to timely master the working state of the electromagnetic damping energy absorption module.

[0171] Figure 8 Corresponding to the real-time health state monitoring and display function of the edge end (vehicle-mounted system), the damage model deployed on the vehicle (which can be trained by the cloud side and then distributed) is used to process the sensor data in real time, and the residual life is given. The input of this damage model includes strain, current, temperature, etc., and the output is residual life. At the same time, these data (such as impact level, temperature, strain, etc.) will also be packaged according to Figure 9 the format and transmitted to the cloud side.

[0172] As shown in Figure 9 , the data structure for transmission is used to transmit data between the vehicle and the cloud side. The transmission can be raw sensor data or processed feature data, which is used for training or updating the cloud-side model.

[0173] As described in Figure 9 , the packaging process of data from collection to transmission includes:

[0174] Step 100, data structure initialization;

[0175] Step 101, field assignment (timestamp, latitude and longitude, impact level, vehicle ID, etc.);

[0176] Step 102, GPS data collection and coordinate conversion;

[0177] Step 103, load level (impact level) reading;

[0178] Step 104, vehicle ID acquisition;

[0179] Step 105, checksum calculation (e.g., take the sum of all fields, take the result modulo 256, and then handle overflow);

[0180] Step 106, data packaging;

[0181] Step 107, communication transmission

[0182] Among them, the timestamp is used for data synchronization; the latitude and longitude are used for positioning the vehicle position and can be used for analyzing the load characteristics related to the geographical position; the impact level represents the load level (such as impact load, endurance load, etc.) and can be calculated in real time by the edge side, which is used to describe the severity of the current load; the vehicle ID is used to identify the vehicle and is used to distinguish the data of different vehicles in the cloud; the checksum is used to ensure the integrity of data transmission.

[0183] Among them, the "load level reading" corresponds to the type and level of impact load detected by the vehicle in real time during driving, and this data can be obtained by the dynamics model of the edge side (vehicle).

[0184] Specifically, the deep network training system of the cloud collects multi-scene motion data from multiple edge nodes to construct a training sample set; uses a Kriging machine learning model to train the sample set to form an intelligent prediction model of automobile load; and distributes the global model parameters obtained by training to each edge node to update the local dynamics model.

[0185] In some embodiments of the present specification, the driving state data of the vehicle is collected in real time by the edge node corresponding to the vehicle, and deep learning training is performed in combination with the cloud to continuously optimize the perception algorithm, dynamics model and decision strategy, so as to realize the collaborative training of the cloud and the local dynamics model, and continuously improve the overall performance of the system.

[0186] In some embodiments, the machine learning algorithm used in the cloud training stage includes a deep neural network, a reinforcement learning algorithm, and a Kriging model, which is used for offline deep training.

[0187] Please refer to Figure 10 , Figure 10 is a schematic diagram of the logic of the cloud offline training of the dynamics model in a suspension protection method according to an embodiment of the present application.

[0188] Specifically, the cloud model of the dynamic model is trained offline, comprising:

[0189] Step 1, feature parameter extraction and sample set construction;

[0190] Step 2, basic load prediction model training;

[0191] Step 3, bias model training;

[0192] Step 4, confidence evaluation and iterative optimization;

[0193] Specifically, Step 1, feature parameter extraction and sample set construction, comprises:

[0194] Extract a set of vehicle design parameters X = {x1, x2,..., xn}; wherein each parameter x is an m-dimensional vector representing different dimensional feature parameters (such as vertical force, lateral force, material stiffness, etc.);

[0195] Obtain a target load data set F = {F1, F2,..., Fn} through multi-body dynamics simulation or real vehicle test; wherein F represents the true value of the target load;

[0196] Construct an original training sample set S = {X, F}.

[0197] Wherein, the vehicle design parameters can include suspension geometry, spring stiffness, shock absorber damping coefficient, etc.

[0198] The target load refers to the six-dimensional mechanical quantity (FX, FY, FZ, MX, MY, MZ) borne by the key connection points (suspension / body mounting points) of the vehicle.

[0199] Specifically, Step 2, basic load prediction model training, comprises:

[0200] Train a Gaussian process regression model (Kriging) based on the training sample set S, output the load prediction function

[0201] Wherein, based on the training sample set S, the hyperparameter combination θ = {θ1, θ2,..., θk} is initialized, the maximum likelihood estimation (MLE) loss function is calculated by the improved MGBDE algorithm (Multi-Gene Bounded Differential Evolution), and the improved Gaussian bare bone differential evolution (MGBDE) optimizer is used to repeatedly optimize the hyperparameter combination based on the loss function to obtain the optimal hyperparameter combination θoptim, and the load prediction function is output

[0202] Specifically, Step 3, bias model training, comprises:

[0203] Compute prediction bias for sample point And train load bias compensation model based on prediction bias dataset (X, ε), output load bias prediction Wherein, ε represents the prediction bias (the absolute difference between the true value of the target load F and the load prediction value ).

[0204] Wherein, traverse the sample set S, and perform the following on each sample point by leave-one-out method:

[0205] Exclude sample point (Xk, Fk), and construct temporary sample set S' = S \ {(Xk, Fk)};

[0206] Train temporary Kriging model on temporary sample set S' with optimal hyperparameter combination θoptim;

[0207] Compute the prediction bias εk for the sample point, and generate the prediction bias set ε = {ε1, ε2,..., εn};

[0208] Construct sample point prediction bias set Sε = {X, ε}, and train load bias prediction (reuse step 2 training logic) based on sample prediction bias set Sε.

[0209] Specifically, step 4, confidence evaluation and iterative optimization, includes:

[0210] Real-time calculation of confidence Wherein, is the sum of the predicted load and the prediction bias;

[0211] Evaluate the mean square error (MSE): if the mean square error MSE > δ (δ is a threshold set by the user), then expand the sample set (supplement multi-body simulation / road test) and return to step 1 to expand the sample set; otherwise, terminate the training, the model training is completed, and is deployed to the edge computing node to update the local dynamics model.

[0212] In the embodiments of the present specification, the real-time data acquisition capability of the edge end (vehicle sensor) is utilized, the parameter sample set is constructed by extracting the key feature parameters of the impact load, the iterative training is combined with the historical and real-time data, the prediction and adaptive control of the key component impact load are performed, the online optimization algorithm architecture with reinforcement learning capability is constructed by combining the powerful computing resources of the cloud, the system parameters can be dynamically adjusted through continuous iterative learning, the running state is optimized, and the stability and reliability under complex working conditions are ensured.

[0213] The present application aims at the protection short board of the traditional suspension system under extreme working conditions. Through intelligent sensing, dynamic prediction and hierarchical failure chain design, the technical effect from "passive response" to "active protection" is realized, the problems of low prediction accuracy, high maintenance cost and low energy management efficiency in the prior art are solved, and reliable technical support is provided for intelligent driving safety.

[0214] It should be noted that the above description of the process is only for example and illustration, and does not limit the scope of the present application. Those skilled in the art can make various modifications and changes to the process under the guidance of the present application. However, these modifications and changes are still within the scope of the present application.

[0215] In one or more embodiments of the present application, a controller is also provided for implementing the suspension protection method of any one of the embodiments provided by the present application.

[0216] The controller is an electronic device that manages and controls the operation of the suspension protection method. For example, the controller automatically adjusts the damping force of the electromagnetic damping module (such as from 80 N·s / m to 120 N·s / m) according to the real-time road conditions (such as bumps or curves), optimizing the stability and comfort of the vehicle. The controller predicts the impact load of the front pit (such as 15 kN) through millimeter wave radar and dynamic model, and the controller lifts the aerodynamic suspension height (such as 30 mm) in advance to prevent the chassis from touching the bottom. The controller monitors the local strain data of the suspension swing arm in real time, and if an overload risk (such as strain exceeding 2000με) is detected, the active buckling protection device is triggered to absorb energy immediately.

[0217] In some embodiments, the controller is linked with ESP (Electronic Stability Program) to enhance the front suspension stiffness to reduce the body pitch, while adjusting the rear wheel brake force distribution.

[0218] In some embodiments, the controller receives optimized suspension control parameters (such as damping curves for ice and snow roads) through the cloud, and dynamically updates the local strategy library to adapt to new working conditions.

[0219] In some embodiments, the controller can be a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components.

[0220] Figure 11 is a structural schematic diagram of the suspension protection system according to some embodiments of the present application.

[0221] As Figure 11As shown, a structural diagram of a suspension protection system is also provided in one or more embodiments of the present specification. The suspension protection system can include a controller 1110 and a suspension system 1120 connected with the controller.

[0222] The controller 1110 can be configured to perform the steps in the above-mentioned embodiments of the vehicle control method, respectively. For the specific implementation of these modules and more details, please refer to the corresponding method part, which will not be repeated here.

[0223] The specific implementation of the above operations can refer to the previous embodiments, which will not be repeated here.

[0224] The present application also provides a computer readable storage medium, which stores instructions. When the instructions are executed by a processor, the processor is configured to perform the above-mentioned suspension protection method.

[0225] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0226] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The means for performing the functions specified in one or more flows and / or blocks.

[0227] These computer program instructions can also be stored in a computer readable storage medium, which can guide the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The means for performing the functions specified in one or more flows and / or blocks.

[0228] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0229] In one typical arrangement, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0230] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), or flash memory, for example. Memory is an example of computer readable media.

[0231] Computer readable media includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology for storing information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition in this paper, computer readable media does not include transitory computer readable media, such as modulated data signals and carriers.

[0232] As shown in FIG. 1, it is a schematic diagram of the architecture of a vehicle provided in an embodiment of the present application. In this embodiment, the vehicle 200 includes the controller provided in any of the above embodiments, or the suspension protection system, or performs the steps of any of the suspension protection methods provided in the embodiments of the present application. In this embodiment, the vehicle can be a fuel automobile, a plug-in hybrid electric vehicle or a new energy vehicle, etc., and the present disclosure does not make specific limitation on this. Figure 12

[0233] ​​​In one embodiment, the vehicle can be configured in a fully or partially autonomous driving mode. For example, the vehicle can control itself while in the autonomous driving mode and can determine a current state of the vehicle and its surrounding environment, determine a possible behavior of at least one other vehicle in the surrounding environment, and determine a confidence level corresponding to a likelihood that the other vehicle will perform the possible behavior based on the determined information, control the vehicle based on the determined information. While the vehicle is in the autonomous driving mode, the vehicle can be placed to operate without human interaction.

[0234] In the description of the present application, the terms "first", "second", etc. are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0235] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0236] The embodiments, implementation manners and related technical features of the present application can be combined, replaced with each other without conflict.

[0237] The above is only the preferred embodiment of the present application, and does not limit the present application in any form. Any simple modification, equivalent change and modification made to the above embodiment without departing from the technical solution of the present application and according to the technical essence of the present application still belongs to the scope of the technical solution of the present application.

Claims

1. A method of suspension protection, characterized in that, The method comprises: Based on the road condition information corresponding to the vehicle and the vehicle motion state information, the suspension system of the vehicle is controlled by multi-level protection.

2. The method of claim 1, wherein, Based on the road condition information corresponding to the vehicle and the vehicle motion state information, the suspension system of the vehicle is controlled by multi-level protection, comprising: Predicting the impact load by combining the real-time road condition information and the vehicle motion state information through the dynamic model; According to the predicted impact load, the suspension system of the vehicle is controlled by multi-level protection.

3. The method according to claim 2, characterized in that According to the predicted impact load, the suspension system of the vehicle is controlled by multi-level protection, comprising: According to the predicted impact load, the target protection control strategy required by the suspension system is determined; The target protection control strategy is executed to protect the suspension system of the vehicle.

4. The method of claim 3, wherein, In the case that the predicted impact load is lower than the first threshold value, the target protection control strategy includes the pneumatic emergency protection control strategy; the execution of the target protection control strategy to protect the suspension system of the vehicle, comprising: The pneumatic emergency protection control strategy is executed to adjust the body posture and / or generate driver prompt information.

5. The method of claim 3, wherein, In the case that the predicted impact load exceeds the first threshold value and is lower than the second threshold value, the target protection control strategy includes the electromagnetic damping buffer control strategy; the execution of the target protection control strategy to protect the suspension system of the vehicle, comprising: The electromagnetic damping buffer control strategy is executed to activate the electromagnetic damping components in the suspension system.

6. The method of claim 5, wherein, The electromagnetic damping components include electromagnetic damping buffer components and electromagnetic damping energy absorption components; the execution of the electromagnetic damping buffer control strategy to activate the electromagnetic damping components in the suspension system, comprising: The electromagnetic damping buffer control strategy is executed to activate the electromagnetic damping buffer components to dynamically respond and buffer the load impact, and activate the electromagnetic damping energy absorption components to dynamically absorb the energy generated by the load impact.

7. The method of claim 6, wherein, The execution of the electromagnetic damping buffer control strategy also includes evaluating the remaining life of the electromagnetic damping components.

8. The method of claim 7, wherein, Evaluating the remaining life of the electromagnetic damping components also includes: According to the evaluation result of the remaining life of the electromagnetic damping components, driver prompt information is generated.

9. The method of claim 7, wherein, Evaluating the remaining life of the electromagnetic damping components, comprising: The remaining life of the electromagnetic damping components is evaluated by a multi-field coupling damage evaluation model.

10. The method of claim 5, wherein, In the case that the predicted impact load exceeds the first threshold value and is lower than the second threshold value, the method further comprises: Adjusting the suspension parameters according to the real-time road condition information and the vehicle load change information; The suspension parameters include at least one of the elastic stiffness, the shock absorber damping force, the body height and the inclination stiffness.

11. The method of claim 3, wherein, In the case that the predicted impact load exceeds the second threshold value, the target protection control strategy includes the active failure chain control strategy; the execution of the target protection control strategy to protect the suspension system of the vehicle, comprising: The active failure chain control strategy is executed to control the active buckling components in the suspension system to protect the suspension system of the vehicle.

12. The method of claim 11, wherein, The step of executing the active failure chain control strategy also includes: The man-machine interaction system is controlled to generate driver prompt information.

13. The method of claim 12, wherein, The driver prompt information includes audible and visual warning information.

14. The method of claim 11, wherein, The step of executing the active failure chain control strategy further comprises: The vehicle-mounted system records event information of executing the active failure chain control strategy; The event information includes at least one of event type, timestamp, and fault information.

15. The method of claim 14, wherein, After the vehicle-mounted system records the event information of executing the active failure chain control strategy, the method further comprises: Generating a maintenance request according to the event information; Obtaining location information of the vehicle, and determining a target maintenance center of the vehicle based on the location information; Sending the maintenance request and the location information to the target maintenance center server.

16. The method of claim 11, wherein, The step of executing the active failure chain control strategy further comprises: Activating the vehicle-mounted emergency system to make the vehicle enter an emergency mode.

17. The method according to any one of claims 11 to 16, characterized in that, The active buckling component comprises an energy guide groove module and an electromagnetic lock; the energy guide groove module is arranged on the surface of a suspension key component; the electromagnetic lock fixes the energy guide groove module in a preset position through electromagnetic attraction force; The active failure chain control strategy is executed to control the active buckling component in the suspension system to work to protect the suspension system of the vehicle, which comprises: The active failure chain control strategy is executed to control the electromagnetic lock to be powered off, so that the energy guide groove module is separated from the preset position to absorb and guide impact energy, thereby protecting the suspension key component.

18. The method of claim 2, wherein, The dynamic model is based on real-time local model training of impact load prediction and / or cloud model training based on a combination of a training sample set and a historical working condition database to optimize the dynamic model; The training sample set is constructed according to vehicle state data and vehicle motion data from multiple edge nodes; each edge node corresponds to a vehicle; the historical working condition database is used to store vehicle state data and vehicle motion data collected from actual vehicle operation.

19. The method of claim 18, wherein, The real-time local model training of the dynamic model based on impact load prediction comprises: The real-time collected road condition information and vehicle motion state information are processed by the dynamic model to be trained to obtain predicted impact load data; The dynamic model is adjusted in parameters based on the deviation between the real impact load data measured in real time and the predicted impact load data of the dynamic model, so that the dynamic model learns online and adapts.

20. The method of claim 18, wherein, The cloud model training based on a combination of a training sample set and a historical working condition database comprises: The dynamic model is trained based on the training sample set and the historical working condition database to generate optimized global model parameters; The optimized model parameters are distributed to edge nodes to update the dynamic model of the edge nodes.

21. The method of claim 20, wherein, The training of the dynamic model based on a combination of the training sample set and the historical working condition database to generate optimized global model parameters further comprises: Based on the training sample set, the remaining life of the electromagnetic damping buffer control strategy is evaluated when the electromagnetic damping component is identified to be executed.

22. The method of claim 1, wherein, Further comprising: Real-time road condition information and vehicle motion state information in the vehicle driving process are collected by an intelligent chassis system, and a global environment and vehicle state model is generated through multi-source data fusion; The global environment and vehicle state model integrates at least one of road condition information, vehicle motion state, suspension load distribution, and material health state.

23. The method of claim 22, wherein, The step of collecting real-time road condition information during the driving of the vehicle by the intelligent chassis system comprises: The laser radar set by the intelligent chassis system collects real-time road condition information during the driving of the vehicle; wherein the real-time road condition information comprises at least one of road surface undulation, obstacle position, and vehicle distance.

24. The method according to claim 23, wherein The step of collecting vehicle motion state information during the driving of the vehicle by the intelligent chassis system comprises: At least one sensor set by the intelligent chassis system collects corresponding vehicle state data and vehicle motion data; The collected vehicle state data and vehicle motion data are fused to generate vehicle motion state information representing a global environment and vehicle state model.

25. The method of claim 24, wherein, The step of collecting vehicle motion state information by at least one sensor set by the intelligent chassis system comprises: An inertial measurement instrument set by the intelligent chassis system collects vehicle motion data during the driving of the vehicle; wherein the vehicle motion data comprises at least one of three-axis linear acceleration, three-axis angular velocity, and body attitude angle; and / or A distributed fiber optic strain gauge set by the intelligent chassis system detects vehicle state data during the driving of the vehicle; wherein the vehicle state data at least comprises suspension strain distribution information; and / or An electrorheological fluid pressure sensing array set by the intelligent chassis system detects vehicle state data during the driving of the vehicle; wherein the vehicle state data at least comprises at least one of suspension local pressure distribution information and suspension damping force; and / or A material performance compensation sensor set by the intelligent chassis system detects vehicle state data during the driving of the vehicle; wherein the vehicle state data at least comprises at least one of suspension component material temperature, aging degree, and deformation information.

26. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 25.

27. A controller having stored thereon computer programs or instructions, characterized in that, The computer program or instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 25.

28. A suspension protection system characterized by, A suspension system comprising a controller as claimed in claim 27.

29. A computer program product, characterised in that, A computer program or instructions, which, when executed by a processor, implement the steps of the method of any one of claims 1 to 25.

30. A vehicle characterized by A controller as claimed in claim 27, or a suspension protection system as claimed in claim 28, or a processor executing the steps of the method of any one of claims 1 to 25.

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