Intelligent friction plate and wear detection method

Abstract

This invention discloses an intelligent friction pad and its wear detection method, belonging to the technical field of automotive braking systems. The intelligent friction pad includes: a friction pad body with a limit wear thickness; at least two temperature sensors embedded in the side; a data transmission module; and a data processing chip integrated into the ECU or independently set up, which obtains wear amount and remaining mileage based on temperature signals combined with pre-stored operating conditions and vehicle model parameters. The detection method includes: establishing a correlation model between wear amount and temperature increment through bench testing; calculating the front and rear axle inertia based on vehicle model parameters, and fitting the wear amount formula with the operating condition amplitude coefficient; calculating wear amount by real-time temperature acquisition; calculating remaining lifespan based on wear amount and cumulative mileage; and displaying the data on the instrument panel with tiered alarms. This invention achieves real-time monitoring of friction pad wear and prediction of remaining lifespan, with small detection errors, and is adaptable to multiple vehicle models and operating conditions.

Description

Technical Field

[0001] This invention relates to the field of automotive braking system technology, specifically to an intelligent friction pad and wear detection method, which enables real-time monitoring of friction pad wear and accurate prediction of remaining service life. Background Technology

[0002] Brake pads are a core safety component of a vehicle's braking system, and their wear condition directly affects driving safety. Currently, the maximum wear thickness for mainstream brake pads is 8mm. When the wear reaches this threshold, the brake pads must be replaced promptly; otherwise, it may lead to the risk of brake failure.

[0003] In existing technologies, friction pad wear monitoring mainly employs mechanical contact alarm devices. Metal contacts are pre-installed on the friction pad substrate. When the wear reaches a critical threshold, the contacts make contact with the brake disc, activating the circuit and triggering an alarm. This solution is simple in structure and low in cost, but it has the following technical drawbacks: The monitoring is highly delayed: it can only trigger an alarm when the friction pads wear to a critical state, and it cannot achieve real-time monitoring of the wear process. Users cannot predict the wear progress in advance, and there is a risk of brake failure due to sudden excessive wear. Lack of quantitative monitoring capability: It can only provide a qualitative alarm indicating that the "critical point has been reached", and cannot accurately reflect the current specific amount of wear. Users may find it difficult to grasp the timing of replacement, which may result in material waste due to premature replacement or affect driving safety due to delayed replacement. Poor reliability: Mechanical contacts are susceptible to high temperatures (200-300℃), high-frequency vibration, and dusty environments, resulting in contact oxidation, wear, or jamming, which can cause alarm failure. Limited adaptability: The mechanical contact structure needs to be matched with friction pads of a specific thickness, making it difficult to adapt to friction pads of different vehicle models and different braking conditions, resulting in poor flexibility in mass application.

[0004] With the development of automotive intelligence, users are increasingly demanding higher accuracy, greater intelligence (such as remaining mileage prediction), and greater versatility in friction pad wear detection. Existing electronic detection solutions mostly use a single temperature sensor, which does not consider the correlation between wear and operating parameters. Furthermore, data collected by a single sensor is easily affected by differences in heat dissipation and uneven stress, resulting in significant detection errors.

[0005] Therefore, providing an intelligent friction pad and wear detection method that can achieve real-time wear monitoring, accurate prediction of remaining life, and adaptability to multiple vehicle models and operating conditions is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides an intelligent friction pad and a wear detection method. By employing a dual-temperature sensor layout, a side-drilling embedding process, an inertia calculation model, and an operating condition amplitude coefficient, the invention achieves accurate calculation of friction pad wear and real-time prediction of remaining service life.

[0007] Specifically, this invention embeds at least two temperature sensors on the side of the friction pad body to collect temperature signals during braking; the temperature signals are transmitted to a data processing chip via a data transmission module; the chip calculates the current wear and remaining mileage based on pre-stored operating condition parameters and vehicle model parameters. The dual-sensor layout reduces the random errors of temperature measurement at a single location, the side drilling embedding process ensures the reliability of sensor fixation and the integrity of the friction pad structure, the front axle inertia calculation model and the rear axle inertia calculation model achieve precise matching of braking characteristics for different vehicle models, and the operating condition amplitude coefficient enables accurate calculation of wear under all operating conditions.

[0008] Thus, this invention realizes a complete closed loop from temperature acquisition to wear calculation and remaining life prediction, significantly improving the accuracy, real-time performance and intelligence level of friction plate wear detection.

[0009] According to one aspect of the present invention, a smart friction pad is provided, comprising: The friction pad body has a maximum wear thickness; At least two temperature sensors are embedded in the side of the friction pad body, and the temperature sensors are used to collect temperature signals during the braking process; A data transmission module is connected to the temperature sensor, and the data transmission module is used to transmit the temperature signal to the vehicle's data processing chip; The data processing chip is integrated into the vehicle ECU or set up independently. The data processing chip is used to obtain the current wear amount and remaining driving range of the intelligent friction pad based on the received temperature signal, combined with pre-stored operating condition parameters and vehicle model parameters.

[0010] According to another aspect of the present invention, a wear detection method based on the above-described smart friction pad is provided, comprising the following steps: Through bench tests, peak braking temperatures were collected under different braking conditions, different initial temperatures, and different wear amounts to establish a correlation model between wear amount and temperature increment. The front and rear axle inertia are calculated based on vehicle parameters, and the wear calculation formulas for the front and rear axles are obtained by fitting the working condition amplitude coefficient. During actual driving, the brake temperature signal is collected in real time by the temperature sensor, the temperature increment is calculated, and the result is substituted into the wear calculation formula to obtain the current wear amount. Calculate the remaining service life based on the current wear and tear and the cumulative mileage. The current wear level and remaining service life are displayed on the vehicle's dashboard, and an alarm is triggered when the remaining service life is lower than a preset threshold.

[0011] Compared with the prior art, the present invention has the following beneficial effects: High detection accuracy: The dual-sensor layout reduces the random error of temperature measurement at a single location. Combined with WLTP full-condition data and inertia calculation model, the wear calculation error is ≤0.03mm. High real-time performance: Real-time monitoring of the entire wear range from initial thickness to ultimate wear thickness (0-8mm) is achieved, solving the problem of lag in existing technologies that can only alarm under extreme conditions; Wide adaptability: The front axle inertia calculation model and the rear axle inertia calculation model are built based on the vehicle's curb weight, wheel rolling radius and front and rear axle braking force distribution coefficient, which can accurately adapt to different vehicle models; the operating condition amplitude coefficient covers the entire WLTP operating condition and is suitable for microcars to commercial vehicles. High level of intelligence: Calculates remaining service life based on current wear and accumulated mileage, and dynamically corrects the wear rate according to recent operating conditions, with a remaining mileage prediction error of ≤5%; High structural reliability: The side drilling and embedding process forms an interference fit, which can fix the sensor without additional fasteners. The high-temperature sealant encapsulation ensures the long-term stable operation of the sensor in harsh environments. Excellent user experience: The tiered alarm mechanism (Level 1 alarm, Level 2 alarm) provides different warning levels, making it easier for users to plan replacement and maintenance in advance. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the structure of a smart friction pad provided in an embodiment of the present invention; Figure 2 This is a flowchart of a wear detection method based on a smart friction plate provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] To facilitate understanding of the technical solution of this invention, some technical terms appearing in the specification are explained as follows: Wear The difference in thickness of the friction material of the friction pad from its initial thickness to the thickness reduction after use, expressed in mm; Intelligent friction pad: refers to the friction pad in this invention that integrates a temperature sensor and a data transmission structure, which can provide real-time feedback on its thickness and wear status through temperature monitoring combined with a preset database; IBT (Initial Temperature): The initial temperature of the friction pads before braking begins, in °C; (Temperature increment): Braking temperature increment, calculated using the following formula: , This represents the peak temperature during braking. WLTP test cycle: The global light vehicle test procedure includes four typical test cycles: low-speed urban, medium-speed suburban, high-speed, and aggressive driving. Operating condition amplitude coefficient K: A differential coefficient that adapts to different WLTP operating conditions, pre-stored in the data processing chip, and automatically matched according to the operating conditions; CAN bus: Controller Area Network, a serial communication protocol widely used in automotive electronic control systems; ECU (Electronic Control Unit): The core of the vehicle's electronic control, responsible for collecting operating parameters, processing data, and providing alarm feedback; NTC thermistor: Negative temperature coefficient thermistor, which has the characteristics of high temperature resistance and high accuracy, and is used for temperature signal acquisition. Front axle inertia The calculation is based on the front axle inertia calculation model, and the calculation formula is as follows: ; Rear axle inertia The calculation is based on the rear axle inertia calculation model, and the calculation formula is as follows: ; in, For vehicle curb weight, The radius of the wheel's rolling motion. Front and rear axle braking force distribution coefficient (when accurate data is unavailable) Take 0.7); Remaining service life: The remaining mileage of the friction pad, which is calculated from the remaining thickness and the average wear rate.

[0017] Example 1: Intelligent Friction Plate Structure like Figure 1 As shown, the present invention provides an intelligent friction pad, the core of which is to achieve accurate collection of braking temperature by embedding dual temperature sensors on the side of the friction pad body, providing a data basis for subsequent wear calculation.

[0018] Specifically, the smart friction pad includes the following components: The friction pad body has a maximum wear thickness. In this embodiment, the maximum wear thickness is 8mm, meaning the friction pad needs to be replaced when it wears down from its initial thickness to 8mm. The friction pad body includes a friction material layer and a steel back fixedly connected to the friction material layer. The friction material layer is the part that directly contacts the brake disc and generates friction, and the steel back is used for mounting and fixing.

[0019] It should be noted that in all embodiments of the present invention, 8mm, which is currently the mainstream thickness in the passenger vehicle industry, is used as an example of the ultimate wear thickness for illustration. However, this does not constitute a limitation of the present invention. Those skilled in the art can adaptively adjust the value of the ultimate wear thickness according to the specific specifications of different vehicle models and different friction pad suppliers, and modify the relevant constant terms in the model accordingly, which still falls within the protection scope of the present invention.

[0020] At least two temperature sensors are embedded in the side of the friction pad body to collect temperature signals during braking. In this embodiment, high-temperature resistant NTC thermistors with a temperature resistance rating of not less than 300℃ are used to adapt to the high-temperature environment generated by braking. The at least two temperature sensors are respectively installed in the central and edge regions of the friction surface of the friction pad body to reduce the random error of temperature measurement at a single location. The sensor in the central region collects the temperature of the friction core area, while the sensor in the edge region collects the temperature of the area with faster heat dissipation. The combined effect of the two sensors can more accurately reflect the true thermal load state of the friction pad.

[0021] Temperature sensor installation process: The friction pad body has mounting holes on its side. The temperature sensor is embedded in these mounting holes, with the hole diameter smaller than the sensor's outer diameter to create an interference fit. Specifically, after the friction pad is pressed and cured using conventional processes, holes are drilled on its side (approximately 1-2 mm above the base material). The drilling depth is adapted to the sensor height, and the hole diameter is 0.1-0.2 mm smaller than the sensor's outer diameter, creating a slight interference fit during embedding. This allows the sensor to be initially fixed in the drilled hole without the need for additional mechanical fasteners. After embedding, high-temperature resistant sealant is used to fill the gaps and cure, achieving secure fixation and dust / high-temperature protection.

[0022] The data transmission module is connected to the temperature sensor and transmits the temperature signal to the vehicle's data processing chip. In this embodiment, the data transmission module adopts a dual-mode transmission architecture, including both a wired transmission unit and a wireless transmission unit to adapt to the needs of different vehicle models. The wired transmission unit is a high-temperature shielded cable that is directly connected to the vehicle's CAN bus interface; the wireless transmission unit is a low-power Bluetooth chip (such as CC2541) that is wired to the sensor to achieve wireless signal transmission. The dual-mode redundancy design ensures the reliability of data transmission under complex braking environments.

[0023] A data processing chip, integrated into the vehicle ECU or set up independently, is used to obtain the current wear amount and remaining driving range of the intelligent friction pad based on the received temperature signal, combined with pre-stored operating condition parameters and vehicle model parameters. In this embodiment, the data processing chip adopts two configuration schemes: the basic scheme reuses the chip built into the vehicle ECU (such as Bosch ME9.7) and processes the data with built-in algorithms; the upgraded scheme adds an independent STM32F407 data processing chip, integrated into the vehicle controller, which communicates bidirectionally with the ECU and CAN bus, and pre-stores the fitting formula, WLTP operating condition parameters, and vehicle model core parameters after subsequent model debugging. , , ).

[0024] Specifically, the data processing chip pre-stores a front axle inertia calculation model and a rear axle inertia calculation model. The front axle inertia calculation model is constructed based on the vehicle's curb weight, wheel rolling radius, and front-rear axle braking force distribution coefficient, and is used to calculate the front axle inertia; the rear axle inertia calculation model is constructed based on the vehicle's curb weight, wheel rolling radius, and front-rear axle braking force distribution coefficient, and is used to calculate the rear axle inertia.

[0025] The data processing chip also pre-stores operating condition amplitude coefficients corresponding to different driving conditions, and is used to calculate the current wear amount within the full wear range from the initial thickness to the ultimate wear thickness based on the operating condition amplitude coefficients.

[0026] Example 2: Overall Flowchart of Wear Detection Method like Figure 2 As shown, the present invention provides a wear detection method based on any of the above-mentioned intelligent friction plates, which includes the following five core steps, forming a complete closed loop from model building to real-time detection to user feedback.

[0027] Step S1: Bench Test and Correlation Model Establishment Through bench tests, peak braking temperatures were collected under different braking conditions, different initial temperatures, and different wear levels to establish a correlation model between wear and temperature increment.

[0028] Specifically, a LINK3900 braking test bench was used, equipped with a constant temperature chamber with a temperature control accuracy of ±1℃ and a high-speed data acquisition instrument with a sampling frequency of 100Hz. The operating conditions selected were based on four typical WLTP driving conditions: low-speed urban driving condition (vehicle speed 0-50km / h, deceleration 0.1-0.3g), medium-speed suburban driving condition (vehicle speed 50-90km / h, deceleration 0.2-0.4g), high-speed driving condition (vehicle speed 90-130km / h, deceleration 0.3-0.6g), and aggressive driving condition (vehicle speed ≥100km / h, deceleration 0.4-0.7g).

[0029] Wear The braking temperature range was set to 0-8 mm, with 1 mm intervals, for a total of 9 gradients; the initial braking temperature (IBT) was set to -10℃, 0℃, 10℃, 20℃, 30℃, and 40℃, for a total of 6 gradients. Bench tests were conducted according to the combinations of operating parameters, with each operating condition repeated 3 times. Peak braking temperatures were simultaneously collected using dual sensors. Calculate the temperature increment Outliers deviating from the average by ±5% are removed, and the average of the valid data is taken as the final data to establish the wear measurement. With temperature increment The basic data table of the association model.

[0030] Taking low-speed urban driving conditions as an example (IBT=10℃, deceleration a=0.2g), the collected data are shown in Table 1: Table 1: Low-speed urban operating condition data table

[0031] Step S2: Inertia Calculation and Wear Formula Fitting The front and rear axle inertia are calculated based on vehicle model parameters, and the wear calculation formulas for the front and rear axles are obtained by fitting the amplitude coefficients of the operating conditions.

[0032] Specifically, the front axle inertia is based on the vehicle's curb weight. Wheel rolling radius Front and rear axle braking force distribution coefficient (When accurate data is unavailable) Take 0.7). Calculated using the front axle inertia calculation model: ; The rear axle inertia is calculated using the rear axle inertia calculation model: .

[0033] Combining the massive amounts of data collected under all WLTP operating conditions, the wear rate was found to be ( ) and temperature increment ( ), inertia ( There are complex nonlinear relationships between these functions. After comparing the goodness of fit of various function models (including polynomial, exponential, and power functions), the sine function was finally selected as the basic model for fitting. The reason for choosing the sine function is that experimental data shows that as the amount of wear increases, the rate of increase in temperature exhibits a "saturation" trend of being fast at first and then slowing down, and the nonlinear characteristics of the sine function in a specific range can perfectly simulate this physical phenomenon.

[0034] Based on this, we introduce a pre-stored operating condition amplitude coefficient. To differentiate heat load differences under various operating conditions, and using Python's scikit-learn library to iteratively optimize massive amounts of experimental data, a calculation formula for front and rear axle wear that closely matches the physical process was finally fitted, directly relating to... , , and : Formula for calculating front axle wear: (fit degree) ); Formula for calculating rear axle wear: (fit degree) ); in, Values ​​are matched according to driving conditions: low speed urban area 3.15, medium speed suburban area 3.30, highway 3.50, and aggressive driving 3.65; / The actual inertia of the vehicle model is calculated; 0.009 is the optimized angular frequency coefficient (adapted to an 8mm range); 0.32 is the inertia influence factor, reflecting the nonlinear influence of inertia on wear rate for different vehicle models; 2.6 is a constant term used to correct the initial state. The model construction process strictly follows the principle of combining physical laws with data-driven approaches, ensuring that the wear calculation error is ≤0.03mm for all vehicle models, all working conditions, and the entire 0-8mm range.

[0035] Step S3: Real-time data acquisition and current wear calculation During actual driving, the brake temperature signal is collected in real time by the temperature sensor, the temperature increment is calculated, and the result is substituted into the wear calculation formula to obtain the current wear amount.

[0036] Specifically, during vehicle operation, dual temperature sensors embedded in the side of the friction pads continuously collect brake temperature signals. The data transmission module transmits the temperature signals to the data processing chip via CAN bus or wirelessly. Simultaneously, the ECU collects vehicle speed and accumulated mileage via CAN bus. Parameters such as vehicle model are automatically retrieved by the data processing chip. ) and operating condition amplitude coefficient (Match the corresponding WLTP operating conditions according to real-time vehicle speed and deceleration).

[0037] The data processing chip calculates the current wear level using the following logic: Calculate the temperature increment ΔT: = , = ; Based on vehicle model parameters, the front axle inertia Jfront is calculated using a front axle inertia calculation model, and the rear axle inertia is calculated using a rear axle inertia calculation model. ; Substitute the values ​​into the corresponding shaft's wear calculation formula to directly calculate the current wear amount. , .

[0038] Step S4: Calculation of Remaining Useful Life The remaining service life is calculated based on the current wear and tear and the cumulative mileage.

[0039] Specifically, first, calculate the average wear rate: Calculate the average wear rate: ( (This refers to the current actual wear and tear).

[0040] Then, the average wear rate is adjusted based on recent operating condition distribution: during frequent and aggressive driving. Increase by 10-20%.

[0041] Finally, calculate the remaining service life: calculate the remaining thickness: (In this embodiment, the maximum wear thickness is 8mm); Calculate the remaining service life: .

[0042] Step S5: Data Feedback and Tiered Alarms The current wear level and remaining service life are displayed on the vehicle's dashboard, and an alarm is triggered when the remaining service life is lower than a preset threshold.

[0043] Specifically, current wear and tear With remaining service life The data is transmitted to the vehicle's dashboard via the CAN bus and displayed in real time in digital form for easy viewing by the user.

[0044] The alarm triggering mechanism adopts a tiered alarm system: when When the speed is ≤ the first preset threshold (e.g., 8000km), the instrument panel will trigger the first level alarm (yellow alarm, reminding you to prepare for replacement). when When the speed is ≤ the second preset threshold (e.g., 1500km), a second-level alarm (red alarm, requiring immediate replacement) is triggered to ensure driving safety.

[0045] Example 3: Real-vehicle verification results To verify the reliability, accuracy, and adaptability of this technical solution in actual driving scenarios, three typical vehicle models were selected for real-vehicle road tests, covering all WLTP operating conditions and the 0-8mm full wear range.

[0046] The test subjects were selected from three mainstream vehicle types: small cars ( =800kg, =0.35m, =0.7), mid-to-large SUVs ( =1800kg, =0.40m, =0.72), performance cars ( =2500kg, =0.45m, =0.68). Each type of vehicle is equipped with two sets of the intelligent friction plates of this invention (one set for the front axle and one set for the rear axle).

[0047] The test conditions simulated mixed driving under four WLTP driving conditions, with a cumulative mileage of 20,000 km and an ambient temperature of -10℃ to 40℃, covering urban congestion, suburban ring roads, highway cruising, and intense mountain driving scenarios.

[0048] The verification benchmark is based on the wear data after bench test calibration. Simultaneously, a third-party precision measuring instrument (accuracy 0.001mm) is used to collect the actual wear of the friction plate in real time, and the deviation between the detected value and the benchmark value is compared.

[0049] Wear detection accuracy verification: at different wear stages ( Multiple comparisons were conducted at various points (2mm, 4mm, 6mm, and 8mm). The detection error for wear on the front and rear axles of all three vehicle types was ≤0.028mm, with an average error of 0.021mm, far exceeding the preset target of ≤0.03mm. Performance cars, in particular, showed... Under extreme wear conditions of 8mm, the detection error is only 0.025mm, which meets the accuracy requirements of extreme alarm.

[0050] Verification of the accuracy of remaining service life prediction: Based on the change in wear rate during the test, the predicted remaining service life is compared with the actual remaining service life (in terms of wear rate change). =8mm as the endpoint), the prediction error for all vehicle types is ≤4.8%, with small cars having the smallest prediction error (3.2%), while performance cars have a slightly higher error (4.8%) due to fluctuations in driving conditions, all meeting the preset standard of ≤5%. Under frequent and intense driving scenarios, the prediction accuracy does not decrease significantly after wear rate correction.

[0051] Data transmission stability verification: The entire process of dual-mode transmission data is recorded. Under complex working conditions (high temperature, bumpy mountain roads), the wireless transmission success rate is 99.8% and the wired transmission success rate is 100%. There are no data interruptions or bit errors. The data latency is ≤50ms. Real-time feedback on wear and remaining service life can be achieved.

[0052] Braking performance consistency verification: The braking distance and braking force distribution uniformity of the friction pads installed with the present invention are compared with those of the original ordinary friction pads. The deviation of the emergency braking distance at 200km / h is ≤0.5m, and the deviation of the braking force distribution between the front and rear axles is ≤2%. There is no uneven wear or braking vibration, which proves that the side drilling and embedding process has not damaged the structural stability of the friction pads.

[0053] Real-vehicle verification results show that this technical solution can be accurately adapted to different vehicle models and all driving conditions. The accuracy of wear detection, the accuracy of remaining service life prediction, the stability of data transmission and the compatibility of braking performance all meet the preset targets. There are no structural defects or functional failures, and it is fully capable of actual mass production and application.

[0054] Example 4: Working Condition and Vehicle Model Adaptation Example Table 2 shows examples of inertia calculation and temperature increment under different WLTP operating conditions and vehicle parameters, further illustrating the adaptability of the present invention: Table 2: Operating Conditions and Vehicle Model Compatibility Table

[0055] The technical solution of this invention can also be implemented in various alternative ways, including but not limited to: Sensor type replacement: K-type thermocouples (temperature resistance 600℃) can be used to replace NTC thermistors, which are suitable for commercial vehicle scenarios with higher braking temperatures, while the installation process and data processing logic remain unchanged; Transmission method alternatives: wired transmission can be used only (simplifying the structure and reducing costs) or wireless transmission can be used only (reducing wiring and adapting to the retrofitting of older vehicle models), while the core transmission protocol remains unchanged; Alternative packaging materials: High-temperature resistant ceramic adhesive can be used instead of high-temperature sealant, with comparable packaging effect and protection performance; Remaining service life calculation alternative: Wear rate can be calculated based on historical braking frequency and average deceleration weighting, replacing the existing average rate algorithm and adapting to users with large differences in driving habits; Coefficient value substitution: When accurate data is unavailable, in addition to uniformly using the empirical value of 0.7, more refined presets can be made based on vehicle type to further improve vehicle compatibility accuracy. For example, a preset value for microcars can be made. =0.65, compact car =0.68, mid-to-large SUV =0.72, performance car =0.75, etc. These preset values ​​can also be stored in the data processing chip and used as default parameters.

[0056] Alarm threshold replacement: Alarm thresholds can be customized according to user needs (e.g., the first preset threshold of 8000km / second preset threshold of 1500km can be adjusted to the first preset threshold of 10000km / second preset threshold of 2000km) to adapt to different usage scenarios.

[0057] This technical solution is applicable to friction pads in disc braking systems. By adjusting the sensor embedment depth and model parameters, it can adapt to friction pads with different thicknesses of friction materials (typically 8-15mm). It is not only suitable for passenger cars but can also be extended to commercial vehicles, light trucks, and other vehicle types, demonstrating broad application prospects.

[0058] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

[0059] If a flowchart is used in this application, it is used to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0060] The above provides a detailed description of an intelligent friction plate and wear detection method provided in this application. The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart friction pad, characterized in that, include: The friction pad body has a maximum wear thickness; At least two temperature sensors are embedded in the side of the friction pad body, and the temperature sensors are used to collect temperature signals during the braking process; A data transmission module is connected to the temperature sensor, and the data transmission module is used to transmit the temperature signal to the vehicle's data processing chip; The data processing chip is integrated into the vehicle ECU or set up independently. The data processing chip is used to obtain the current wear amount and remaining driving range of the intelligent friction pad based on the received temperature signal, combined with pre-stored operating condition parameters and vehicle model parameters.

2. The intelligent friction pad according to claim 1, characterized in that, The at least two temperature sensors are respectively installed in the center and edge regions of the friction surface of the friction plate body to reduce the random error of temperature measurement at a single location.

3. The intelligent friction pad according to claim 1, characterized in that, The friction plate body has a mounting hole on its side, and the temperature sensor is embedded in the mounting hole. The diameter of the mounting hole is smaller than the outer diameter of the temperature sensor, forming an interference fit.

4. The intelligent friction pad according to claim 1, characterized in that, The data processing chip has a front axle inertia calculation model and a rear axle inertia calculation model pre-stored. The front axle inertia calculation model is constructed based on the vehicle's curb weight, wheel rolling radius, and front and rear axle braking force distribution coefficient, and is used to calculate the front axle inertia. The rear axle inertia calculation model is constructed based on the vehicle's curb weight, the wheel rolling radius, and the front and rear axle braking force distribution coefficient, and is used to calculate the rear axle inertia.

5. The smart friction pad according to any one of claims 1 to 4, characterized in that, The data processing chip has pre-stored operating condition amplitude coefficients corresponding to different driving conditions; The data processing chip is also used to calculate the current wear amount within the full wear range from the initial thickness to the ultimate wear thickness based on the operating condition amplitude coefficient.

6. A wear detection method based on the intelligent friction plate according to claim 5, characterized in that, Includes the following steps: Through bench tests, peak braking temperatures were collected under different braking conditions, different initial temperatures, and different wear amounts to establish a correlation model between wear amount and temperature increment. The front axle inertia and rear axle inertia are calculated based on the vehicle model parameters, and the wear calculation formulas for the front axle and rear axle are obtained by fitting the amplitude coefficient of the working condition. During actual driving, the brake temperature signal is collected in real time by the temperature sensor, the temperature increment is calculated, and the result is substituted into the wear calculation formula to obtain the current wear amount. Calculate the remaining service life based on the current wear and tear and the cumulative mileage. The current wear level and remaining service life are displayed on the vehicle's dashboard, and an alarm is triggered when the remaining service life is lower than a preset threshold.

7. The wear detection method according to claim 6, characterized in that, The front axle inertia is calculated based on the vehicle's curb weight, wheel rolling radius, and front-to-rear axle braking force distribution coefficient, and is obtained through the front axle inertia calculation model. The rear axle inertia is calculated based on the vehicle curb weight, the wheel rolling radius, and the front and rear axle braking force distribution coefficient, using the rear axle inertia calculation model. The operating condition amplitude coefficient is matched to the corresponding driving conditions based on the real-time vehicle speed and deceleration.

8. The wear detection method according to claim 6, characterized in that, The remaining service life is calculated based on the difference between the ultimate wear thickness and the current wear amount, combined with the average wear rate, which is the ratio of the current wear amount to the cumulative mileage.

9. The wear detection method according to claim 8, characterized in that, It also includes the following steps: correcting the average wear rate based on the recent operating condition distribution, and calculating the remaining service life based on the corrected average wear rate.

10. The wear detection method according to claim 6, characterized in that, The preset thresholds include a first preset threshold and a second preset threshold. The first preset threshold is greater than the second preset threshold. When the remaining service life is lower than the first preset threshold, a first-level alarm is triggered, and when it is lower than the second preset threshold, a second-level alarm is triggered.

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