Vehicle control method, control method, and vehicle

Abstract

The embodiment of the application discloses a kind of vehicle control method, control method and vehicle, belong to vehicle control field.Therein, the vehicle control method includes obtaining the current state data of vehicle, wherein the current state data includes at least two types of data representing vehicle state;The current state data is handled using a preset quadratic control algorithm, to obtain the adjustment parameter for the air spring, wherein the quadratic control algorithm includes the algorithm with quadratic performance index and / or carries out quadratic integral;According to the adjustment parameter, the operating state of the air spring is adjusted.The embodiment of the application has the technical effect of realizing the rapid adjustment of vehicle vertical dynamic balance, improving the driving comfort and safety of vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle control, in particular, to a vehicle control method, a control method and a vehicle. BACKGROUND

[0002] In the transportation field, ensuring the stability and driving comfort of vehicles under various road conditions and load conditions is an important link to optimize overall transportation efficiency and safety. The air spring system, as a key technology to improve the performance of vehicle suspension, directly affects the stability and comfort of the vehicle during dynamic driving.

[0003] However, when the current vehicle controls the air spring system, it relies on the traditional PID control algorithm, which limits the performance of the PID control algorithm and makes it difficult to ensure the driving stability of the vehicle under complex road conditions and easily changing loads. SUMMARY

[0004] The embodiments of the present application provide a vehicle control method, a control method and a vehicle to at least solve the technical problem of poor driving stability of the vehicle.

[0005] According to a first aspect of the embodiments of the present application, a vehicle control method is provided, the vehicle comprising a vehicle frame and a wheel support, an air spring being provided between the vehicle frame and the wheel support for changing the distance between the vehicle frame and the wheel support, the method comprising:

[0006] Obtaining current state data of the vehicle, wherein the current state data comprises at least two types of data representing the state of the vehicle;

[0007] Processing the current state data using a preset quadratic control algorithm to obtain an adjustment parameter for the air spring, wherein the quadratic control algorithm comprises an algorithm with a quadratic performance index and / or quadratic integral;

[0008] Adjusting the operating state of the air spring according to the adjustment parameter.

[0009] In this embodiment, when obtaining the state data of the vehicle, the current state data obtained includes at least two types of data, which provides more comprehensive data support for the control of the vehicle, so that the vehicle can be adjusted to a steady state under complex road conditions and easily changing loads in the subsequent adjustment of the operating state of the air spring using the current state data, thereby improving the driving stability of the vehicle. At the same time, the quadratic control algorithm is used to process the current state data, so that the adjustment parameter obtained can accurately respond to the changes in the state of the vehicle, realize the rapid adjustment of the vertical dynamic balance of the vehicle, and improve the driving comfort and safety of the vehicle.

[0010] In conjunction with the first aspect, in an optional implementation of this application embodiment, the step of processing the current state data using a preset quadratic control algorithm to obtain adjustment parameters for the air spring includes:

[0011] The current state data and the preset expected indicators are calculated using the quadratic control algorithm to obtain the target control strategy. The expected indicators include indicators when the vehicle state meets the preset conditions, and the target control strategy includes a strategy to converge the vehicle state towards the expected indicators.

[0012] The adjustment parameters are determined based on the target control strategy.

[0013] By adopting this implementation method, the desired index is preset so that when the quadratic control algorithm processes the current state data, it can obtain the target control strategy. Thus, when the air spring is controlled according to the target control strategy, the vehicle's state can be adjusted in a direction that converges with the desired index, thereby improving the vehicle's stability.

[0014] In conjunction with the first aspect, in an optional implementation of this application embodiment, the step of calculating the target control strategy by using the quadratic control algorithm based on the current state data and the preset expected index includes:

[0015] The preset vehicle dynamic model is updated based on the current state data, wherein the vehicle dynamic model is generated using historical state data of the vehicle, and the historical state data includes the vehicle state data obtained before the current state data.

[0016] The target control strategy is obtained by calculating the updated vehicle dynamic model and the desired index using the quadratic control algorithm.

[0017] This implementation method pre-defines a vehicle dynamic model, allowing the quadratic control algorithm to directly utilize data from the vehicle dynamic model when determining the target control strategy. This facilitates the quadratic control algorithm obtaining more comprehensive vehicle state data, improving the accuracy and quality of the target control strategy, and thus enhancing vehicle stability.

[0018] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, obtaining the current state data of the vehicle includes:

[0019] Acquire raw state data from at least two sensors of the vehicle at a target time, wherein different sensors are used to acquire different types of raw state data;

[0020] The original state data is processed by Kalman filtering to obtain the current state data.

[0021] Using this implementation method, Kalman filtering can effectively denoise the original state data, improve the data quality of the current state data, make it easier to participate in subsequent calculations, improve the accuracy of adjustment parameters, and improve vehicle driving stability.

[0022] In conjunction with the first aspect, in an optional implementation of this application embodiment, before performing Kalman filtering on the original state data to obtain the current state data, the method further includes:

[0023] The raw state data is preprocessed, wherein the preprocessing includes at least one of error correction and synchronization time baseline.

[0024] By adopting this implementation method, the original state data is preprocessed first, which helps to improve the quality of the preprocessed original state data, thereby improving the accuracy of subsequent data processing.

[0025] In conjunction with the first aspect, in an optional implementation of the embodiments of this application, the method further includes:

[0026] When performing Kalman filtering on the original state data, the Kalman filter is used to predict the vehicle state after the target time, thus obtaining the predicted state data.

[0027] The predicted state data is processed using the quadratic control algorithm to obtain the predicted adjustment parameters for the air spring.

[0028] The operating state of the air spring is adjusted according to the predicted adjustment parameters.

[0029] This implementation method predicts and adjusts vehicle status, proactively preventing unstable vehicle states under complex road conditions and enhancing the ability to respond to emergencies.

[0030] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, the air spring is connected to an air passage, and a solenoid valve is provided on the air passage;

[0031] The adjustment parameters include the opening degree and / or opening duration of the solenoid valve;

[0032] Adjusting the operating state of the air spring according to the adjustment parameters includes:

[0033] The solenoid valve is controlled according to the opening degree and / or opening duration to adjust the flow rate and / or pressure of compressed air in the air spring.

[0034] This implementation method controls the solenoid valve to change the operating state of the air spring. The control process is simple and convenient, and it is easy to improve the efficiency of restoring stable operation of the vehicle.

[0035] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, the current state data includes multiple types of parameters, wherein the types of parameters include at least two of displacement, vehicle speed, vehicle acceleration, and the tilt angle between the vehicle and the horizontal plane;

[0036] The quadratic control algorithm includes at least one of the linear quadratic adjustment algorithm and the H-infinity control algorithm.

[0037] According to a second aspect of the embodiments of this application, a vehicle control method is provided, employing the control method described above.

[0038] According to a third aspect of the embodiments of this application, a vehicle is provided, the vehicle including a frame and a wheel bracket, wherein an air spring for changing the distance between the frame and the wheel bracket is provided between the frame and the wheel bracket.

[0039] The vehicle also includes a controller, which employs the control method described above.

[0040] In conjunction with the third aspect, in an optional implementation of the embodiments of this application, the air spring can be controlled to extend or compress, one end of the air spring is fixedly connected to the vehicle frame, and the other end is fixedly connected to the wheel bracket;

[0041] The vehicle also includes an air tank for storing gas for the air springs;

[0042] An air passage connects the air storage tank to the air inlet of the air spring;

[0043] A solenoid valve is installed in the air circuit to control the opening and closing of the air circuit, thereby controlling the inflation and deflation of the air spring, wherein the air spring extends or compresses during inflation and deflation.

[0044] According to a fourth aspect of the embodiments of this application, a vehicle suspension system is provided, including a parameter acquisition module, a suspension controller, a linear quadratic adjuster, and a solenoid valve, wherein the solenoid valve is used to control the inflation and deflation of the air springs in the suspension system;

[0045] The parameter acquisition module is used to acquire at least two types of current state data that characterize the vehicle state. The parameter acquisition module is connected to the suspension controller and is used to transmit the current state data to the suspension controller.

[0046] The suspension controller is connected to the linear quadratic regulator and is used to process the current state data using the linear quadratic regulator to obtain the adjustment parameters for the solenoid valve.

[0047] The linear quadratic regulator is connected to the solenoid valve and is used to adjust the duty cycle of the solenoid valve according to the adjustment parameters.

[0048] The parameter acquisition module includes an inertial measurement unit, a positioner, and an encoder.

[0049] The technical effects achieved by the second to fourth aspects mentioned above are similar to those achieved by the corresponding technical means in the first aspect, and will not be repeated here. Attached Figure Description

[0050] Figure 1 This is a flowchart of a vehicle control method provided in an embodiment of this application;

[0051] Figure 2 This is a flowchart illustrating a vehicle control method provided in this application in a specific application;

[0052] Figure 3 This is a structural block diagram of a vehicle control section provided in an embodiment of this application. Detailed Implementation

[0053] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0054] It should be understood that "multiple" as mentioned herein refers to two or more. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily imply differentness.

[0055] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0056] First, the terminology used in the embodiments of this application will be introduced.

[0057] LQR (linear quadratic regulator) is a type of linear quadratic regulator that provides optimal control with linear state feedback, making it easy to construct closed-loop optimal control. LQR optimal control can achieve good performance indicators for the original system at a low cost (in fact, it can also be used to tune unstable systems), and the method is simple and easy to implement. Furthermore, the powerful functionality of Matlab makes system simulation easy. Specifically, the role of LQR involves, given a linear system (where the control variable and the state variable have a linear relationship), establishing a cost function (an indicator of control quality) for this system, and within certain constraints, finding a control sequence that makes the system stable (stability can be understood as maintaining a certain value or tracking a certain curve).

[0058] H∞ control algorithm: also known as H∞ optimal control, is a boundary optimization design that uses the H∞ norm as the objective function.

[0059] LQG: The full name of LQG Regulator is Linear-Quadratic-Gaussian Regulator. It is a controller obtained by combining an optimal quadratic linear regulator (LQR) and an optimal state estimator (Kalman filter).

[0060] Kalman filtering is an algorithm that uses the state equations of a linear system to optimally estimate the system state using observed input and output data. Since the observed data includes noise and disturbances within the system, the optimal estimation can also be viewed as a filtering process. It performs state estimation through two main steps: prediction and update. In the prediction step, the Kalman filter uses the system's state transition equations to predict the state and covariance matrix at the next time step; in the update step, it compares the observed values ​​with the predicted values, incorporating observation noise and uncertainties in the system model, to update the system's state estimate and covariance.

[0061] IMU, also known as Inertial Measurement Unit, is a device that measures the three-axis attitude angles (or angular rates) and acceleration of an object. Typically, an IMU contains three single-axis accelerometers and three single-axis gyroscopes. The accelerometers detect the acceleration signals of the object along the three independent axes of the carrier coordinate system, while the gyroscopes detect the angular velocity signals of the carrier relative to the navigation coordinate system. By measuring the angular velocity and acceleration of the object in three-dimensional space, the object's attitude can be calculated.

[0062] In modern commercial transportation, ensuring vehicle stability and driving comfort under various road conditions and load loads is crucial for optimizing overall transportation efficiency and safety. As a key technology for improving vehicle suspension performance, the air spring system's performance during dynamic driving directly impacts the driver's experience and the integrity of cargo. However, current commercial vehicle air spring systems still have limitations when facing complex road environments and load variations, especially in real-time adjustment of suspension parameters to cope with unexpected situations. Traditional control methods often struggle to achieve precise and efficient performance optimization.

[0063] Limitations of existing technology

[0064] 1. Due to the limitations of single-sensor control, traditional air spring control systems mostly rely on data from a single sensor (such as an accelerometer or pressure sensor) for adjustment. Under complex road conditions, such as potholes, sharp turns, or changes in load, this system cannot fully capture vehicle dynamics, resulting in slow response or insufficient adjustment, which affects driving stability.

[0065] 2. Lack of predictive adjustment capability: When encountering sudden situations (such as emergency braking or sudden changes in load), the current system can often only make a passive response, lacking the ability to actively predict and quickly adjust, thereby reducing driving comfort and safety.

[0066] 3. Limitations of control algorithms: Although traditional PID (proportional-integral-derivative) control algorithms are effective within a certain range, their performance and adaptability are limited when facing nonlinear and dynamically changing system states, making it difficult to achieve dynamic optimization control effects.

[0067] To overcome the aforementioned limitations and achieve a higher level of vehicle dynamic control and driving experience, innovative control technologies and sensor fusion strategies have become the focus of research and development. In particular, the integrated application of advanced algorithms and multi-sensor fusion technologies provides a feasible solution to address the shortcomings of traditional control methods.

[0068] This application proposes an advanced control strategy that integrates multi-sensor fusion (including IMU, GPS, wheel speed sensors, etc.) with LQR (linear quadratic control), aiming to significantly improve the performance, stability, and adaptability to road conditions of commercial air spring systems.

[0069] Based on this, embodiments of this application provide a vehicle control method, a control method, and a vehicle, which at least solves the following problem:

[0070] 1. Improved response speed and accuracy: Traditional control strategies have limited response speed and accuracy to changes in road surface or vehicle load. Traditional ECAS commercial vehicle systems use classic control, which often cannot respond to load disturbances in a timely manner and is difficult to quickly adjust to the optimal vertical balance state during dynamic driving.

[0071] 2. Safety and stability optimization: When faced with complex road conditions, such as curved road sections, uneven road surfaces, or emergency braking situations, the existing air spring system lacks effective adaptive capabilities, which may increase the risk of vehicle instability or rollover.

[0072] It has at least the following effects:

[0073] This application can monitor and analyze vehicle status and road conditions in real time, automatically adjust air spring parameters, and provide precise, real-time vertical dynamic balance control, significantly improving vehicle stability and driving comfort. Simultaneously, by integrating intelligent predictive algorithms, the system can make adjustments in advance based on sensor data trend analysis, avoiding potential instability, enhancing safety, and reducing reaction time in emergency situations.

[0074] It has at least one of the following characteristics:

[0075] 1. Abandoning the original single sensor input, based on the attitude capture principle of devices such as drones and small aircraft, multi-sensor fusion technology is adopted. By using the real-time fusion of data from multiple sensors, aircraft sensing devices such as IMU, GPS, and encoders are used to obtain more comprehensive and accurate vehicle status information, including position, speed, acceleration, tilt angle, etc., to provide high-precision data support for the control system.

[0076] 2. Integration of LQR control algorithm: LQR control theory is applied to the parameter adjustment of air spring system. By optimizing the control law, the system can actively and accurately respond to changes in driving conditions, realize rapid adjustment of vehicle vertical dynamic balance, and improve driving comfort and safety.

[0077] 3. Intelligent prediction and preventive control: By combining trend analysis of sensor data, predictive adjustments are made through LQR control to proactively prevent vehicle instability in complex road conditions, such as tilting or excessive vibration, thereby enhancing the system's ability to respond to emergencies.

[0078] The vehicle control method provided in the embodiments of this application will be further described below, referring to... Figure 1The flowchart of the vehicle control method shown is illustrated, wherein the vehicle includes a frame and wheel supports, and an air spring is provided between the frame and the wheel supports for changing the distance between the frame and the wheel supports. The method includes the following processing steps.

[0079] S100: Obtain the current status data of the vehicle.

[0080] The current state data includes at least two types of data characterizing the vehicle's state. Specifically, different types of data characterizing the vehicle's state can be acquired by different sensors. For example, in one application scenario, the current state data includes the vehicle's speed, acceleration, and levelness. The vehicle's speed can be acquired using a speed sensor, acceleration can be acquired using an IMU (Inertial Measurement Unit), and levelness can be measured using a gyroscope. It should be noted that vehicle state refers to a state related to the vehicle's stability, such as the vehicle's levelness, acceleration, and the distance between the vehicle chassis and the ground.

[0081] S102. The current state data is processed using a preset quadratic control algorithm to obtain the adjustment parameters for the air spring.

[0082] The quadratic control algorithm includes algorithms with quadratic performance indicators and / or quadratic integration. Specifically, the quadratic control algorithm includes algorithms for linear quadratic regulators and algorithms for LQG controllers.

[0083] Since the current state data reflects the vehicle's condition, such as the distance between the chassis and wheel supports, and the quadratic control algorithm can calculate adjustment parameters based on this data, adjusting the vehicle according to these parameters can improve its stability. Therefore, this embodiment does not specifically limit the quadratic control algorithm, nor does it limit the specific calculation process of the adjustment parameters.

[0084] To facilitate understanding, we will use LQR as an example. Before controlling the steady state of the vehicle, the vehicle's state data is collected, specifically including the vehicle's displacement, velocity, acceleration, and tilt angle with the horizontal plane. The collected state data is used to form a vehicle dynamic model (i.e., vehicle simulation) in a time sequence. Based on the vehicle dynamic model and preset desired indicators (such as minimizing vehicle body vibration), the calculation parameters in LQR are iterated or optimized, so that after receiving the vehicle's state data, LQR can output adjustment parameters that minimize the vehicle body vibration.

[0085] S104. Adjust the operating state of the air spring according to the adjustment parameters.

[0086] In one embodiment, the adjustment parameter can be a parameter of the air spring itself, such as the extension, contraction, and elastic coefficient of the air spring. The adjustment parameter can also be a parameter of a control device used to control the change of the operating state of the air spring, such as a solenoid valve. The adjustment parameter includes the opening degree of the solenoid valve. When the opening degree of the solenoid valve changes, the flow rate and pressure of the compressed air entering the air spring change, thereby causing the operating state of the air spring to change. Specifically, the operating state of the air spring includes, for example, the stiffness and damping of the air spring.

[0087] In this embodiment, when acquiring vehicle state data, the obtained current state data includes at least two types of data, providing more comprehensive data support for vehicle control. This makes it easier to adjust the vehicle to a steady state under complex road environments and easily changing load conditions when subsequently adjusting the air spring's operating state using the current state data, thereby improving vehicle driving stability. Simultaneously, the use of a quadratic control algorithm to process the current state data ensures that the obtained adjustment parameters accurately respond to changes in vehicle state, enabling rapid adjustment of the vehicle's vertical dynamic balance and improving driving comfort and safety.

[0088] In one possible embodiment of this application, processing the current state data using a preset quadratic control algorithm to obtain adjustment parameters for the air spring includes:

[0089] The current state data and the preset expected indicators are calculated using the quadratic control algorithm to obtain the target control strategy. The expected indicators include indicators when the vehicle state meets the preset conditions, and the target control strategy includes a strategy to converge the vehicle state towards the expected indicators.

[0090] The adjustment parameters are determined based on the target control strategy.

[0091] In one embodiment, the preset conditions can be set according to actual needs. For example, the preset condition could be that the vibration amplitude of the vehicle body does not exceed 1 cm. This embodiment does not impose specific limitations on this.

[0092] In one embodiment, the current vibration amplitude of the vehicle body may be 2cm, and the target control strategy output by the quadratic control algorithm only needs to adjust the vehicle in the direction of reducing the vibration amplitude.

[0093] By adopting this implementation method, the desired index is preset so that when the quadratic control algorithm processes the current state data, it can obtain the target control strategy. Thus, when the air spring is controlled according to the target control strategy, the vehicle's state can be adjusted in a direction that converges with the desired index, thereby improving the vehicle's stability.

[0094] Optionally, in one implementation of this embodiment, the step of calculating the target control strategy by using the quadratic control algorithm based on the current state data and the preset expected index includes:

[0095] The preset vehicle dynamic model is updated based on the current state data, wherein the vehicle dynamic model is generated using historical state data of the vehicle, and the historical state data includes the vehicle state data obtained before the current state data.

[0096] The target control strategy is obtained by calculating the updated vehicle dynamic model and the desired index using the quadratic control algorithm.

[0097] In one embodiment, the vehicle dynamic model is constructed through simulation and generated based on the vehicle's historical state data.

[0098] In one embodiment, the vehicle dynamic model is updated using the current state data, so that the calculation parameters of the quadratic control algorithm are updated accordingly, ensuring the accuracy of the target control strategy output in the next iteration.

[0099] This implementation method pre-defines a vehicle dynamic model, allowing the quadratic control algorithm to directly utilize data from the vehicle dynamic model when determining the target control strategy. This facilitates the quadratic control algorithm obtaining more comprehensive vehicle state data, improving the accuracy and quality of the target control strategy, and thus enhancing vehicle stability.

[0100] Optionally, in one implementation of this embodiment, obtaining the vehicle's current state data includes:

[0101] Acquire raw state data from at least two sensors of the vehicle at a target time, wherein different sensors are used to acquire different types of raw state data;

[0102] The original state data is processed by Kalman filtering to obtain the current state data.

[0103] Using this implementation method, Kalman filtering can effectively denoise the original state data, improve the data quality of the current state data, make it easier to participate in subsequent calculations, improve the accuracy of adjustment parameters, and improve vehicle driving stability.

[0104] Optionally, in one implementation of this embodiment, before performing Kalman filtering on the original state data to obtain the current state data, the method further includes:

[0105] The raw state data is preprocessed, wherein the preprocessing includes at least one of error correction and synchronization time baseline.

[0106] By adopting this implementation method, the original state data is preprocessed first, which helps to improve the quality of the preprocessed original state data, thereby improving the accuracy of subsequent data processing.

[0107] Optionally, in one implementation of this embodiment, the method further includes:

[0108] When performing Kalman filtering on the original state data, the Kalman filter is used to predict the vehicle state after the target time, thus obtaining the predicted state data.

[0109] The predicted state data is processed using the quadratic control algorithm to obtain the predicted adjustment parameters for the air spring.

[0110] The operating state of the air spring is adjusted according to the predicted adjustment parameters.

[0111] This implementation method predicts and adjusts vehicle status, proactively preventing unstable vehicle states under complex road conditions and enhancing the ability to respond to emergencies.

[0112] Optionally, in one implementation of this embodiment, the air spring is connected to an air passage, and a solenoid valve is provided on the air passage;

[0113] The adjustment parameters include the opening degree and / or opening duration of the solenoid valve;

[0114] Adjusting the operating state of the air spring according to the adjustment parameters includes:

[0115] The solenoid valve is controlled according to the opening degree and / or opening duration to adjust the flow rate and / or pressure of compressed air in the air spring.

[0116] This implementation method controls the solenoid valve to change the operating state of the air spring. The control process is simple and convenient, and it is easy to improve the efficiency of restoring stable operation of the vehicle.

[0117] Optionally, in one implementation of this embodiment, the current state data includes multiple types of parameters, wherein the types of parameters include at least two of displacement, vehicle speed, vehicle acceleration, and the tilt angle between the vehicle and the horizontal plane;

[0118] The quadratic control algorithm includes at least one of the linear quadratic adjustment algorithm and the H-infinity control algorithm.

[0119] This application also provides a vehicle control method, which employs the control method described above.

[0120] This application embodiment also provides a vehicle, the vehicle including a frame and a wheel bracket, and an air spring for changing the distance between the frame and the wheel bracket is provided between the frame and the wheel bracket;

[0121] The vehicle also includes a controller, which employs the control method described above.

[0122] Optionally, in one implementation of this embodiment, the air spring can be controlled to extend or compress, with one end of the air spring fixedly connected to the vehicle frame and the other end fixedly connected to the wheel bracket;

[0123] The vehicle also includes an air tank for storing gas for the air springs;

[0124] An air passage connects the air storage tank to the air inlet of the air spring;

[0125] A solenoid valve is installed in the air circuit to control the opening and closing of the air circuit, thereby controlling the inflation and deflation of the air spring, wherein the air spring extends or compresses during inflation and deflation.

[0126] This application also provides a vehicle suspension system, such as... Figure 3 As shown, it includes a parameter acquisition module, a suspension controller (i.e., an ECU suspension controller), a linear quadratic regulator (i.e., an LQR integrated controller), and a solenoid valve. The solenoid valve is used to control the inflation and deflation of the air springs in the suspension system.

[0127] The parameter acquisition module is used to acquire at least two types of current state data that characterize the vehicle state. The parameter acquisition module is connected to the suspension controller and is used to transmit the current state data to the suspension controller.

[0128] The suspension controller is connected to the linear quadratic regulator and is used to process the current state data using the linear quadratic regulator to obtain the adjustment parameters for the solenoid valve.

[0129] The linear quadratic regulator is connected to the solenoid valve and is used to adjust the duty cycle of the solenoid valve according to the adjustment parameters.

[0130] The parameter acquisition module includes an inertial measurement unit (IMU), a locator (GPS), and an encoder to detect the vehicle's position, speed, acceleration, and tilt angle.

[0131] In the above embodiments of this application, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. The steps illustrated in the related flowcharts can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown here. In other words, the order of steps described in the foregoing embodiments is merely an example. Reasonable adjustments to the order of steps based on the content of the embodiments of this application are also within the protection scope of the embodiments of this application.

[0132] In one specific implementation of this application embodiment, the vehicle control method includes the following processing steps:

[0133] like Figure 2 and 3 As shown, the purpose of this implementation method is to design a multi-sensor fusion air spring control system that can accurately and in real-time adjust the vehicle's vertical dynamic balance, especially under complex road conditions and dynamic load conditions, to ensure maximum vehicle driving safety and comfort. The system architecture mainly includes the following core modules:

[0134] 1. Sensor Data Acquisition and Preprocessing Module: This module is responsible for acquiring real-time data from multiple sensors (such as IMU inertial measurement unit, GPS global positioning system, wheel speed sensor, etc.) and performing preliminary preprocessing, including noise removal, error correction, and synchronization of time baseline.

[0135] 2. Data Fusion and Analysis Module: Employing advanced multi-sensor data fusion algorithms, such as Kalman filtering, the module performs fusion analysis on preprocessed sensor data to obtain data including displacement, velocity, acceleration, and tilt angle. In addition, it constructs a comprehensive dynamic model of the vehicle.

[0136] 3. LQR control strategy generation module: Based on the fusion analysis of vehicle state data, as well as the preset comprehensive dynamic model and control performance indicators (such as minimizing vehicle body vibration), the optimal control strategy that makes the vehicle state converge to the desired target is calculated using LQR (linear quadratic regulator) theory.

[0137] 4. Air Spring Dynamic Adjustment Module: This module adjusts the stiffness and damping of the air spring in real time according to the LQR control strategy to adapt to the vehicle's vertical dynamic balance requirements. It changes the spring's elastic properties by precisely controlling the pressure of the gas inside the air spring.

[0138] 5. Intelligent Prevention and Feedback Control Module: The system possesses predictive algorithms that can anticipate potential changes in road conditions based on sensor data trends, enabling proactive preventative control adjustments and reducing reaction time in emergencies. Simultaneously, the system also features self-detection capabilities, automatically identifying and repairing issues to enhance its robustness and reliability.

[0139] The specific steps include the following:

[0140] 1. Initialize the multi-sensor fusion module: Ensure all sensors are properly connected, perform initial data calibration and bias correction. Set data fusion algorithm parameters, such as the dynamic model and noise statistics of the Kalman filter.

[0141] Real-time data acquisition and processing: The sensor module monitors the vehicle status in real time, including but not limited to three-dimensional acceleration, angular rate, position, speed and other information. The data acquisition frequency must meet the real-time control requirements.

[0142] 2. Data fusion to generate vehicle model: The data fusion module processes real-time sensor data to generate a comprehensive model that reflects the current dynamic state of the vehicle.

[0143] 3. LQR control strategy calculation and output: Based on the real-time updated vehicle model, the LQR control strategy generation module calculates the optimal control strategy, including the adjustment amount of air spring parameters.

[0144] 4. Dynamic adjustment of air spring parameters: The calculated control strategy is applied to the air spring, and the spring stiffness and damping are dynamically adjusted in real time by adjusting the flow and pressure of compressed air.

[0145] 5. Intelligent Prevention and Feedback Control: The system continuously monitors data trends and takes preventative control measures in advance based on predictive algorithms. Simultaneously, the self-monitoring module periodically checks the system status to ensure the long-term stability and effectiveness of the control system.

[0146] The sequence numbers or order of description of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0150] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital versatile disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiments of this application can be a non-volatile storage medium; in other words, it can be a non-transient storage medium.

[0151] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the scene data of the current frame in the 3D virtual scene involved in the embodiments of this application, the client's device information, and the scene interaction information are all obtained with full authorization.

[0152] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle control method, characterized in that, The vehicle includes a frame and wheel supports, and an air spring is provided between the frame and the wheel supports for changing the distance between the frame and the wheel supports. The method includes: Obtain the current status data of the vehicle, wherein the current status data includes at least two types of data characterizing the vehicle status; The current state data is processed using a preset quadratic control algorithm to obtain the adjustment parameters for the air spring. The quadratic control algorithm includes algorithms with quadratic performance indicators and / or quadratic integration. Adjust the operating state of the air spring according to the aforementioned adjustment parameters; The process of using a preset quadratic control algorithm to process the current state data to obtain adjustment parameters for the air spring includes: The current state data and the preset expected indicators are calculated using the quadratic control algorithm to obtain the target control strategy. The expected indicators include indicators when the vehicle state meets the preset conditions, and the target control strategy includes a strategy to converge the vehicle state towards the expected indicators. The adjustment parameters are determined according to the target control strategy; The step of calculating the target control strategy by using the current state data and the preset expected index through the quadratic control algorithm includes: The preset vehicle dynamic model is updated based on the current state data, wherein the vehicle dynamic model is generated using historical state data of the vehicle, and the historical state data includes the vehicle state data obtained before the current state data. The target control strategy is obtained by calculating the updated vehicle dynamic model and the desired index using the quadratic control algorithm.

2. The vehicle control method according to claim 1, characterized in that, The acquisition of the vehicle's current status data includes: Acquire raw state data from at least two sensors of the vehicle at a target time, wherein different sensors are used to acquire different types of raw state data; The original state data is processed by Kalman filtering to obtain the current state data.

3. The vehicle control method according to claim 2, characterized in that, Before performing Kalman filtering on the original state data to obtain the current state data, the method further includes: The raw state data is preprocessed, wherein the preprocessing includes at least one of error correction and synchronization time baseline.

4. The vehicle control method according to claim 1, characterized in that, The air spring is connected to an air passage, and a solenoid valve is installed on the air passage; The adjustment parameters include the opening degree and / or opening duration of the solenoid valve; Adjusting the operating state of the air spring according to the adjustment parameters includes: The solenoid valve is controlled according to the opening degree and / or opening duration to adjust the flow rate and / or pressure of compressed air in the air spring.

5. The vehicle control method according to claim 1, characterized in that, The current state data includes multiple types of parameters, including at least two of the following: displacement, vehicle speed, vehicle acceleration, and the vehicle's tilt angle relative to the horizontal plane. The quadratic control algorithm includes at least one of the linear quadratic adjustment algorithm and the H-infinity control algorithm.

6. A method for controlling a vehicle, characterized in that, The control method described in any one of claims 1-5 shall be adopted.

7. A vehicle, characterized in that, The vehicle includes a frame and wheel brackets, and an air spring is provided between the frame and the wheel brackets for changing the distance between the frame and the wheel brackets; The vehicle also includes a controller, which employs the control method described in any one of claims 1-5.

8. The vehicle according to claim 7, characterized in that, The air spring can be controlled to extend or compress, one end of the air spring is fixedly connected to the vehicle frame, and the other end is fixedly connected to the wheel bracket; The vehicle also includes an air tank for storing gas for the air springs; An air passage connects the air storage tank to the air inlet of the air spring; A solenoid valve is installed in the air circuit to control the opening and closing of the air circuit, thereby controlling the inflation and deflation of the air spring, wherein the air spring extends or compresses during inflation and deflation.

9. A suspension system for the vehicle according to claim 7, characterized in that, It includes a parameter acquisition module, a suspension controller, a linear quadratic adjuster, and a solenoid valve, wherein the solenoid valve is used to control the inflation and deflation of the air springs in the suspension system; The parameter acquisition module is used to acquire at least two types of current state data that characterize the vehicle state. The parameter acquisition module is connected to the suspension controller and is used to transmit the current state data to the suspension controller. The suspension controller is connected to the linear quadratic regulator and is used to process the current state data using the linear quadratic regulator to obtain the adjustment parameters for the solenoid valve. The linear quadratic regulator is connected to the solenoid valve and is used to adjust the duty cycle of the solenoid valve according to the adjustment parameters. The parameter acquisition module includes an inertial measurement unit, a positioner, and an encoder.

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