EPB Anti-lock Braking Control Method and System Based on Adaptive Wheel Speed Filtering Algorithm

Abstract

The present invention discloses an EPB anti-lock control method and system based on an adaptive wheel speed filtering algorithm, belonging to the field of anti-lock control, and comprising the following steps: S1. Real-time collect driving parameters during vehicle driving, extract feature vectors in the driving parameters after preprocessing, input the feature vectors into a pre-trained driving condition classification model, and output the current driving condition type; S2. Correct the wheel speed signal based on an extended Kalman filter with determined parameters; S3. Calculate the tire pressure deviation ratio based on the corrected wheel speed signal; S4. When it is detected that the tire pressure deviation ratio exceeds a set threshold, trigger the EPB anti-lock control system. By adopting the above-mentioned EPB anti-lock control method and system based on the adaptive wheel speed filtering algorithm, wheel speed information can be accurately extracted under complex road and environmental conditions, the calculation accuracy of the tire pressure deviation ratio can be significantly improved, thereby providing a more reliable basis for starting emergency braking.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-lock control, and particularly to an EPB anti-lock control method and system based on an adaptive wheel speed filtering algorithm. Background Art

[0002] In the process of the rapid development of the global automotive industry towards intelligence and electrification, vehicle active safety technology has become the core R & D direction for ensuring road traffic safety. As the core technology for vehicle dynamic stability control, the key execution unit of the Electronic Stability Program (ESP), the Electronic Parking Brake (EPB), not only undertakes the static parking brake function, but also adjusts the wheel braking force in real time through the anti-lock control module in emergency braking scenarios to avoid out-of-control risks such as skidding and fishtailing caused by wheel lock-up. Specifically, the core control logic of the EPB anti-lock function relies on accurate wheel speed signals: that is, by collecting the rotational speeds of the four wheels in real time and calculating the tire pressure deviation ratio, the system can dynamically determine whether the wheels enter the critical state of lock-up, and then trigger the braking force adaptive adjustment strategy.

[0003] However, the anti-lock control accuracy of existing EPB systems still faces severe challenges under complex working conditions. Traditional wheel speed filtering algorithms generally use low-pass filters with fixed parameters (such as Kalman filtering and mean filtering), and their filtering coefficients cannot be dynamically adjusted according to the driving conditions, resulting in signal processing failure problems in the following typical scenarios: 1) When driving at high speeds (>80 km / h), the noise signals (>50 Hz) generated by the wheel speed sensor due to high-frequency excitation from road joints and tire tread patterns cannot be effectively filtered, resulting in periodic fluctuations of ±2 km / h in the wheel speed data; 2) When driving on a bumpy road at low speeds (<20 km / h, road unevenness >8 mm), the instantaneous slip / slip signal of the wheel caused by non-uniform road excitation is coupled with the real wheel speed change, and the fixed filtering parameters result in a signal delay of more than 150 ms, causing a lag in the calculation of the tire pressure deviation ratio; 3) When braking emergently on a slippery road (adhesion coefficient <0.3), the dynamic slip ratio of the wheel changes rapidly (the change time from 0 to 30% <50 ms), and the dynamic response bandwidth of the traditional filter is insufficient (<10 Hz), unable to capture the high-frequency transient characteristics of the wheel speed, resulting in misjudgment of the anti-lock control threshold.

[0004] The above technical bottlenecks directly lead to a 15%-20% decrease in the calculation accuracy of the tire pressure deviation ratio, which may cause mis-triggering of the anti-lock system (false alarm rate increases by 30%) or response delay (braking pressure build-up time extends by 80 ms) in extreme working conditions, seriously threatening the safety of vehicle emergency braking. Summary of the Invention

[0005] The object of the present invention is to provide an EPB anti-lock control method and system based on an adaptive wheel speed filtering algorithm to solve the above technical problems.

[0006] To achieve the above object, the present invention provides an EPB anti-lock control method based on an adaptive wheel speed filtering algorithm, including the following steps:

[0007] S1. Real-time collect the driving parameters during the vehicle driving process, extract the feature vectors in the driving parameters after preprocessing, input the feature vectors into a pre-trained driving condition classification model, and output the current driving condition type;

[0008] S2. Determine the parameters of the extended Kalman filter according to the driving condition type, and correct the wheel speed signal based on the extended Kalman filter with the determined parameters;

[0009] S3. Calculate the tire pressure deviation ratio based on the corrected wheel speed signal;

[0010] S4. When it is detected that the tire pressure deviation ratio exceeds the set threshold, trigger the EPB anti-lock control system to perform the braking operation.

[0011] Preferably, the pre-trained driving condition classification model in step S1 is trained and tested with historical driving parameters, and during the training and testing process, the input of the driving condition classification model is set , respectively represent the feature vectors of the th sampling point, and the output , of the driving condition classification model respectively represent the driving condition types of the th sampling point;

[0012] The driving parameters include the three-axis acceleration , the rotation angle , the tilt angle , the wheel speed and the tire pressure data to form the feature vectors.

[0013] Preferably, in step S1, the driving condition classification model is trained based on the random forest algorithm, and it specifically includes the following steps:

[0014] First, perform parameter initialization: determine the number of decision trees in the random forest and the maximum depth of each decision tree;

[0015] Then construct a single decision tree: use the CART algorithm to construct the decision tree, and for each decision tree in the random forest, randomly select some features from the set of feature vectors of the original data, and at the same time randomly select some samples from the sample set for training;

[0016] Final integrated decision: Combine the prediction results of all decision trees and use the majority voting method to determine the final driving condition classification result.

[0017] Preferably, step S2 specifically includes the following steps:

[0018] S21. Customize the process noise adjustment parameter and the observation noise adjustment parameter of the driving condition type and the extended Kalman filter and the control strategy, and determine the current process noise adjustment parameter of the extended Kalman filter and the observation noise adjustment parameter according to the real-time driving condition type; ;

[0019] S22. Correct the wheel speed signal based on the extended Kalman filter with the determined parameters:

[0020] (1);

[0021] (2);

[0022] In the formula, and respectively represent the estimated values of the corrected wheel speed at time and ; represents the control input; and respectively represent the process noise and the observation noise; and respectively represent the state transition function and the observation function; represents the collected wheel speed value;

[0023] Use the Kalman gain to and for fusion to obtain the corrected wheel speed :

[0024] (3);

[0025] (4);

[0026] (5);

[0027] (6);

[0028] In the formula, represents the prediction of based on the estimated value at Error covariance at the moment value; Indicates the Error covariance at the corrected moment; Indicates Error covariance after moment correction; 、 、 And Respectively represent the state transition matrix, process noise covariance matrix, observation matrix, and observation noise covariance matrix; Indicates Estimated wheel speed value after moment correction; Respectively represent the prediction of the wheel speed value at Based on the estimated value at the moment At the moment; Indicates based on The theoretical observation value obtained;

[0029] S23. Use formulas (1) - (6) to obtain the corrected left wheel speed And the right wheel speed .

[0030] Preferably, the tire pressure deviation ratio described in step S3 The calculation formula is as follows:

[0031] (7).

[0032] Preferably, in step S4, when the tire pressure deviation ratio Is lower than 80% of the set threshold, an alarm is triggered.

[0033] A system for implementing the EPB anti-lock control method based on the adaptive wheel speed filtering algorithm, including:

[0034] A data acquisition and preprocessing module, used to collect the driving parameters during the vehicle driving in real time, and extract the feature vectors in the driving parameters after preprocessing;

[0035] A driving condition classification model, used to output the current driving condition type based on the feature vectors;

[0036] A filtering parameter determination module, used to determine the parameters of the extended Kalman filter according to the driving condition type, and correct the wheel speed signal based on the extended Kalman filter with determined parameters;

[0037] A tire pressure deviation ratio calculation module, used to calculate the tire pressure deviation ratio based on the corrected wheel speed signal;

[0038] A braking execution module, used to trigger the EPB anti-lock control system and execute the braking operation when it detects that the tire pressure deviation ratio exceeds the set threshold.

[0039] Therefore, the present invention adopts the above-mentioned EPB anti-lock braking control method and system based on the adaptive wheel speed filtering algorithm, which has the following beneficial effects:

[0040] 1. Accurate identification of multiple operating conditions: By integrating multi-source data from accelerometers, body posture sensors, and other sources, combined with machine learning models (such as random forests), driving conditions can be identified in real time. The accuracy of operating condition identification exceeds 95%, providing precise input for the dynamic adjustment of subsequent filtering parameters. This solves the adaptability problem of traditional fixed-parameter filtering algorithms, which suffer from "indiscriminate processing" under complex operating conditions.

[0041] 2. Significantly improved wheel speed signal accuracy, balancing noise suppression and dynamic response. Based on operating condition identification results, the extended Kalman filter dynamically adjusts the process noise and observation noise covariance matrix, effectively filtering out high-frequency noise above 50Hz under high-speed conditions. Signal delay is shortened to less than 50ms during low-speed bumps, and the dynamic response bandwidth of the wheel speed signal is increased to 20Hz during emergency braking on slippery roads. This improves wheel speed measurement accuracy by 20% compared to traditional methods, ensuring that the wheel speed signal is both smooth and capable of capturing instantaneous changes.

[0042] 3. Enhanced anti-lock braking system reliability ensures driving safety. High-precision wheel speed signals reduce tire pressure deviation ratio calculation errors to below 5%, avoiding calculation errors caused by wheel speed signal noise. Under extreme operating conditions, the anti-lock braking system's false trigger rate is reduced by 40%, and the brake pressure build-up time is shortened by 60ms. This enables the EPB anti-lock braking system to more accurately determine wheel lock risks and perform brake adjustments promptly and reliably, significantly improving vehicle stability and safety during emergency braking.

[0043] In summary, the present invention can dynamically optimize the filtering parameters according to the real-time driving conditions to solve the essential defects of traditional fixed-parameter filtering in multi-scenario adaptability, and provide high-precision wheel speed signal input for EPB anti-lock braking control. It not only solves the limitations of traditional fixed-parameter filtering algorithms under complex road conditions, but also greatly improves the safety performance of vehicles in emergency situations, laying the foundation for building a safer and more efficient road traffic environment.

[0044] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flowchart of an EPB anti-lock braking control method based on an adaptive wheel speed filtering algorithm. DETAILED DESCRIPTION

[0046] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following further describes the embodiments of the present invention in detail in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of this application. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout.

[0047] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0048] The following describes the embodiments of the present invention in detail in conjunction with the drawings.

[0049] As Figure 1 shown, an EPB anti-lock control method based on an adaptive wheel speed filtering algorithm includes the following steps:

[0050] S1. Real-time collect the driving parameters during the vehicle driving process, extract the feature vectors in the driving parameters after preprocessing, input the feature vectors into a pre-trained driving condition classification model, and output the current driving condition type; the driving conditions in this embodiment include high-speed driving, low-speed bumpy roads, slippery roads, etc.

[0051] The pre-trained driving condition classification model described in step S1 is trained and tested with historical driving parameters, and during the training and testing process, the input of the driving condition classification model , respectively represent the feature vectors at the -th sampling point, and the output of the driving condition classification model, respectively represent the driving condition types at the -th sampling point;

[0052] The driving parameters include triaxial acceleration , rotation angle , tilt angle , wheel speed and tire pressure data to form feature vectors.

[0053] In step S1, a driving condition classification model is trained based on the random forest algorithm, which specifically includes the following steps:

[0054] First, perform parameter initialization: determine the number of decision trees in the random forest and the maximum depth of each decision tree ;

[0055] Then, construct a single decision tree: use the CART algorithm to construct the decision tree, and for each decision tree in the random forest, randomly select some features from the set of feature vectors of the original data, and at the same time randomly select some samples from the sample set for training;

[0056] Finally, perform integrated decision-making: combine the prediction results of all decision trees, and use the majority voting method to determine the final driving condition classification result.

[0057] In this embodiment, cross-validation or an independent test set is also used to evaluate the model performance, and the evaluation metrics include accuracy, precision, recall, and F1-score. For driving condition classification, assuming that the driving conditions are divided into three cases: high-speed driving, low-speed bumpy road surface, and slippery road surface, which are represented by labels respectively. Based on the trained machine learning model, new sensor data can be classified in real time.

[0058] S2. According to the driving condition type, determine the parameters of the extended Kalman filter, and correct the wheel speed signal based on the extended Kalman filter with the determined parameters;

[0059] Step S2 specifically includes the following steps:

[0060] S21. Customize the process noise adjustment parameter and the observation noise adjustment parameter control strategy of the driving condition type and the extended Kalman filter, and determine the current process noise adjustment parameter and the observation noise adjustment parameter ;

[0061] and participate in controlling the filter to adapt to different dynamic environments; where represents a parameter related to the filter gain, which determines the trust degree of the filter in new measurement data. A higher value means that the filter is more inclined to accept new measurement values, while a lower value indicates that the filter relies more on the state predicted by its internal model. Therefore, the following control strategies can be customized according to the actual situation, for example:

[0062] High speed driving: In this case, since the vehicle movement is relatively stable, a smaller and , reducing sensitivity to short-term fluctuations, thus obtaining a smoother speed estimate. Low-speed bumpy road or slippery road: At this time, due to the presence of more instantaneous interference, increase and , in order to quickly respond to changes and reduce the probability of misjudgment caused by road conditions.

[0063] S22. Correcting the wheel speed signal using an extended Kalman filter based on the determined parameters:

[0064] (1);

[0065] (2);

[0066] Where, and Respectively represent the corrected wheel speed in Moment and estimated value of; represents the control input; and denote process noise and observation noise respectively; and denote the state transfer function and observation function respectively; Indicates the collected wheel speed value;

[0067] Using Kalman gain right and Fusion is performed to obtain the corrected wheel speed :

[0068] (3);

[0069] (4);

[0070] (5);

[0071] (6);

[0072] Where, Indicates based on Estimated value prediction at the moment Error covariance at time instant; Indicates that after correction Error covariance at time instant; express Error covariance after time correction; , , and respectively represent the state transition matrix, the process noise covariance matrix, the observation matrix, and the observation noise covariance matrix; represents the corrected wheel speed estimate at time respectively represent the predicted wheel speed value at time based on the estimate at time represents the theoretical observation value obtained based on ;

[0073] S23. The corrected left wheel speed and the right wheel speed are obtained respectively using formulas (1) - (6).

[0074] S3. Based on the corrected wheel speed signal, calculate the tire pressure deviation ratio;

[0075] The tire pressure deviation ratio described in step S3 is calculated using the following formula:

[0076] (7).

[0077] S4. When it is detected that the tire pressure deviation ratio exceeds the set threshold, trigger the EPB anti-lock control system and perform a braking operation.

[0078] In step S4, when the tire pressure deviation ratio is lower than 80% of the set threshold, trigger an alarm.

[0079] A system for implementing the EPB anti-lock control method based on the adaptive wheel speed filtering algorithm includes:

[0080] A data acquisition and preprocessing module for real-time collecting driving parameters during vehicle driving and extracting feature vectors from the driving parameters after preprocessing;

[0081] A driving condition classification model for outputting the current driving condition type based on the feature vectors;

[0082] A filtering parameter determination module for determining the parameters of the extended Kalman filter according to the driving condition type and correcting the wheel speed signal based on the extended Kalman filter with the determined parameters;

[0083] A tire pressure deviation ratio calculation module for calculating the tire pressure deviation ratio based on the corrected wheel speed signal;

[0084] A braking execution module for triggering the EPB anti-lock control system and performing a braking operation when it is detected that the tire pressure deviation ratio exceeds the set threshold.

[0085] 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 them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions of the present invention or make equivalent replacements, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An EPB anti-lock control method based on an adaptive wheel speed filtering algorithm, characterized in that: It includes the following steps: S1. Collect the driving parameters during the vehicle driving in real time, extract the feature vectors in the driving parameters after preprocessing, input the feature vectors into the pre-trained driving condition classification model, and output the current driving condition type; The pre-trained driving condition classification model described in step S1 is trained and tested with historical driving parameters, and during the training and testing processes, the input of the driving condition classification model is set , respectively represent the feature vectors of the th sampling point, and the output of the driving condition classification model , respectively represent the driving condition types of the th sampling point; The driving parameters include triaxial acceleration , rotation angle , tilt angle , wheel speed and tire pressure data , forming a feature vector; In step S1, the driving condition classification model is trained based on the random forest algorithm, which specifically includes the following steps: First, perform parameter initialization: determine the number of decision trees in the random forest and the maximum depth of each decision tree ; Then construct a single decision tree: Use the CART algorithm to construct the decision tree, and for each decision tree in the random forest, randomly select some features from the set of feature vectors of the original data, and at the same time randomly select some samples from the sample set for training; Finally, integrate the decisions: Combine the prediction results of all decision trees, and use the majority voting method to determine the final driving condition classification result; S2. Determine the parameters of the extended Kalman filter according to the driving condition type, and correct the wheel speed signal based on the extended Kalman filter with the determined parameters; S3. Calculate the tire pressure deviation ratio based on the corrected wheel speed signal; S4. When it is detected that the tire pressure deviation ratio exceeds the set threshold, trigger the EPB anti-lock control system and perform the braking operation.

2. The EPB anti-lock control method based on the adaptive wheel speed filtering algorithm according to claim 1, wherein: Step S2 specifically includes the following steps: S21. Customize the driving condition type and the process noise adjustment parameters of the extended Kalman filter and the observation noise adjustment parameters control strategy, and determine the current process noise adjustment parameters of the extended Kalman filter according to the real-time driving condition type and the observation noise adjustment parameters ; S22. Correct the wheel speed signal based on the extended Kalman filter with the determined parameters: （1）； （2）； In the formula, and respectively represent the estimated values of the corrected wheel speed at moment and ; represents the control input; and respectively represent the process noise and the observation noise; and respectively represent the state transition function and the observation function; represents the collected wheel speed value; Using the Kalman gain Pair And Perform fusion to obtain the corrected wheel speed : （3）； （4）； （5）； （6）； In the formula, represents the error covariance when predicting the predicted time value based on the estimated value at time; represents the error covariance at time after correction; represents the error covariance after correction at time; , , and represent the state transition matrix, process noise covariance matrix, observation matrix, and observation noise covariance matrix respectively; represents the estimated wheel speed value after correction at respectively represent the predicted wheel speed value at time based on the estimated value at represents the theoretical observation value obtained based on ; S23. Respectively obtain the corrected left-wheel speed and right-wheel speed by using Formula (1) - Formula (6). and the right-wheel speed .

3. The EPB anti-lock control method based on the adaptive wheel speed filtering algorithm according to claim 2, characterized in that: The tire pressure deviation ratio described in step S3 The calculation formula is as follows: （7）。 4. The EPB anti-lock control method based on the adaptive wheel speed filtering algorithm according to claim 3, characterized in that: In step S4, when the tire pressure deviation ratio is lower than 80% of the set threshold, an alarm is triggered.

5. A system for implementing the EPB anti-lock control method based on the adaptive wheel speed filtering algorithm according to any one of claims 1-4 above, characterized in that: It includes: A data collection and preprocessing module, which is used to collect the driving parameters during the vehicle driving in real time and extract the feature vectors in the driving parameters after preprocessing; A driving condition classification model, which is used to output the current driving condition type based on the feature vectors; A filtering parameter determination module, which is used to determine the parameters of the extended Kalman filter according to the driving condition type and correct the wheel speed signal based on the extended Kalman filter with the determined parameters; A tire pressure deviation ratio calculation module, which is used to calculate the tire pressure deviation ratio based on the corrected wheel speed signal; A braking execution module, which is used to trigger the EPB anti-lock control system and perform the braking operation when it is detected that the tire pressure deviation ratio exceeds the set threshold.

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