ABS control strategy optimization method and system based on indirect tire pressure monitoring

Abstract

The present invention discloses an ABS control strategy optimization method and system based on indirect tire pressure monitoring, which includes the following steps: S1, real-time collection of vehicle driving status information; S2, processing the driving status information and identifying the dynamic behavior of the tires; S3, estimating the tire pressure status of the wheels; S4, assessing the braking risk under the current driving state; S5, generating ABS control parameters; S6, generating control instructions and transmitting them to the ABS actuator; S7, adjusting the pressure of the hydraulic system; S8, assessing the effectiveness of the ABS control parameters and adjusting the ABS control parameters based on the feedback results; S9, outputting and displaying the ABS working status information and tire pressure information. The present invention adopts the above-mentioned ABS control strategy optimization method and system based on indirect tire pressure monitoring, which can dynamically adjust the control strategy according to the actual operating status, thereby improving braking performance and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of ABS control strategy optimization, and in particular to an ABS control strategy optimization method and system based on indirect tire pressure monitoring. Background Art

[0002] With the rapid development of the automotive industry, vehicle safety performance has received increasing attention. As an important component of modern vehicle braking systems, ABS (Anti-lock Braking System) can prevent wheels from locking during emergency braking, improving vehicle controllability and safety.

[0003] However, most existing ABS systems rely on fixed control strategies that are unable to dynamically adjust to the vehicle's actual operating conditions. Such static control strategies often prove inadequate when faced with complex and changing road conditions and driving habits.

[0004] Existing ABS systems also often overlook the impact of tire pressure on braking performance. Tire pressure is a key factor affecting a vehicle's braking distance and stability. Abnormal tire pressure alters the friction coefficient between the wheel and the road, rendering the ABS system's control strategy ineffective and thus affecting braking effectiveness. Specifically, excessively high tire pressure reduces the tire's contact area with the road, reducing friction; while excessively low tire pressure can cause tire deformation and increase rolling resistance, also negatively impacting braking performance.

[0005] While some vehicles are currently equipped with direct tire pressure monitoring systems (TPMS), these systems present challenges such as high cost and complex installation. Furthermore, direct tire pressure monitoring systems require sensors installed in each tire, which are not only expensive but also require regular battery replacement, adding to the burden on vehicle owners. Furthermore, these systems lack real-time big data analysis, making it difficult to dynamically adjust braking strategies based on actual tire pressure changes, thus preventing the full potential of ABS from being realized. Summary of the Invention

[0006] The purpose of the present invention is to provide an ABS control strategy optimization method and system based on indirect tire pressure monitoring to solve the above technical problems.

[0007] To achieve the above object, the present invention provides an ABS control strategy optimization method based on indirect tire pressure monitoring, comprising the following steps:

[0008] S1. Real-time collection of vehicle driving status information and analog-to-digital conversion. The driving status information includes the rotational speed of the four wheels, wheel angular velocity, vehicle speed, longitudinal acceleration, lateral acceleration, and steering angle.

[0009] S2. Processing the driving state information collected in step S1 using a Kalman filter algorithm, and identifying the dynamic behavior of the tire based on the wheel speed information;

[0010] S3, estimating the tire pressure state of the wheel based on the wheel speed using an intelligent decision-making algorithm;

[0011] S4. Consider tire pressure, vehicle speed, and road conditions, and use fault tree analysis to assess the braking risk under the current driving state.

[0012] S5. Generate ABS control parameters based on risk assessment results;

[0013] S6. Generate a control command based on the ABS control parameter and transmit it to the ABS actuator;

[0014] S7, ABS actuator adjusts the pressure of the hydraulic system according to the control command received to prevent the wheels from locking during braking;

[0015] S8, feeding back the braking information of the ABS actuator and evaluating the effectiveness of the ABS control parameters generated in step S5 in combination with the wheel speed and acceleration, and adjusting the ABS control parameters based on the feedback result;

[0016] S9. Output and display ABS working status information and tire pressure information.

[0017] Preferably, in step S1 , a low-pass filter algorithm is used to remove high-frequency noise from the driving state information after analog-to-digital conversion.

[0018] Preferably, step S2 specifically includes the following steps:

[0019] S21. Establish a state space model of the system, which includes a state equation and an observation equation;

[0020] The state equation is expressed as follows:

[0021] xk=A×x{l-1}+B×u{l-1}+wk (1);

[0022] The observation equation is expressed as follows:

[0023] zk=H×xk+vk (2);

[0024] Where xk represents the state vector at time k, x{k-1} represents the state vector at time k-1, k represents the state transfer matrix, B represents the control matrix, u{k-1} represents the control input at time k-1, wk represents the process noise, zk represents the observation vector at time k, H represents the observation matrix, and vk represents the observation noise.

[0025] S22, initializing the state estimate and error covariance of the Kalman filter;

[0026] S23. At each time step, use the state equation to predict the next state and update the error covariance;

[0027] The state prediction expression is as follows:

[0028] x{k|k-1}=A×x{k-1|k-1} (3);

[0029] Where x{k|k-1} represents the state prediction value at time k based on the input information at time k-1, and x{k-1|k-1} represents the state prediction value at time k-1;

[0030] The error covariance update expression is as follows:

[0031] P{k|k-1}=a×P{k-1|k-1}×A T +Q (4);

[0032] Where P{k|k-1} represents the error covariance prediction value at time k, P{k-1|k-1} represents the error covariance prediction value at time k-1, and Q represents the process noise covariance matrix;

[0033] S24. When new observation data is input, the residual is calculated using the observation equation, and the residual is used to update the state estimate and the error covariance;

[0034] The residual expression is as follows:

[0035] yk=zk-H×x{l|k-1} (5);

[0036] Where yk represents the residual;

[0037] The expression for updating the estimated state value is as follows:

[0038] x{k|k}=x{k|k-1}+Kk×yk (6);

[0039] Where x{k|k} represents the updated state estimate at time k; Kk represents the Kalman gain;

[0040] The updated error covariance expression is as follows:

[0041] P{k|k}=(E-Kk×H)×P{k|k-1} (7);

[0042] Where P{k|k} represents the error covariance matrix after the update at time k, E represents the identity matrix, and P{k|k-1} represents the error covariance matrix predicted at time k based on the input information at time k-1.

[0043] S25. Output the filtered optimal state estimate

[0044] S26, iteratively update steps S23 to S25 until the iteration end condition is met, and output the processed driving state information to step S27;

[0045] S27. Calculate the rate of change of the wheel speed using differential operation:

[0046] Δω=ωk-ω{k-1} (8);

[0047] Where Δω represents the rate of change of wheel speed, ωk represents the wheel speed at time k, and ω{k-1} represents the wheel speed at time k-1;

[0048] S28. Extracting short-term and long-term trends of wheel speed by time series analysis method;

[0049] S29. Calculate the slip ratio λ based on the collected wheel angular velocity and vehicle speed:

[0050] λ=(vw-v) / v (9);

[0051] Where vw represents the wheel angular velocity, v represents the vehicle speed;

[0052] S210 : Determine the dynamic behavior of the tire by combining the extracted short-term and long-term trends of the wheel speed and the slip ratio λ.

[0053] Preferably, in step S29, when the extracted short-term trend of the wheel speed exceeds a set first threshold range, it is determined that the wheel is slipping;

[0054] When the wheel change rate exceeds a set second threshold range, it is determined that the wheel is locked;

[0055] When the slip ratio λ is greater than a set third threshold, it is determined that the wheel enters a critical slip state; when the slip ratio λ is less than the set third threshold, it is determined that the wheel is in a normal braking state.

[0056] Preferably, in step S3, the machine learning unit of the intelligent decision-making algorithm uses supervised learning, and its cost function J(θ) is expressed as follows:

[0057] J(θ)=(1 / 2m)×Σ[(hθ(x i )-y i ) 2 ] (10);

[0058] In the formula, m represents the number of training samples, hθ represents the prediction model, and x i Indicates the input wheel speed information, y i Indicates output;

[0059] The intelligent decision-making algorithm uses the K-means clustering algorithm to identify the driving mode, and the expression for updating the cluster center vj is as follows:

[0060] vj=(1 / K)×∑[x i ] (11);

[0061] Where K represents the number of points in cluster j, x i Represents the coordinates of a point.

[0062] Preferably, step S4 specifically includes the following steps:

[0063] S41. Define the top event of system failure;

[0064] S42. Identify all failure modes that lead to the top event and construct a fault tree;

[0065] S43. Assign a failure probability Pi to each bottom event;

[0066] S44. Calculate the failure-free probability P:

[0067] P = Π(1-Pi) (12);

[0068] Where π represents the multiplication symbol, and Pi represents the failure probability of the i-th component or event;

[0069] S45. Determine the source of system risk by analyzing the minimum cut set of the fault tree.

[0070] Preferably, in step S5, the ABS control parameters are optimized using the Ziegler-Nichols method to determine the gain of the PID controller;

[0071] It specifically includes the following steps:

[0072] S51, set the integral term I and the differential term D of the PID controller to 0, increase the proportional term P until the system starts to oscillate, and record the critical proportional gain Ku and the oscillation period Pu at this time;

[0073] S52. Calculate the PID controller parameters according to the formula provided by Ziegler-Nichols:

[0074] Kp=0.6Ku, Ki=2Kp / Pu, Kd=KpPu / 8 (13);

[0075] Where Kp represents the proportional gain; Ki represents the integral gain; and Kd represents the differential gain.

[0076] Preferably, step S6 specifically includes the following steps:

[0077] S61. Define a state space model of the system, where the state space model includes a state transfer matrix A, a control matrix B, a control weight matrix Q, and a state weight matrix R;

[0078] S62, solve the LQR optimal control problem to obtain the control gain matrix N, and make the quadratic cost function J = ∫(x TQx +u TRu )dt is minimized, where J represents the quadratic cost function, x represents the current state vector, and u represents the state feedback;

[0079] S63. Generate a control command using state feedback u=-Nx, where x represents the current state vector and N represents the control gain matrix obtained by solving the Riccati equation;

[0080] S64. Verify compliance with physical laws and the kinematic constraints of the vehicle.

[0081] Preferably, in step S8, when the slip ratio λ is greater than a set third threshold, the braking force of the wheel is reduced according to a set ratio;

[0082] When the slip ratio λ is less than a set third threshold, the braking force of the wheel is increased according to a set ratio.

[0083] The system for the ABS control strategy optimization method based on indirect tire pressure monitoring includes a signal acquisition module, a data processing and analysis module, a control decision module, a communication and display module, an in-vehicle network communication module, a power management module, and a data display module;

[0084] Among them, the signal acquisition module is used to collect the vehicle's driving status information;

[0085] The data processing and analysis module is used to process the driving status information collected by the signal acquisition module through the Kalman filter algorithm;

[0086] The control decision module is used to generate ABS control parameters based on the driving status information processed by the data processing and analysis module;

[0087] The vehicle network communication module is used to upload the ABS control parameters generated by the control decision module to the communication and display module and the vehicle terminal respectively.

[0088] Therefore, the present invention adopts the above-mentioned ABS control strategy optimization method and system based on indirect tire pressure monitoring, which has the following beneficial effects:

[0089] 1. Real-time feedback: The real-time feedback mechanism enables the system to quickly adapt to changes in tire conditions and adjust the ABS control strategy. This not only improves the vehicle's dynamic stability but also significantly enhances driving safety, ensuring optimal braking performance under different driving conditions.

[0090] 2. Adaptive Adjustment: The adaptive adjustment function further enhances driving safety and comfort, thus providing a more personalized braking experience;

[0091] 3. Through efficient data processing algorithms such as differential operations and sliding window averaging, the module can extract useful information from noise and identify abnormal changes in tire pressure. Accurate data analysis can improve the system's recognition accuracy of tire status, thereby reducing false positives and missed positives. In emergency braking or complex road conditions, it can prevent vehicle loss of control due to abnormal tire pressure.

[0092] 4. By dynamically adjusting ABS control parameters, the optimization strategy can automatically adjust the braking force distribution in the case of uneven tire pressure or slippery road conditions to minimize braking distance and improve vehicle stability;

[0093] 5. By continuously optimizing data analysis algorithms, the system can better adapt to different driving conditions and vehicle loads, achieving a more personalized braking experience;

[0094] 6. Through continuous learning and adjustment, the control strategy optimization module helps extend tire service life, reduce maintenance costs, and lay a solid foundation for future intelligent driving technology.

[0095] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 This is a flowchart of an ABS control strategy optimization method based on indirect tire pressure monitoring according to the present invention. DETAILED DESCRIPTION

[0097] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is usually placed when in use. These are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention. In the description of the present invention, it should also be noted that, unless otherwise expressly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0098] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0099] like Figure 1 As shown, an ABS control strategy optimization method based on indirect tire pressure monitoring includes the following steps:

[0100] S1. Real-time collection of vehicle driving status information and analog-to-digital conversion. The driving status information includes the rotational speed of the four wheels, wheel angular velocity, vehicle speed, longitudinal acceleration, lateral acceleration, and steering angle.

[0101] In step S1, a low-pass filter algorithm (such as a moving average or IIR filter) is used to remove high-frequency noise from the driving state information after analog-to-digital conversion, thereby providing a clean and stable input for subsequent data analysis.

[0102] S2. Processing the driving state information collected in step S1 using a Kalman filter algorithm, and identifying the dynamic behavior of the tire based on the wheel speed information;

[0103] Step S2 specifically includes the following steps:

[0104] S21. Establish a state space model of the system, which includes a state equation and an observation equation;

[0105] The state equation is expressed as follows:

[0106] xk=A×x{k-1}+B×u{k-1}+wk (1);

[0107] The observation equation is expressed as follows:

[0108] zk=H×xk+vk (2);

[0109] Where xk represents the state vector at time k, x{k-1} represents the state vector at time k-1, A represents the state transfer matrix, B represents the control matrix, u{k-1} represents the control input at time k-1, wk represents the process noise, zk represents the observation vector at time k, H represents the observation matrix, and vk represents the observation noise.

[0110] S22, initializing the state estimate and error covariance of the Kalman filter;

[0111] S23. At each time step, use the state equation to predict the next state and update the error covariance;

[0112] The state prediction expression is as follows:

[0113] x{k|k-1}=A×x{k-1|k-1} (3);

[0114] Where x{k|k-1} represents the state prediction value at time k based on the input information at time k-1, and x{k-1|k-1} represents the state prediction value at time k-1;

[0115] The error covariance update expression is as follows:

[0116] P{k|k-1}=A×P{k-1|k-1}×A T +Q (4);

[0117] Where P{k|k-1} represents the error covariance prediction value at time k, P{k-1|k-1} represents the error covariance prediction value at time k-1, and Q represents the process noise covariance matrix;

[0118] S24. When new observation data is input, the residual is calculated using the observation equation, and the residual is used to update the state estimate and the error covariance;

[0119] The residual expression is as follows:

[0120] yk=zk-J×x{k|k-1} (5);

[0121] Where yk represents the residual;

[0122] The expression for updating the estimated state value is as follows:

[0123] x{k|k}=x{k|k-1}+Kk×yk (6);

[0124] Where x{k|k} represents the updated state estimate at time k; Kk represents the Kalman gain;

[0125] The updated error covariance expression is as follows:

[0126] P{k|k}=(E-Kk×H)×P{k|k-1} (7);

[0127] Where P{k|k} represents the error covariance matrix after the update at time k, E represents the identity matrix, and P{k|k-1} represents the error covariance matrix predicted at time k based on the input information at time k-1.

[0128] S25. Output the filtered optimal state estimate

[0129] S26, iteratively updating steps S23 to S25 until an iteration end condition is met, and outputting the processed driving state information to step S27, so that the system can accurately estimate the state of the vehicle in the presence of noise;

[0130] S27. Calculate the rate of change of the wheel speed using differential operation:

[0131] Δω=ωk-ω{k-1} (8);

[0132] Where Δω represents the rate of change of wheel speed, ωk represents the wheel speed at time k, and ω{k-1} represents the wheel speed at time k-1;

[0133] S28. Extracting short-term and long-term trends of wheel speed by time series analysis method;

[0134] S29. Calculate the slip ratio λ based on the collected wheel angular velocity and vehicle speed:

[0135] λ=(vw-v) / v (9);

[0136] Where vw represents the wheel angular velocity, v represents the vehicle speed;

[0137] In step S29, when the extracted short-term trend of the wheel speed exceeds a set first threshold range, it is determined that the wheel is slipping;

[0138] When the wheel change rate exceeds a set second threshold range, it is determined that the wheel is locked;

[0139] When the slip ratio λ is greater than a set third threshold, it is determined that the wheel enters a critical slip state; when the slip ratio λ is less than the set third threshold, it is determined that the wheel is in a normal braking state.

[0140] S210 : Determine the dynamic behavior of the tire by combining the extracted short-term and long-term trends of the wheel speed and the slip ratio λ.

[0141] S3, estimating the tire pressure state of the wheel based on the wheel speed using an intelligent decision-making algorithm;

[0142] In step S3, the machine learning unit of the intelligent decision-making algorithm uses supervised learning, and its cost function J(θ) is expressed as follows:

[0143] J(θ)=(1 / 2m)×∑[(hθ(x i )-y i ) 2 ] (10);

[0144] In the formula, m represents the number of training samples, hθ represents the prediction model, and x i Indicates the input wheel speed information, y i Indicates output;

[0145] In this embodiment, the prediction model hθ is a function based on the input feature x and the model parameter θ, and its expression is as follows:

[0146] hθ(x)=θ0+θ1×x1+…+θn×xn

[0147] Where θ0 represents the intercept term, θ1…θn represent the weights of the corresponding input features x1…xn respectively;

[0148] The intelligent decision-making algorithm uses the K-means clustering algorithm to identify the driving mode, and the expression for updating the cluster center vj is as follows:

[0149] vj=(1 / K)×∑[x i ] (11);

[0150] Where K represents the number of points in cluster j, x i Represents the coordinates of a point.

[0151] S4. Consider tire pressure, vehicle speed, and road conditions, and use fault tree analysis to assess the braking risk under the current driving state.

[0152] Step S4 specifically includes the following steps:

[0153] S41. Define the top event of system failure;

[0154] S42. Identify all failure modes that lead to the top event and construct a fault tree;

[0155] S43. Assign a failure probability Pi to each bottom event, i.e., the probability that the i-th component fails within a given time;

[0156] S44. Calculate the failure-free probability P, that is, the probability that the system will not fail within a given time:

[0157] P = Π(1-Pi) (12);

[0158] Where π represents the multiplication symbol, which represents the product of the failure probabilities of all individual components, and Pi represents the failure probability of the i-th component or event;

[0159] S45. Determine the source of system risk by analyzing the minimum cut set of the fault tree.

[0160] S5. Generate ABS control parameters based on the risk assessment results. These parameters include the power distribution ratio and the anti-lock braking intervention threshold, ensuring braking effectiveness while minimizing the increase in braking distance caused by abnormal tire pressure.

[0161] In step S5, the ABS control parameters are optimized using the Ziegler-Nichols method to determine the gain of the PID controller;

[0162] It specifically includes the following steps:

[0163] S51, set the integral term I and the differential term D of the PID controller to 0, increase the proportional term P until the system starts to oscillate, and record the critical proportional gain Ku and the oscillation period Pu at this time;

[0164] S52. Calculate the PID controller parameters according to the formula provided by Ziegler-Nichols:

[0165] Kp=0.6Ku, Ki=2Kp / Pu, Kd=KpPu / 8 (13);

[0166] Where Kp represents the proportional gain, Ki represents the integral gain, and Kd represents the differential gain. S6. Generate a control command based on the ABS control parameters and transmit it to the ABS actuator;

[0167] Step S6 specifically includes the following steps:

[0168] S61. Define a state space model of the system, where the state space model includes a state transfer matrix A, a control matrix B, a control weight matrix Q, and a state weight matrix R;

[0169] S62, solve the LQR optimal control problem to obtain the control gain matrix N, and make the quadratic cost function J = ∫(x TQx +u TRu )dt is minimized, where J represents the quadratic cost function, x represents the current state vector, and u represents the state feedback;

[0170] S63. Generate a control command using state feedback u=-Nx, where x represents the current state vector and N represents the control gain matrix obtained by solving the Riccati equation;

[0171] S64. Verify compliance with physical laws and the kinematic constraints of the vehicle.

[0172] S7, ABS actuator adjusts the pressure of the hydraulic system according to the control command received to prevent the wheels from locking during braking;

[0173] S8, feeding back the braking information of the ABS actuator and evaluating the effectiveness of the ABS control parameters generated in step S5 in combination with the wheel speed and acceleration, and adjusting the ABS control parameters based on the feedback result;

[0174] In step S8, when the slip ratio λ is greater than a set third threshold, the braking force of the wheel is reduced according to a set ratio;

[0175] When the slip ratio λ is less than a set third threshold, the braking force of the wheel is increased according to a set ratio.

[0176] S9. Output and display ABS working status information and tire pressure information.

[0177] The system for the ABS control strategy optimization method based on indirect tire pressure monitoring includes a signal acquisition module, a data processing and analysis module, a control decision module, a communication and display module, an in-vehicle network communication module, a power management module, and a data display module;

[0178] Among them, the signal acquisition module is used to collect the vehicle's driving status information;

[0179] In this embodiment, the signal acquisition module includes a wheel speed sensor, an acceleration sensor, and a steering angle sensor. The wheel speed sensor is used to monitor the rotational speed of the four wheels in real time. When the wheel rotates, the wheel speed sensor generates a pulse signal with a frequency proportional to the wheel rotational speed. The pulse signal is converted into a digital signal to obtain a rotational speed signal. The acceleration sensor is used to monitor the longitudinal and lateral acceleration of the vehicle to assist in analyzing the tire status. It is installed on the vehicle suspension system. The steering angle sensor is used to monitor the vehicle's steering angle to optimize the ABS control strategy when turning.

[0180] The data processing and analysis module is used to process the driving status information collected by the signal acquisition module through the Kalman filter algorithm;

[0181] The control decision module is used to generate ABS control parameters based on the driving status information processed by the data processing and analysis module;

[0182] The vehicle network communication module is used to upload the ABS control parameters generated by the control decision module to the communication and display module and the vehicle terminal respectively.

[0183] In this embodiment, the in-vehicle network communication module is responsible for transmitting data between the aforementioned modules. Taking Ethernet communication as an example, the detailed steps are as follows: First, the in-vehicle network communication module assigns a unique IP address to each device requiring communication. Second, the TCP / IP protocol stack is used to establish network connections and data transmission rules. Then, when data needs to be transferred from one module to another, the data is encapsulated into Ethernet frames and sent over the in-vehicle network. Finally, after receiving the data, the target module decapsulates the frame and extracts the payload. During this process, the in-vehicle network communication module may use congestion control algorithms (such as TCP's congestion window management) to ensure the reliability and efficiency of data transmission.

[0184] The communication and display module uses the CAN communication protocol to convey real-time vehicle information and warnings to the driver. This includes the following steps: the data processing and analysis module packages the processed data into CAN frames; second, the communication and display module receives the CAN frames via the CAN bus and parses the specific information; then, according to the preset display logic (in this embodiment, the preset display logic includes: when the ABS system is activated, the ABS indicator light on the instrument panel lights up. When the tire pressure is abnormal, a warning message appears on the display screen, accompanied by an audio prompt. The display logic can also adjust the display mode of information based on vehicle speed. For example, at highway speeds, only the most important information is displayed), key information such as tire pressure and ABS status is converted into graphics or text; finally, it is presented to the driver through the vehicle's display screen (such as the instrument panel or center console screen).

[0185] The power management module provides stable power supply for all the above modules and monitors the power status to ensure stable operation of the system;

[0186] The data display module is used to obtain data from the vehicle network communication module and present it to the driver in an intuitive manner.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing ABS control strategy based on indirect tire pressure monitoring, characterized by: The following steps are involved: S1. Real-time collection of vehicle driving status information and analog-to-digital conversion. The driving status information includes the rotational speed of the four wheels, wheel angular velocity, vehicle speed, longitudinal acceleration, lateral acceleration, and steering angle. S2. Processing the driving state information collected in step S1 using a Kalman filter algorithm, and identifying the dynamic behavior of the tire based on the wheel speed information; S3, estimating the tire pressure state of the wheel based on the wheel speed using an intelligent decision-making algorithm; In step S3, the machine learning unit of the intelligent decision-making algorithm uses supervised learning, and its cost function J(θ) is expressed as follows: J(θ)=(1 / 2m)×∑[(hθ(x i )-y i ) 2 ] (10)? In the formula, m represents the number of training samples, hθ represents the prediction model, and x i Indicates the input wheel speed information, y i Indicates output; The intelligent decision-making algorithm uses the K-means clustering algorithm to identify the driving mode, and the expression for updating the cluster center vj is as follows: vj=(1 / K)×∑[x i ] (11); Where K represents the number of points in cluster j, x i Represents the coordinates of a point; S4. Consider tire pressure, vehicle speed, and road conditions, and use fault tree analysis to assess the braking risk under the current driving state. S5. Generate ABS control parameters based on risk assessment results; S6. Generate a control command based on the ABS control parameter and transmit it to the ABS actuator; S7, ABS actuator adjusts the pressure of the hydraulic system according to the control command received to prevent the wheels from locking during braking; S8, feeding back the braking information of the ABS actuator and evaluating the effectiveness of the ABS control parameters generated in step S5 in combination with the wheel speed and acceleration, and adjusting the ABS control parameters based on the feedback result; S9. Output and display ABS working status information and tire pressure information.

2. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 1, characterized in that: In step S1 , a low-pass filter algorithm is used to remove high-frequency noise from the driving state information after analog-to-digital conversion.

3. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 2, characterized in that: Step S2 specifically includes the following steps: S21. Establish a state space model of the system, which includes a state equation and an observation equation; The state equation is expressed as follows: xk=A×x{k-1}+B×u{k-1}+wk (1); The observation equation is expressed as follows: zk=H×xk+vk (2); Where xk represents the state vector at time k, x{k-1} represents the state vector at time k-1, A represents the state transfer matrix, B represents the control matrix, u{k-1} represents the control input at time k-1, wk represents the process noise, zk represents the observation vector at time k, H represents the observation matrix, and vk represents the observation noise. S22, initializing the state estimate and error covariance of the Kalman filter; S23. At each time step, use the state equation to predict the next state and update the error covariance; The state prediction expression is as follows: x{k|k-1}=A×x{k-1|k-1} (3); Where x{k|k-1} represents the state prediction value at time k based on the input information at time k-1, and x{k-1|k-1} represents the state prediction value at time k-1; The error covariance update expression is as follows: P{k|k-1}=A×P{k-1|k-1}×A T +Q (4); Where P{k|k-1} represents the error covariance prediction value at time k, P{k-1|k-1} represents the error covariance prediction value at time k-1, and Q represents the process noise covariance matrix; S24. When new observation data is input, the residual is calculated using the observation equation, and the residual is used to update the state estimate and the error covariance; The residual expression is as follows: yk=zk-H×x{k|k-1} (5); Where yk represents the residual; The expression for updating the estimated state value is as follows: x{k|k}=x{k|k-1}+Kk×yk (6); Where x{k|k} represents the updated state estimate at time k; Kk represents the Kalman gain; The updated error covariance expression is as follows: P{k|k}=(E-Kk×H)×P{k|k-1} (7); Where P{k|k} represents the error covariance matrix after the update at time k, E represents the identity matrix, and P{k|k-1} represents the error covariance matrix predicted at time k based on the input information at time k-1. S25. Output the filtered optimal state estimate S26, iteratively update steps S23 to S25 until the iteration end condition is met, and output the processed driving state information to step S27; S27. Calculate the rate of change of the wheel speed using differential operation: Δω=ω{k}-ω{k-1} (8); Where Δω represents the rate of change of wheel speed, ω{k} represents the wheel speed at time k, and ω{k-1} represents the wheel speed at time k-1; S28. Extracting short-term and long-term trends of wheel speed by time series analysis method; S29. Calculate the slip ratio λ based on the collected wheel angular velocity and vehicle speed: λ=(v w -v) / v(9); Where, v w represents the linear velocity of the wheel, and v represents the vehicle speed; S210 : Determine the dynamic behavior of the tire by combining the extracted short-term and long-term trends of the wheel speed and the slip ratio λ.

4. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 3, characterized in that: In step S29, when the extracted short-term trend of the wheel speed exceeds a set first threshold range, it is determined that the wheel is slipping; When the wheel change rate exceeds a set second threshold range, it is determined that the wheel is locked; When the slip ratio λ is greater than a set third threshold, it is determined that the wheel enters a critical slip state; when the slip ratio λ is less than the set third threshold, it is determined that the wheel is in a normal braking state.

5. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 4, characterized in that: Step S4 specifically includes the following steps: S41. Define the top event of system failure; S42. Identify all failure modes that lead to the top event and construct a fault tree; S43. Assign a failure probability Pi to each bottom event; S44. Calculate the failure-free probability P: P = Π(1-Pi) (12); Where Π represents the multiplication symbol, and Pi represents the failure probability of the i-th component or event; S45. Determine the source of system risk by analyzing the minimum cut set of the fault tree.

6. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 5, characterized in that: In step S5, the ABS control parameters are optimized using the Ziegler-Nichols method to determine the gain of the PID controller; It specifically includes the following steps: S51, set the integral term I and the differential term D of the PID controller to 0, increase the proportional term P until the system starts to oscillate, and record the critical proportional gain Ku and the oscillation period Pu at this time; S52. Calculate the PID controller parameters according to the formula provided by Ziegler-Nichols: Kp=0.6Ku, Ki=2Kp / Pu, Kd=KpPu / 8 (13); Where Kp represents the proportional gain; Ki represents the integral gain; and Kd represents the differential gain.

7. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 6, characterized in that: Step S6 specifically includes the following steps: S61. Define a state space model of the system, where the state space model includes a state transfer matrix A, a control matrix B, a control weight matrix Q, and a state weight matrix R; S62, solve the LQR optimal control problem to obtain the control gain matrix N, and make the quadratic cost function J = ∫(x T Qx+u T Ru)dt is minimized, where J represents the quadratic cost function, x represents the current state vector, and u represents the state feedback; S63. Generate a control command using state feedback u=-Nx, where x represents the current state vector and N represents the control gain matrix obtained by solving the Riccati equation; S64. Verify compliance with physical laws and the kinematic constraints of the vehicle.

8. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 7, characterized in that: In step S8, when the slip ratio λ is greater than a set third threshold, the braking force of the wheel is reduced according to a set ratio; When the slip ratio λ is less than a set third threshold, the braking force of the wheel is increased according to a set ratio.

9. The system for optimizing ABS control strategy based on indirect tire pressure monitoring according to claim 8, characterized in that: It includes signal acquisition module, data processing and analysis module, control decision module, communication and display module, vehicle network communication module, power management module and data display module; Among them, the signal acquisition module is used to collect the vehicle's driving status information; The data processing and analysis module is used to process the driving status information collected by the signal acquisition module through the Kalman filter algorithm; The control decision module is used to generate ABS control parameters based on the driving status information processed by the data processing and analysis module; The vehicle network communication module is used to upload the ABS control parameters generated by the control decision module to the communication and display module and the vehicle terminal respectively.

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