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

Abstract

The invention discloses an ABS control strategy optimization method and system based on indirect tire pressure monitoring. The ABS control strategy optimization method comprises the following steps that S1, vehicle driving state information is collected in real time; s2, the driving state information is processed, and the dynamic behavior of the tire is recognized; s3, estimating the tire pressure state of the wheel; s4, the braking risk in the current driving state is evaluated; s5, ABS control parameters are generated; s6, generating a control instruction and transmitting the control instruction to an ABS actuator; s7, adjusting the pressure of the hydraulic system; s8, evaluating the validity of the ABS control parameters, and adjusting the ABS control parameters based on a feedback result; and S9, outputting and displaying the working state information and the tire pressure information of the ABS. According to the ABS control strategy optimization method and system based on indirect tire pressure monitoring, the control strategy can be dynamically adjusted according to the actual operation state, and the braking performance and safety are improved.

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 automobile industry, the safety performance of vehicles has received increasing attention. As an important part of the modern automobile braking system, ABS (anti-lock braking system) can prevent the wheels from locking during emergency braking and improve the vehicle's handling and safety.

[0003] However, most existing ABS systems rely on fixed control strategies and cannot dynamically adjust to the actual operating status of the vehicle. This static control strategy often seems to be inadequate when faced with complex and changing road conditions and driving habits.

[0004] At the same time, existing ABS systems usually ignore the impact of tire pressure on braking performance. Tire pressure is one of the key factors affecting vehicle braking distance and stability. When tire pressure is abnormal, the friction coefficient between the wheel and the ground will change, causing the control strategy of the ABS system to no longer be applicable, thus affecting the braking effect. Specifically, too high tire pressure will reduce the contact area between the tire and the ground and reduce friction; while too low tire pressure will cause the tire to deform and increase rolling resistance, which is also not conducive to braking performance.

[0005] Although some vehicles are currently equipped with direct tire pressure monitoring systems (TPMS), these systems have problems such as high cost and complex installation. Moreover, direct tire pressure monitoring systems require sensors to be installed in each tire. These sensors are not only expensive, but also require regular battery replacement, which increases the burden on car owners. In addition, these systems cannot provide real-time big data analysis, making it difficult for the system to dynamically adjust the braking strategy according to actual tire pressure changes, thus failing to fully realize the potential of ABS. 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 rotation speed of the four wheels, wheel angular velocity, vehicle speed, longitudinal acceleration, lateral acceleration and steering angle;

[0009] S2, using a Kalman filter algorithm to process the driving state information collected in step S1, 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 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 according to the risk assessment results;

[0013] S6, generating a control command based on the ABS control parameter and transmitting the control command to the ABS actuator;

[0014] S7, ABS actuator adjusts the pressure of the hydraulic system according to the received control command 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, where the state space model includes a state equation and an observation equation;

[0020] The state equation expression is 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] In the formula, 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 estimation value 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 updating 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 updating steps S23 to S25 until the iteration end condition is met, and outputting the processed driving state information to step S27;

[0045] S27, calculating the rate of change of the wheel speed by 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, calculating the slip ratio λ based on the collected wheel angular velocity and vehicle speed:

[0050] λ=(vw-v) / v (9);

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

[0052] S210 , judging 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, and 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 identifies the driving mode through the K-means clustering algorithm, and the expression for updating the cluster center vj is as follows:

[0060] vj=(1 / K)×∑[x i ] (11);

[0061] In the formula, 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 leading to the top event and construct a fault tree;

[0065] S43, assigning a failure probability Pi to each bottom event;

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

[0067] P = Π(1-Pi) (12);

[0068] In the formula, 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 Ziegler-Nichols method is used to optimize the ABS control parameters to determine the gain of the PID controller;

[0071] It specifically includes the following steps:

[0072] S51, setting the integral term I and the differential term D of the PID controller to 0, increasing the proportional term P until the system starts to oscillate, and recording 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] Wherein, Kp represents proportional gain; Ki represents integral gain; Kd represents differential gain.

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

[0077] S61, defining a state space model of the system, the state space model including 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, generating a control instruction 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 the laws of physics 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 of 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 on-board network communication module, a power management module and a data display module;

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

[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 state 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, which not only improves the dynamic stability of the vehicle, but also significantly enhances driving safety and ensures the best braking effect under different driving conditions;

[0090] 2. Adaptive adjustment: The adaptive adjustment function further improves 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 false negatives. In emergency braking or complex road conditions, it can prevent vehicle loss of control due to abnormal tire pressure.

[0092] 4. By dynamically adjusting the control parameters of ABS, 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 the data analysis algorithm, 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 to extend the service life of tires, 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 The present invention is a flowchart of an ABS control strategy optimization method based on indirect tire pressure monitoring. DETAILED DESCRIPTION

[0097] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the invented product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of the present invention, it should also be noted that, unless otherwise clearly 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 it can be an indirect connection through an intermediate medium, or it can be a connection between the 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 are described in detail below in conjunction with 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 rotation 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 an IIR filter) is used to remove high-frequency noise from the driving state information after analog-to-digital conversion, so as to provide a clean and stable input for subsequent data analysis.

[0102] S2, using a Kalman filter algorithm to process the driving state information collected in step S1, 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, where the state space model includes a state equation and an observation equation;

[0105] The state equation expression is 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] In the formula, 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 estimation value 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 updating 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 the 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, calculating the rate of change of the wheel speed by 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, calculating the slip ratio λ based on the collected wheel angular velocity and vehicle speed:

[0135] λ=(vw-v) / v (9);

[0136] In the formula, 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, and 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 , judging 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 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] In the formula, θ0 represents the intercept term, θ1…θn represent the weights of the corresponding input features x1…xn respectively;

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

[0149] vj=(1 / K)×∑[x i ] (11);

[0150] In the formula, 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 leading to the top event and construct a fault tree;

[0155] S43, assigning a failure probability Pi to each bottom event, i.e., the probability of the i-th component failing 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] In the formula, 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. The ABS control parameters include power distribution ratio and anti-lock brake intervention threshold, so as to reduce the increase in braking distance caused by abnormal tire pressure while ensuring the braking effect;

[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, setting the integral term I and the differential term D of the PID controller to 0, increasing the proportional term P until the system starts to oscillate, and recording 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] Wherein, Kp represents proportional gain; Ki represents integral gain; and Kd represents differential gain. S6. Generate control instructions based on ABS control parameters and transmit them to the ABS actuator;

[0167] Step S6 specifically includes the following steps:

[0168] S61, defining a state space model of the system, the state space model including 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, generating a control instruction 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 the laws of physics and the kinematic constraints of the vehicle.

[0172] S7, ABS actuator adjusts the pressure of the hydraulic system according to the received control command 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 of 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 on-board network communication module, a power management module and a data display module;

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

[0179] In this embodiment, the signal acquisition module includes a wheel speed sensor, an acceleration sensor and a steering angle sensor, wherein the wheel speed sensor is used to monitor the rotation speed of the four wheels in real time. When the wheel rotates, the wheel speed sensor generates a pulse signal, the frequency of the pulse is proportional to the rotation speed of the wheel, and the pulse signal is converted into a digital signal to obtain a rotation speed signal; the acceleration sensor is used to monitor the longitudinal and lateral acceleration of the vehicle to assist in analyzing the tire status, and is installed on the vehicle suspension system; the steering angle sensor is used to monitor the steering angle of the vehicle so as 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 state 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 vehicle network communication module is responsible for transmitting data between the above modules. Taking Ethernet communication as an example, the detailed steps are as follows: First, the vehicle network communication module assigns a unique IP address to each device that needs to communicate; second, the TCP / IP protocol stack is used to establish network connection and data transmission rules; then, when data needs to be transmitted from one module to another, the data is encapsulated into Ethernet frames and sent through the vehicle network; finally, after the target module receives the data, it unpacks the frame and extracts the payload. In this process, the 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 the vehicle's real-time information and warnings to the driver. It includes the following steps: the data processing and analysis module packages the processed data into CAN frames; secondly, the communication and display module receives the CAN frames through the CAN bus and parses out specific information; then, according to the preset display logic (the preset display logic in this embodiment includes: when the ABS system is activated, the ABS indicator light on the dashboard lights up. When the tire pressure is abnormal, a warning message appears on the display screen, accompanied by a sound prompt. The display logic can also adjust the display mode of the information according to the vehicle speed. For example, when driving at high speed, only the most important information is displayed), the tire pressure and ABS status key information are converted into graphics or text; finally, it is presented to the driver through the vehicle's display screen (such as the dashboard or the 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 solution of the present invention rather than to limit it. 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 solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An ABS control strategy optimization method 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 rotation speed of the four wheels, wheel angular velocity, vehicle speed, longitudinal acceleration, lateral acceleration and steering angle; S2, using a Kalman filter algorithm to process the driving state information collected in step S1, 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 algorithm; 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 according to the risk assessment results; S6, generating a control command based on the ABS control parameter and transmitting the control command to the ABS actuator; S7, ABS actuator adjusts the pressure of the hydraulic system according to the received control command 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, where the state space model includes a state equation and an observation equation; The state equation expression is as follows: xk=A×x{k-1}+B×u{k-1}+wk(1); The observation equation is expressed as follows: z k=H× x k+ v k(2); In the formula, 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, v k represents the observation noise; S22, initializing the state estimation value 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 updating 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 updating steps S23 to S25 until the iteration end condition is met, and outputting the processed driving state information to step S27; S27, calculating the rate of change of the wheel speed by differential operation: Δω=ωk-ω{k-1}(8); 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; S28, extracting short-term and long-term trends of wheel speed by time series analysis method; S29, calculating the slip ratio λ based on the collected wheel angular velocity and vehicle speed: λ=(vw-V) / v(9); In the formula, vw represents the wheel angular velocity, v represents the vehicle speed; S210 , judging 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, and 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: 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 identifies the driving mode through the K-means clustering algorithm, and the expression for updating the cluster center vj is as follows: vj=(1 / K)×∑[x i ] (11); In the formula, K represents the number of points in cluster j, x i Represents the coordinates of a point.

6. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 5, characterized in that: Step S4 specifically includes the following steps: S41. Define the top event of system failure; S42, identify all failure modes leading to the top event and construct a fault tree; S43, assigning a failure probability Pi to each bottom event; S44. Calculate the failure-free probability P: P = Π(1-Pi)(12); In the formula, 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.

7. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 6, 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, setting the integral term I and the differential term D of the PID controller to 0, increasing the proportional term P until the system starts to oscillate, and recording 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); Wherein, Kp represents proportional gain; Ki represents integral gain; Kd represents differential gain.

8. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 7, characterized in that: Step S6 specifically includes the following steps: S61, defining a state space model of the system, the state space model including 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 TQx +u TMu )dt is minimized, where J represents the quadratic cost function, x represents the current state vector, and u represents the state feedback; S63, generating a control instruction 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 the laws of physics and the kinematic constraints of the vehicle.

9. The ABS control strategy optimization method based on indirect tire pressure monitoring according to claim 8, 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.

10. The system of the ABS control strategy optimization method based on indirect tire pressure monitoring as claimed in claim 9, 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 driving status information of the vehicle; 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 state 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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