WO2024082718A1 - 一种轮胎路面信息的监测方法、装置及系统 - Google Patents
一种轮胎路面信息的监测方法、装置及系统 Download PDFInfo
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- WO2024082718A1 WO2024082718A1 PCT/CN2023/105889 CN2023105889W WO2024082718A1 WO 2024082718 A1 WO2024082718 A1 WO 2024082718A1 CN 2023105889 W CN2023105889 W CN 2023105889W WO 2024082718 A1 WO2024082718 A1 WO 2024082718A1
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- 238000004364 calculation method Methods 0.000 claims description 11
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- 101100233916 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) KAR5 gene Proteins 0.000 description 2
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- 101100012902 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) FIG2 gene Proteins 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/105—Speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
- B60W2530/20—Tyre data
Definitions
- the present invention relates to the field of tire monitoring, and in particular to a method, device and system for monitoring tire road surface information.
- the real-time monitoring of vehicle tires and road conditions is achieved through intelligent tire technology. Most of them are based on the signal diagram obtained by the sensor. By extracting characteristic factors, the mechanical characteristic parameters of the tire are estimated using the neural network algorithm, and the tire dynamic parameters are estimated by combining empirical/semi-empirical/physical models. In the existing technical solutions, the tire pressure, sinking amount and contact time and other state information are used to achieve real-time monitoring of the vertical force, contact footprint length and vehicle speed of the tire.
- this vehicle status monitoring system uses a spring to measure the sinking amount of the tire, but during the rolling process of the tire, the spring, as a flexible body, will not remain upright like a rigid body, resulting in a certain error in the measured sinking amount;
- the acceleration monitoring module is arranged circumferentially, and the designed system is cumbersome; it only realizes the monitoring of the tire force from the tire level, and does not take it into account from the road level, and does not propose a solution for road surface recognition under different working conditions.
- the present invention provides a tire-pavement information monitoring method, device and system, which realize tire data analysis and identification of different road surface information, and improve the accuracy of tire-pavement information monitoring.
- an embodiment of the present invention provides a method for monitoring tire-pavement information, comprising:
- the vehicle speed is calculated based on the tire free rolling radius and the vertical acceleration signal of the tire collected in real time, and the contact time and contact footprint length are calculated based on the vertical acceleration signal and the vehicle speed.
- the contact footprint length, the tire free rolling radius and the tire pressure information collected in real time are combined with the tire semi-empirical model to calculate the vertical force;
- the tire-pavement information is generated in real time according to the road surface information and tire status data; wherein the tire status data includes vehicle speed, contact time, contact footprint length and vertical force.
- the embodiment of the present invention is implemented to collect the flexible sensing signal of the tire in real time, and extract characteristic factors based on the flexible sensing signal.
- the characteristic factors are combined with machine learning fuzzy reasoning algorithms to intelligently identify road surface information, and the flexible sensing technology is used to realize the recognition of road surface information, and the recognition of different road surfaces is realized from the road level.
- the vertical acceleration of the tire is obtained, the vehicle speed is calculated, and the contact time and contact footprint length of the tire are obtained according to the vertical acceleration and the vehicle speed.
- the contact footprint length and tire pressure information are combined with the tire semi-empirical model to estimate the dynamic parameter information such as the vertical force borne by the tire, and the vertical acceleration and tire pressure information are used to realize the real-time monitoring of the dynamic parameters such as vehicle speed, contact time, tire vertical force, contact footprint of the tire data.
- the tire data and different road surface information are monitored in real time, avoiding the use of hardware such as springs to affect the dynamic balance and uniformity of the tire, and improving the precision and accuracy of tire road surface information monitoring.
- the flexible sensing signal of the tire collected in real time is extracted with characteristic factors, and the characteristic factors are combined with the machine learning fuzzy inference algorithm to identify the road surface information, specifically:
- the flexible sensor is used to collect the flexible sensing signal of the tire in real time during the rolling process
- the waveform of the flexible sensing signal is converted into the frequency domain, the power spectrum density mean is extracted, and the power spectrum density mean is used as a characteristic factor;
- the fuzzy rules are obtained to determine the output fuzzy set when the input is preset;
- the road surface classification type is obtained and the road surface information is identified.
- the implementation of the embodiment of the present invention is based on the machine learning fuzzy logic algorithm, combined with the structure of the fuzzy logic classifier, and takes the power spectrum density mean of the signal as input to output different road types. It does not require pre-processing steps such as pre-training of the fuzzy logic classifier model, avoids the need to collect a large amount of data for modeling, and outputs accurate data, thus simplifying the complexity of the operation.
- the flexible sensor signal of the tire during rolling is collected in real time through a flexible sensor, specifically: a number of flexible pressure sensors are installed at a number of preset positions of the tire inner liner, and a pressure divider is installed at the center of the tire inner liner;
- the output resistance value of the tire during rolling is collected in real time through a number of flexible pressure sensors, a number of channels are arranged corresponding to a number of preset positions, the output resistance signal of each channel is obtained through a voltage divider device, and a flexible sensing signal is obtained according to the output resistance signal.
- the working principle of the flexible pressure sensor is that during the pressure process, the change in the contact area between the elastic microstructure of the sensor and the electrode brings about a change in the contact resistance of the sensor.
- the vehicle speed is calculated based on the tire free rolling radius and the vertical acceleration signal of the tire collected in real time, specifically:
- the triaxial acceleration sensor is installed at the center of the tire inner liner and integrated into the acceleration channel of the pressure dividing device.
- the vertical acceleration of the tire during rolling is collected in real time through the triaxial acceleration sensor, and the vertical acceleration signal of the acceleration channel is obtained through the pressure dividing device.
- the rotational centripetal acceleration and the tire free rolling radius are numerically calculated to obtain the vehicle speed.
- the acceleration sensor is integrated into the pressure dividing device, which uses less hardware and avoids overly complex structures, making the system more concise, while the algorithm is simpler and more accurate, and the functions implemented are richer.
- the three-axis acceleration sensor is light in weight and small in size, so it is integrated into the pressure dividing device without affecting the dynamic balance of the tire or changing the inherent characteristics of the tire.
- the three-axis acceleration sensor can collect the three-dimensional acceleration of the tire in real time during the rolling process. By comparing the three acceleration data, it is found that the vertical acceleration signal shows a more obvious regular change, so the vertical acceleration signal is used for later calculations to make the calculated data more accurate.
- the contact time and contact footprint length are calculated based on the vertical acceleration signal and the vehicle speed, specifically:
- the distance difference between the signal peaks is calculated to obtain the touchdown time
- the vehicle speed and contact time are numerically calculated to obtain the contact patch length.
- the contact patch length, tire free rolling radius and tire pressure information collected in real time are combined with the tire semi-empirical model to calculate the vertical force, specifically:
- the vertical stiffness is calculated based on the tire pressure information collected in real time, and the contact patch length, vertical stiffness and tire free rolling radius are substituted into the tire semi-empirical model to calculate the vertical force;
- the tire semi-empirical model is as follows:
- a is the contact patch length
- Ro is the free rolling radius of the tire
- Fz is the vertical force
- Cz is the vertical stiffness
- a1 is the first coefficient to be fitted
- b1 is the second coefficient to be fitted
- m is the first number of times to be fitted
- n is the second number of times to be fitted.
- the tire pressure monitoring device provided by the tire is combined to obtain tire pressure parameters, and the tire semi-empirical model is used to realize real-time monitoring of dynamic parameters such as tire vertical force, thereby avoiding the use of hardware such as springs that may affect the dynamic balance and uniformity of the tire.
- an embodiment of the present invention further provides a tire-pavement information collection and transmission device, comprising: a flexible sensing monitoring module, an acceleration monitoring module, a data calculation module and an information integration module;
- the flexible sensing monitoring module is used to extract characteristic factors from the flexible sensing signals collected from the tires in real time, and combine the characteristic factors with the machine learning fuzzy inference algorithm to identify the road surface information.
- the acceleration monitoring module is used to calculate the vehicle speed based on the tire free rolling radius and the vertical acceleration signal of the tire collected in real time;
- the data calculation module is used to calculate the vertical force by combining the contact patch length, the tire free rolling radius and the tire pressure information collected in real time with the tire semi-empirical model;
- the information integration module is used to generate tire-pavement information in real time based on road surface information and tire status data; wherein the tire status data includes vehicle speed, contact time, contact footprint length and vertical force.
- an embodiment of the present invention further provides a tire-pavement information monitoring system, comprising: a tire-pavement information acquisition and transmission device, a power supply device, a tire pressure monitoring device, a wireless transmission device and a vehicle-mounted terminal device; wherein the tire-pavement information acquisition and transmission device executes a tire-pavement information monitoring method;
- the devices are connected as follows: the tire pressure monitoring device is connected to the tire-road surface information collection and transmission device, the power supply device is connected to the tire-road surface information collection and transmission device, the wireless transmission device is connected to the tire-road surface information collection and transmission device, the power supply device is connected to the wireless transmission device, and the vehicle-mounted terminal device is connected to the wireless transmission device.
- the power supply device is used to supply power to the tire and road surface information collection and transmission device;
- the tire pressure monitoring device is used to provide tire pressure information to the tire-road information collection and transmission device;
- the wireless transmission device is used to send tire and road surface information to the vehicle-mounted terminal device in real time;
- the vehicle-mounted terminal device is used to receive tire and road surface information and display the tire and road surface information in real time during the movement of the tire.
- FIG1 is a flow chart of an embodiment of a method for monitoring tire-pavement information provided by the present invention
- FIG2 is a schematic diagram of attaching a tire two-dimensional cross-sectional pressure sensor according to an embodiment of the tire-road surface information monitoring method provided by the present invention
- FIG3 is a simplified logic diagram of a fuzzy logic algorithm of an embodiment of a tire road surface information monitoring method provided by the present invention
- FIG4 is a diagram showing different road surface identification results of an embodiment of the tire road surface information monitoring method provided by the present invention.
- FIG5 is a detailed diagram of the three-dimensional acceleration signal response of an embodiment of the tire-road surface information monitoring method provided by the present invention.
- FIG6 is a schematic structural diagram of an embodiment of a tire and road surface information collection and transmission device provided by the present invention.
- FIG. 7 is a connection diagram of an embodiment of a tire-pavement information monitoring system provided by the present invention.
- FIG8 is a schematic diagram of the installation position in a tire of an embodiment of the tire road surface information monitoring system provided by the present invention.
- 1 is a tire
- 2 is a rim
- 3 is a spoke
- 4 is a tire pressure sensor
- 5 is a flexible sensor monitoring module
- 6 is a power supply device
- 7 is an acceleration monitoring module.
- FIG1 is a flow chart of a tire road surface information monitoring method provided by an embodiment of the present invention.
- This embodiment monitors tire data and different road surface information in real time by establishing a tire semi-empirical model and mathematical algorithms such as machine learning. Avoid using hardware such as springs to affect the dynamic balance and uniformity of the tire, and improve the precision and accuracy of tire road information monitoring.
- the monitoring method includes steps 101 to 103, and each step is as follows:
- Step 101 extract characteristic factors from the flexible sensing signals of the tire collected in real time, and combine the characteristic factors with a machine learning fuzzy inference algorithm to identify road surface information.
- Step 101 specifically includes step 1011 to step 1013, and each step is specifically as follows:
- Step 1011 collect the flexible sensing signal of the tire in the rolling process in real time through the flexible sensor.
- a number of flexible pressure sensors are installed at a number of preset positions on the tire inner liner, and a voltage divider device is installed at the center of the tire inner liner; the output resistance value of the tire during rolling is collected in real time by means of a number of flexible pressure sensors, a number of channels are arranged corresponding to a number of preset positions, the output resistance signal of each channel is obtained by means of the voltage divider device, and a flexible sensing signal is obtained based on the output resistance signal.
- Flexible sensing monitoring mainly includes pressure sensors (flexible pressure sensors) and pressure dividing devices (including but not limited to pressure dividing devices).
- the schematic diagram of the tire two-dimensional cross-section pressure sensor is shown in Figure 2.
- the pressure sensor is glued to several preset positions of the tire inner liner (such as: center, shoulder and side parts), the pressure dividing device is glued to the center of the inner liner, and the pressure sensor is glued to multiple parts. It is necessary to adopt a multi-channel arrangement method, and the resistance change of each channel is displayed by the pressure dividing device to obtain the output resistance signal of each channel, so as to obtain the flexible sensing signal.
- the working principle of the pressure sensor is that during the pressure process, the change of the contact area between the elastic microstructure of the sensor and the electrode brings about the change of the sensor contact resistance.
- the pressure sensor can collect the signal change of the tire in the rolling process in real time.
- the technical indicators of the pressure sensor in this embodiment are: pressure range: 0-8MPa, nonlinear error: ⁇ 3.1%FS, accuracy error: ⁇ 15%FS, durability:>10 million, working temperature: -20 ⁇ 70°C.
- Step 1012 Perform frequency domain conversion on the waveform of the flexible sensing signal, extract the mean power spectrum density, and use the mean power spectrum density as a characteristic factor.
- the characteristic factor of the flexible sensing signal is extracted, that is, the waveform output by the flexible pressure sensor is converted into a frequency domain, and the power spectrum density mean of the signal is obtained as the characteristic factor.
- Step 1013 Fuzzify the characteristic factors and road surface types to obtain fuzzy sets; obtain fuzzy rules based on the fuzzy sets and determine the output fuzzy sets when the preset input is used; obtain the road surface classification type for the membership function of the preset characteristic factors based on the fuzzy rules and the Mamdani type fuzzy inference algorithm and identify the road surface information.
- the characteristic factor (mean value of power spectrum density) of the flexible sensor signal is used as the input, and the output is different road types such as asphalt road and gravel road.
- the logical diagram of the fuzzy logic algorithm is shown in FIG3, which mainly includes three parts.
- the first step is fuzzification, which is to fuzzify an input and an output of the fuzzy reasoning observer to obtain a fuzzy set and a variable domain range, that is, to fuzzify the mean value of power spectrum density and the road type.
- the second step is fuzzy rules. An input is divided into 16 fuzzy sets, so 16 fuzzy rules can be obtained, thereby obtaining the fuzzy set of the output under the given input.
- the third step is defuzzification.
- the membership function of the output road classification is obtained by determining its membership function, and then the road classification type is obtained.
- the results of different road surface identification are shown in Figure 4.
- the sample sequence is the number of points collected by the flexible pressure sensor. During the test, a flexible pressure sensor was pasted inside the tire. During the rolling process of the tire, the flexible pressure sensor collected data. The sample sequence represents sequence points such as 1, 2, and 3.
- the first classification is asphalt road
- the second classification is gravel road.
- Step 102 Calculate the vehicle speed based on the tire free rolling radius and the vertical acceleration signal of the tire collected in real time, and calculate the contact time and contact footprint length based on the vertical acceleration signal and the vehicle speed. Calculate the vertical force by combining the contact footprint length, the tire free rolling radius and the tire pressure information collected in real time with the tire semi-empirical model.
- step 102 specifically includes step 1021 to step 1024, each step is as follows:
- Step 1021 Calculate the vehicle speed based on the tire free rolling radius and the vertical acceleration signal of the tire collected in real time.
- step 1021 is specifically as follows: install a triaxial acceleration sensor at the center of the tire inner liner and integrate it into the pressure divider.
- the acceleration channel of the equipment collects the vertical acceleration of the tire in the rolling process in real time through a three-axis acceleration sensor, and obtains the vertical acceleration signal of the acceleration channel through a voltage divider device; obtains the vertical acceleration value based on the vertical acceleration signal; calculates the rotational centripetal acceleration based on the vertical acceleration value; and performs numerical calculations on the rotational centripetal acceleration and the free rolling radius of the tire to obtain the vehicle speed.
- the three-axis acceleration sensor in order to obtain the speed of the tire during rolling, is integrated into the pressure-dividing device in the flexible sensing monitoring, and glued to the center of the tire inner liner.
- the acceleration sensor used in this embodiment is a MEMS three-axis acceleration sensor. Since the MEMS three-axis acceleration sensor is light in weight and small in size, it is integrated into the pressure-dividing device, which will not affect the dynamic balance of the tire, nor change the inherent characteristics of the tire.
- the technical indicators of the MEMS three-axis acceleration sensor are as follows: range ⁇ 200g, sampling frequency: 200 ⁇ 3200Hz.
- the MEMS three-axis acceleration sensor can collect the acceleration of the tire in the vertical, circumferential and lateral directions during rolling in real time. Since the vertical acceleration outside the contact area is basically a constant, which is equal to the centripetal acceleration of the wheel rotation, this characteristic can be used to reverse the vehicle speed.
- the vehicle speed (wheel speed) is calculated, where a represents the centripetal acceleration and Ro represents the free rolling radius of the tire.
- Step 1022 Calculate the contact time and contact footprint length according to the vertical acceleration signal and the vehicle speed.
- step 1022 is specifically as follows: according to the vertical acceleration signal, the spacing difference of the signal peaks is calculated to obtain the contact time; and the vehicle speed and the contact time are numerically calculated to obtain the contact footprint length.
- the response details of the three-dimensional acceleration signal collected in real time by the MEMS three-axis acceleration sensor are shown in Figure 5.
- the number of sample collection is the number of points collected by the MEMS three-axis acceleration sensor.
- the MEMS three-axis acceleration sensor is pasted inside the tire.
- the MEMS three-axis acceleration sensor collects data.
- the number of sample collection represents the sequence points 1, 2, 3, etc.
- the first fluctuation line from top to bottom is the vertical acceleration
- the second fluctuation line is the lateral acceleration
- the third fluctuation line is the circumferential acceleration.
- the vertical acceleration data shows a relatively obvious regular change.
- the vertical acceleration data is analyzed, and the tire contact time t is obtained through the peak difference of the signal peak spacing, that is, the signal peak spacing difference.
- the contact footprint length s can be calculated by multiplying the vehicle speed by the contact time.
- Step 1023 The contact patch length, the tire free rolling radius, and the tire pressure information collected in real time are combined with the tire semi-empirical model to calculate the vertical force.
- step 1023 specifically includes: calculating the vertical stiffness according to the tire pressure information collected in real time, substituting the contact patch length, the vertical stiffness and the tire free rolling radius into the tire semi-empirical model, and calculating the vertical force;
- the tire semi-empirical model is as follows:
- a is the contact patch length
- Ro is the free rolling radius of the tire
- Fz is the vertical force
- Cz is the vertical stiffness
- a1 is the first coefficient to be fitted
- b1 is the second coefficient to be fitted
- m is the first number of times to be fitted
- n is the second number of times to be fitted.
- a is the contact patch length (mm)
- Ro is the tire free rolling radius (mm)
- Fz is the vertical load (N)
- Cz is the vertical stiffness (N/mm)
- a1 and b1 are the coefficients to be fitted
- a1 is the first coefficient to be fitted
- b1 is the second coefficient to be fitted
- m and n are the number of times to be fitted
- m is the first number of times to be fitted
- n is the second number of times to be fitted.
- the contact patch length is related to the tire pressure and load.
- the vertical load can be calibrated by the tire pressure and load.
- the vertical force of the tire can be obtained by combining the tire contact patch length, vertical stiffness and tire free rolling radius.
- Step 103 Generate tire-pavement information in real time according to the road surface information and tire status data; wherein the tire status data includes vehicle speed, contact time, contact footprint length and vertical force.
- the embodiment of the present invention is implemented to collect the flexible sensing signal of the tire in real time, and extract characteristic factors based on the flexible sensing signal.
- the characteristic factors are combined with machine learning fuzzy reasoning algorithms to intelligently identify road surface information, and the flexible sensing technology is used to realize the recognition of road surface information, and the recognition of different road surfaces is realized from the road level.
- the vertical acceleration of the tire is obtained, the vehicle speed is calculated, and the contact time and contact footprint length of the tire are obtained according to the vertical acceleration and the vehicle speed.
- the contact footprint length and tire pressure information are combined with the tire semi-empirical model to estimate the dynamic parameter information such as the vertical force borne by the tire, and the vertical acceleration and tire pressure information are used to realize the real-time monitoring of the dynamic parameters such as vehicle speed, contact time, tire vertical force, contact footprint of the tire data.
- the tire data and different road surface information are monitored in real time, avoiding the use of hardware such as springs to affect the dynamic balance and uniformity of the tire, and improving the precision and accuracy of tire road surface information monitoring.
- the tire and pavement information collection and transmission device includes a flexible sensing monitoring module 601, an acceleration monitoring module 602, a data calculation module 603 and an information integration module 604;
- the flexible sensing monitoring module 601 is used to extract characteristic factors from the flexible sensing signals of the tire collected in real time, and identify the road surface information by combining the characteristic factors with the machine learning fuzzy inference algorithm;
- the acceleration monitoring module 602 is used to calculate the vehicle speed according to the tire free rolling radius and the vertical acceleration signal of the tire collected in real time;
- the data calculation module 603 is used to calculate the vertical force by combining the contact patch length, the tire free rolling radius and the tire pressure information collected in real time with the tire semi-empirical model;
- the information integration module 604 is used to generate tire-pavement information in real time according to the road surface information and tire status data; wherein the tire status data includes vehicle speed, contact time, contact footprint length and vertical force.
- a mathematical model algorithm is built and combined with a semi-empirical model of the tire to achieve real-time monitoring of dynamic parameters such as the vertical force of the tire, avoiding the use of hardware such as springs that affect the dynamic balance and uniformity of the tire.
- the acceleration monitoring module used only uses one three-axis acceleration sensor, and the acceleration sensor is integrated in the pressure divider device of the flexible sensing monitoring system to avoid an overly complex structure.
- Figure 7 is a connection diagram of the third embodiment of the tire pavement information monitoring system provided by the present invention.
- Figure 8 a schematic diagram of the installation position of the tire pavement information monitoring system in the tire, the flexible sensing monitoring module and the acceleration monitoring module of the tire pavement information acquisition and transmission device are integrated into the system.
- the tire pavement information monitoring system includes a tire pavement information acquisition and transmission device 701, a power supply device 702, a tire pressure monitoring device 703, a wireless transmission device 704 and a vehicle-mounted terminal device 705; wherein the tire pavement information acquisition and transmission device 701 executes a tire pavement information monitoring method;
- the devices are connected as follows: the tire pressure monitoring device 703 is connected to the tire-pavement information collection and transmission device 701, the power supply device 702 is connected to the tire-pavement information collection and transmission device 701, the wireless transmission device 704 is connected to the tire-pavement information collection and transmission device 701, the power supply device 702 is connected to the wireless transmission device 704, and the vehicle-mounted terminal device 705 is connected to the wireless transmission device 704.
- the power supply device 702 is used to supply power to the tire and road surface information collection and transmission device.
- the power supply device uses a lithium battery, and the power supply device and the voltage divider of the tire and road surface information collection and transmission device are bound together to make the system more concise, mainly for the flexible sensor monitoring module, the pressure regulator, and the pressure regulator in the tire and road surface information collection and transmission device.
- the speed monitoring module, data calculation module, information integration module and wireless transmission device are powered.
- the tire pressure monitoring device 703 is used to provide the tire-road information acquisition and transmission device with tire pressure information.
- the tire pressure monitoring device 703 of the tire itself obtains the tire pressure information of the tire and sends the tire pressure information to the tire road surface information acquisition and transmission device 701 to facilitate the subsequent calculation of the tire road surface information.
- the wireless transmission device 704 is used to send tire and road surface information to the vehicle-mounted terminal device 705 in real time.
- the wireless transmission device 704 communicates with the vehicle-mounted terminal device 705 via Bluetooth transmission.
- the vehicle-mounted terminal device 705 is used to receive tire and road surface information and display the tire and road surface information of the tire in real time during the movement.
- the vehicle-mounted terminal device 705 mainly displays tire data and road surface information of the tire in the movement process.
- the tire data mainly includes vehicle speed, contact time, contact footprint length and vertical force.
- the road surface information includes asphalt road and gravel road, including but not limited to these two types of road surface information.
- the implementation of the present invention proposes a flexible sensing technology, which realizes the recognition of road surface information based on a machine learning algorithm, and the system function is more complete, not only considering the tire level, but also the road level.
- the tire pressure parameters are obtained by combining the tire's own tire pressure monitoring system, and the acceleration sensor is integrated in the pressure distribution device, which uses less hardware, makes the system more concise, and the algorithm is simpler and more accurate, and the functions realized are richer.
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- Measuring Fluid Pressure (AREA)
Abstract
一种轮胎路面信息的监测方法、装置及系统，方法包括对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将特征因子结合机器学习模糊推理算法，识别出路面信息；根据轮胎自由滚动半径和实时采集到轮胎的垂向加速度信号，计算车速，并根据垂向加速度信号和车速，计算接地时间和接地印痕长度，将接地印痕长度、轮胎自由滚动半径和实时采集到轮胎的胎压信息，结合轮胎半经验模型，计算垂向力；根据路面信息和轮胎状态数据，实时生成轮胎路面信息；其中，轮胎状态数据包括车速、接地时间、接地印痕长度和垂向力。实现了轮胎数据分析以及对不同路面信息的识别，提高对轮胎路面信息监测的精准性。
Description
本发明涉及轮胎监测领域，尤其涉及一种轮胎路面信息的监测方法、装置及系统。
近年来，汽车智能化程度需求逐步提升，通过智能化手段得到汽车行驶状态时的轮胎及路面信息的相关数据，对于车联网和智能化驾驶控制策略具有重要意义。随着智能轮胎技术的发展，关于智能轮胎技术的车辆系统，大都是通过单一传感器采集数据，实现的功能受限，且大部分只考虑轮胎层面的状态信息，忽略了轮胎与地面相互作用的状态信息以及如何把智能轮胎与整车进行匹配等问题。
目前，通过智能轮胎技术实现对车辆轮胎及路面状态的实时监测，大都基于传感器得到的信号图，通过提取特征因子，运用神经网络算法估算轮胎力学特性参数，结合经验/半经验/物理模型，对轮胎动力学参数进行估算。现有技术方案中，利用胎压、下沉量和接地时间等状态信息实现对轮胎的垂向力、接地印痕长度和车速的实时监测。存在的缺点在于：此车辆状态监测系统采用了弹簧来测量轮胎的下沉量，但是在轮胎滚动的过程中，弹簧作为柔性体的存在，并不会像刚体那样保持直立，导致测量的下沉量存在一定的误差；加速度监测模块呈周向排列，所设计的系统繁琐；只实现了从轮胎层面监测轮胎的受力情况，并没有从道路层面做出考虑，对不同工况的路面识别并没有提出解决方案。
发明内容
本发明提供了一种轮胎路面信息的监测方法、装置及系统，实现轮胎数据分析以及对不同路面信息的识别，提高对轮胎路面信息监测的精准性。
为了解决上述技术问题，本发明实施例提供了一种轮胎路面信息的监测方法，包括：
对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将特征因子结合机器学习模糊推理算法，识别出路面信息；
根据轮胎自由滚动半径和实时采集到轮胎的垂向加速度信号，计算车速，并根据垂向加速度信号和车速，计算接地时间和接地印痕长度，将接地印痕长度、轮胎自由滚动半径和实时采集到轮胎的胎压信息，结合轮胎半经验模型，计算垂向力；
根据路面信息和轮胎状态数据，实时生成轮胎路面信息；其中，轮胎状态数据包括车速、接地时间、接地印痕长度和垂向力。
实施本发明实施例，实时采集轮胎的柔性传感信号，并根据柔性传感信号，提取特征因子，特征因子结合机器学习模糊推理算法，智能识别路面信息，利用柔性传感技术实现对路面信息的识别，从道路层面，实现对不同路面的识别。获取轮胎垂向的加速度，计算出车速，根据垂向加速度和车速得出轮胎的接地时间和接地印痕长度，将接地印痕长度和胎压信息，结合轮胎半经验模型估算轮胎承受的垂向力等动力学参数信息，利用垂向加速度和胎压信息实现对轮胎数据的车速、接地时间、轮胎垂向力、接地印痕等动力学参数的实时监测。通过建立轮胎半经验模型和机器学习等数学算法，实时监测轮胎数据以及对不同路面信息，避免采用弹簧等硬件，对轮胎动平衡、均匀性产生影响，提高对轮胎路面信息监测的精准性和准确度。
作为优选方案，对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将特征因子结合机器学习模糊推理算法，识别出路面信息，具体为：
通过柔性传感器进行实时采集轮胎在滚动过程中的柔性传感信号；
将柔性传感信号的波形图进行频域转换，提取功率谱密度均值，将功率谱密度均值作为特征因子；
把特征因子和路面类型进行模糊化，获得模糊集合；
根据模糊集合，获得模糊规则，确定当预设输入时的输出模糊集合；
根据模糊规则和Mamdani型模糊推理算法，对于预设特征因子的隶属度函数，得到路面分类类型，识别出路面信息。
实施本发明实施例，基于机器学习模糊逻辑算法，结合模糊逻辑分类器的结构，把信号的功率谱密度均值作为输入，输出不同路面类型，不需要进行模糊逻辑分类器模型的预训练类似的预处理步骤，避免需要采集大量的数据进行建模，才能输出准确数据，简化运算的复杂度。作为优选方案，通过柔性传感器进行实时采集轮胎在滚动过程中的柔性传感信号，具体为：将若干柔性压力传感器安装于轮胎内衬层的若干预设位置，将分压设备安装于轮胎内衬层中心部位；
通过若干柔性压力传感器实时采集轮胎在滚动过程中的输出电阻值，将若干预设位置对应布置若干通道，通过分压设备获取各通道的输出电阻信号，根据输出电阻信号，得到柔性传感信号。
实施本发明实施例，柔性压力传感器的工作原理是在受压过程中，传感器的弹性体微结构与电极接触面积的改变带来传感器接触电阻的改变。
作为优选方案，根据轮胎自由滚动半径和实时采集到轮胎的垂向加速度信号，计算车速，具体为：
将三轴加速度传感器安装于轮胎内衬层中心部位，集成在分压设备的加速度通道，通过三轴加速度传感器进行实时采集轮胎在滚动过程中的垂向加速度，通过分压设备获取加速度通道的垂向加速度信号；
根据垂向加速度信号，获得垂向加速度数值；
根据垂向加速度数值，计算旋转向心加速度；
将旋转向心加速度和轮胎自由滚动半径，进行数值运算，得到车速。
实施本发明实施例，将加速度传感器集成在分压装置上，使用硬件少，避免结构过于复杂，可使得系统更加简洁，同时算法更加简单精准，实现的功能更加丰富。三轴加速度传感器质量轻、尺寸小，故集成在分压装置上，不会影响轮胎的动平衡，也不会改变轮胎的固有特性。三轴加速度传感器可以实时采集轮胎在滚动过程中的三向加速度，通过对三种加速度数据比对，发现垂向加速度信号呈规律变化比较明显，所以使用垂向加速度信号进行后期计算，使得计算数据更为精确。
作为优选方案，并根据垂向加速度信号和车速，计算接地时间和接地印痕长度，具体为：
根据垂向加速度信号，计算信号峰值的间距差，得到接地时间；
将车速和接地时间，进行数值运算，得到接地印痕长度。
作为优选方案，将接地印痕长度、轮胎自由滚动半径和实时采集到轮胎的胎压信息，结合轮胎半经验模型，计算垂向力，具体为：
根据实时采集到轮胎的胎压信息，计算垂向刚度，将接地印痕长度、垂向刚度和轮胎自由滚动半径，代入轮胎半经验模型中，计算垂向力；
轮胎半经验模型，如下式：
其中，a为接地印痕长度，Ro为轮胎自由滚动半径，Fz为垂向力，Cz为垂向刚度，a1为第一待拟合系数，b1为第二待拟合系数，m为第一待拟合次数，n为第二待拟合次数。
实施本发明实施例，结合轮胎自带的胎压监测装置获取胎压参数，以及轮胎半经验模型实现对轮胎垂向力等动力学参数的实时监测，避免采用弹簧等硬件，对轮胎动平衡、均匀性产生影响。
为了解决相同的技术问题，本发明实施例还提供了一种轮胎路面信息采集传输装置，包括：柔性传感监测模块、加速度监测模块、数据计算模块和信息集成模块；
其中，柔性传感监测模块用于对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将特征因子结合机器学习模糊推理算法，识别出路面信息；
加速度监测模块用于根据轮胎自由滚动半径和实时采集到轮胎的垂向加速度信号，计算车速；数据计算模块用于将接地印痕长度、轮胎自由滚动半径和实时采集到轮胎的胎压信息，结合轮胎半经验模型，计算垂向力；
信息集成模块用于根据路面信息和轮胎状态数据，实时生成轮胎路面信息；其中，轮胎状态数据包括车速、接地时间、接地印痕长度和垂向力。
为了解决相同的技术问题，本发明实施例还提供一种轮胎路面信息的监测系统，包括：轮胎路面信息采集传输装置、供电装置、胎压监测装置、无线传输装置和车载终端装置；其中，轮胎路面信息采集传输装置执行轮胎路面信息的监测方法；
其中，装置的连接如下：胎压监测装置与轮胎路面信息采集传输装置连接，供电装置与轮胎路面信息采集传输装置连接，无线传输装置与轮胎路面信息采集传输装置连接，供电装置与无线传输装置连接，车载终端装置与无线传输装置连接。
作为优选方案，供电装置用于为轮胎路面信息采集传输装置供电；
胎压监测装置用于为轮胎路面信息采集传输装置提供获取轮胎的胎压信息；
无线传输装置用于实时将轮胎路面信息发送至车载终端装置；
车载终端装置用于接收轮胎路面信息，实时显示轮胎在运动过程中的轮胎路面信息。
图1：为本发明提供的轮胎路面信息的监测方法的一种实施例的流程示意图；
图2：为本发明提供的轮胎路面信息的监测方法的一种实施例的轮胎二维断面压力传感器黏贴示意图；
图3：为本发明提供的轮胎路面信息的监测方法的一种实施例的模糊逻辑算法逻辑简图；
图4：为本发明提供的轮胎路面信息的监测方法的一种实施例的不同路面辨识结果图；
图5：为本发明提供的轮胎路面信息的监测方法的一种实施例的三向加速度信号响应细节图；
图6：为本发明提供的轮胎路面信息采集传输装置的一种实施例的结构示意图；
图7：为本发明提供的轮胎路面信息的监测系统的一种实施例的连接示意图；
图8：为本发明提供的轮胎路面信息的监测系统的一种实施例的轮胎中的安装位置示意图，
其中，1为轮胎，2为轮辋，3为轮辐，4为胎压传感器，5为柔性传感监测模块，6为供电装置，7为加速度监测模块。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例一
请参照图1，为本发明实施例提供的一种轮胎路面信息的监测方法的流程示意图。本实施例通过建立轮胎半经验模型和机器学习等数学算法，实时监测轮胎数据以及对不同路面信息，
避免采用弹簧等硬件，对轮胎动平衡、均匀性产生影响，提高对轮胎路面信息监测的精准性和准确度。该监测方法包括步骤101至步骤103，各步骤具体如下:
步骤101：对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将特征因子结合机器学习模糊推理算法，识别出路面信息。
步骤101具体包括步骤1011至步骤1013，各步骤具体如下:
步骤1011：通过柔性传感器进行实时采集轮胎在滚动过程中的柔性传感信号。
可选的，将若干柔性压力传感器安装于轮胎内衬层的若干预设位置，将分压设备安装于轮胎内衬层中心部位；通过若干柔性压力传感器实时采集轮胎在滚动过程中的输出电阻值，将若干预设位置对应布置若干通道，通过分压设备获取各通道的输出电阻信号，根据输出电阻信号，得到柔性传感信号。
在本实施例中，为获得轮胎在滚动过程中的柔性传感信号，主要是利用柔性传感监测，柔性传感监测主要包括压力传感器(柔性压力传感器)和分压设备(包括但不限于为分压装置)，轮胎二维断面压力传感器黏贴示意图，如图2所示，压力传感器用胶水黏贴在轮胎内衬层的若干预设位置(如：中心、胎肩和胎侧部位)，分压设备黏贴在内衬层中心部位，压力传感器黏贴在多个部位，需要采用多通道布置方式，通过分压装置显示每个通道的电阻变化，获取每个通道的输出电阻信号，从而得到柔性传感信号。压力传感器的工作原理是在受压过程中，传感器的弹性体微结构与电极接触面积的改变带来传感器接触电阻的改变，压力传感器可以实时采集轮胎在滚动过程中的信号变化，在本实施例中的压力传感器的技术指标为：压强范围：0‐8MPa，非线性误差：<±3.1％FS，精度误差：<±15％FS，耐久性：>1000万，工作温度：‐20～70℃。
步骤1012：将柔性传感信号的波形图进行频域转换，提取功率谱密度均值，将功率谱密度均值作为特征因子。
在本实施例中，提取柔性传感信号的特征因子，即对柔性压力传感器所输出的波形图进行频域转换，获取信号的功率谱密度均值作为特征因子。
步骤1013：把特征因子和路面类型进行模糊化，获得模糊集合；根据模糊集合，获得模糊规则，确定当预设输入时的输出模糊集合；根据模糊规则和Mamdani型模糊推理算法，对于预设特征因子的隶属度函数，得到路面分类类型，识别出路面信息。
在本实施例中，基于模糊逻辑算法，结合模糊逻辑分类器的结构，把柔性传感信号的特征因子(功率谱密度均值)作为输入量，输出量是沥青路、石子路等不同路面类型。模糊逻辑算法逻辑简图，如图3所示，主要包括三个部分，第一步模糊化，通过将模糊推理观测器的一个输入量和一个输出量进行模糊化，得到模糊集合以及可变论域范围，即把功率谱密度均值和路面类型进行模糊化。第二步是模糊规则，一个输入量被分为16个模糊集合，因此可以得到16条模糊规则，从而得到给定输入下的输出的模糊集合。第三步是反模糊，根据第二步的模糊规则，通过Mamdani型模糊推理方法，对于给定的功率谱密度均值，通过确定其隶属度函数从而得到输出量路面分类的隶属度函数，进而得出路面分类类型。不同路面辨识结果，如图4所示，样本序列为柔性压力传感器采集的点数，试验时在轮胎内部粘贴了柔性压力传感器，在轮胎在滚动的过程中，柔性压力传感器采集数据，样本序列表示的是1、2、3等序列点，第一分类为沥青路，第二分类为石子路。
步骤102：根据轮胎自由滚动半径和实时采集到轮胎的垂向加速度信号，计算车速，并根据垂向加速度信号和车速，计算接地时间和接地印痕长度，将接地印痕长度、轮胎自由滚动半径和实时采集到轮胎的胎压信息，结合轮胎半经验模型，计算垂向力。
可选的，步骤102具体包括步骤1021至步骤1024，各步骤具体如下:
步骤1021：根据轮胎自由滚动半径和实时采集到轮胎的垂向加速度信号，计算车速。
可选的，步骤1021具体为：将三轴加速度传感器安装于轮胎内衬层中心部位，集成在分压
设备的加速度通道，通过三轴加速度传感器进行实时采集轮胎在滚动过程中的垂向加速度，通过分压设备获取加速度通道的垂向加速度信号；根据垂向加速度信号，获得垂向加速度数值；根据垂向加速度数值，计算旋转向心加速度；将旋转向心加速度和轮胎自由滚动半径，进行数值运算，得到车速。
在本实施例中，为获得轮胎在滚动过程中的车速，将三轴加速度传感器集成在柔性传感监测中的分压设备上，并用胶水黏贴在轮胎内衬层中心部位，本实施例中具体使用的加速度传感器为MEMS三轴加速度传感器，由于MEMS三轴加速度传感器质量轻、尺寸小，故集成在分压装置上，不会影响轮胎的动平衡，也不会改变轮胎的固有特性。同时，MEMS三轴加速度传感器的技术指标如下：量程±200g，采样频率：200～3200Hz。
MEMS三轴加速度传感器可以实时采集轮胎在滚动过程中的垂向、周向和侧向三个方向的加速度，由于在接地区域外垂向加速度基本为一常值，等于车轮旋转的向心加速度，利用这一特性反求车速，根据计算得到车速(车轮转速)，其中，a代表向心加速度，Ro代表轮胎的自由滚动半径。
步骤1022：根据垂向加速度信号和车速，计算接地时间和接地印痕长度。
可选的，步骤1022具体为：根据垂向加速度信号，计算信号峰值的间距差，得到接地时间；将车速和接地时间，进行数值运算，得到接地印痕长度。
在本实施例中，MEMS三轴加速度传感器实时采集的三向加速度信号响应细节，如图5所示，样本采集数为MEMS三轴加速度传感器采集的点数，试验时在轮胎内部粘贴了MEMS三轴加速度传感器，在轮胎在滚动的过程中，MEMS三轴加速度传感器采集数据，样本采集数表示的是1、2、3等序列点。由上到下的第一条波动线为垂向加速度，第二条波动线为侧向加速度，第三条波动线为周向加速度，通过对三种加速度数据比对，发现垂向加速度信号呈规律变化比较明显，对垂向加速度数据进行分析，通过信号峰值间距的峰值差，即信号峰值的间距差，得出轮胎的接地时间t，将车速乘以接地时间可以计算出接地印痕长度s。
步骤1023：将接地印痕长度、轮胎自由滚动半径和实时采集到轮胎的胎压信息，结合轮胎半经验模型，计算垂向力。
可选的，步骤1023具体为：根据实时采集到轮胎的胎压信息，计算垂向刚度，将接地印痕长度、垂向刚度和轮胎自由滚动半径，代入轮胎半经验模型中，计算垂向力；
轮胎半经验模型，如下式：
其中，a为接地印痕长度，Ro为轮胎自由滚动半径，Fz为垂向力，Cz为垂向刚度，a1为第一待拟合系数，b1为第二待拟合系数，m为第一待拟合次数，n为第二待拟合次数。
在本实施例中，计算垂向力，需要结合轮胎半经验模型(SWIFT TIRE轮胎模型)：其中，a为接地印痕长度(mm)，Ro为轮胎自由滚动半径(mm)，Fz为垂直载荷(N)，Cz为垂向刚度(N/mm)，a1和b1为待拟合的系数，a1为第一待拟合系数，b1为第二待拟合系数，m和n为待拟合的次数，m为第一待拟合次数，n为第二待拟合次数。根据轮胎半经验模型，可知垂直载荷(即垂向力)和胎压的变化将会改变接地印痕长度和宽度，垂向刚度Cz是胎压p的函数，与胎压信息直接
相关，因此接地印痕长度与胎压和载荷相关，垂直载荷可以通过胎压、载荷来标定，结合轮胎接地印痕长度、垂向刚度和轮胎自由滚动半径，即可求得轮胎得垂向力。
步骤103：根据路面信息和轮胎状态数据，实时生成轮胎路面信息；其中，轮胎状态数据包括车速、接地时间、接地印痕长度和垂向力。
实施本发明实施例，实时采集轮胎的柔性传感信号，并根据柔性传感信号，提取特征因子，特征因子结合机器学习模糊推理算法，智能识别路面信息，利用柔性传感技术实现对路面信息的识别，从道路层面，实现对不同路面的识别。获取轮胎垂向的加速度，计算出车速，根据垂向加速度和车速得出轮胎的接地时间和接地印痕长度，将接地印痕长度和胎压信息，结合轮胎半经验模型估算轮胎承受的垂向力等动力学参数信息，利用垂向加速度和胎压信息实现对轮胎数据的车速、接地时间、轮胎垂向力、接地印痕等动力学参数的实时监测。通过建立轮胎半经验模型和机器学习等数学算法，实时监测轮胎数据以及对不同路面信息，避免采用弹簧等硬件，对轮胎动平衡、均匀性产生影响，提高对轮胎路面信息监测的精准性和准确度。
实施例二
相应地，参见图6，图6是本发明提供的轮胎路面信息采集传输装置的实施例二的结构示意图。如图6所示，轮胎路面信息采集传输装置包括柔性传感监测模块601、加速度监测模块602、数据计算模块603和信息集成模块604；
其中，柔性传感监测模块601用于对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将特征因子结合机器学习模糊推理算法，识别出路面信息；
加速度监测模块602用于根据轮胎自由滚动半径和实时采集到轮胎的垂向加速度信号，计算车速；
数据计算模块603用于将接地印痕长度、轮胎自由滚动半径和实时采集到轮胎的胎压信息，结合轮胎半经验模型，计算垂向力；
信息集成模块604用于根据路面信息和轮胎状态数据，实时生成轮胎路面信息；其中，轮胎状态数据包括车速、接地时间、接地印痕长度和垂向力。
实施本发明实施例，通过搭建数学模型算法，结合轮胎半经验模型实现对轮胎垂向力等动力学参数的实时监测，避免采用弹簧等硬件，对轮胎动平衡、均匀性产生影响。采用的加速度监测模块只用一个三轴加速度传感器，且加速度传感器集成在柔性传感监测系统的分压装置上，避免结构过于复杂。通过轮胎路面信息采集传输装置，结合机器学习算法，从道路层面，实现对不同路面的识别。
实施例三
相应地，参见图7，图7是本发明提供的轮胎路面信息的监测系统的实施例三的连接示意图。如图8所示，轮胎路面信息的监测系统在轮胎中的安装位置示意图，将轮胎路面信息采集传输装置的柔性传感监测模块和加速度监测模块集成在系统中。如图7所示，轮胎路面信息的监测系统包括轮胎路面信息采集传输装置701、供电装置702、胎压监测装置703、无线传输装置704和车载终端装置705；其中，轮胎路面信息采集传输装置701执行轮胎路面信息的监测方法；
其中，装置的连接如下：胎压监测装置703与轮胎路面信息采集传输装置701连接，供电装置702与轮胎路面信息采集传输装置701连接，无线传输装置704与轮胎路面信息采集传输装置701连接，供电装置702与无线传输装置704连接，车载终端装置705与无线传输装置704连接。
供电装置702用于为轮胎路面信息采集传输装置供电。
在本实施例中，供电装置选用锂电池，供电装置和轮胎路面信息采集传输装置的分压设备绑定在一起，使系统更加简洁，主要为轮胎路面信息采集传输装置中的柔性传感监测模块、加
速度监测模块、数据计算模块、信息集成模块和无线传输装置供电。
胎压监测装置703用于为轮胎路面信息采集传输装置提供获取轮胎的胎压信息。
在本实施例中，轮胎本身自带的胎压监测装置703获取轮胎的胎压信息，将胎压信息发送至轮胎路面信息采集传输装置701中，便于后期计算轮胎路面信息。
无线传输装置704用于实时将轮胎路面信息发送至车载终端装置705。
在本实施例中，无线传输装置704通过蓝牙传输与车载终端装置705进行通信。
车载终端装置705用于接收轮胎路面信息，实时显示轮胎在运动过程中的轮胎路面信息。在本实施例中，车载终端装置705主要显示轮胎在运动过程中的轮胎数据和路面信息，轮胎数据主要是车速、接地时间、接地印痕长度和垂向力，路面信息是沥青路和石子路，包括但不限于这两种路面信息。
实施本发明实施例，提出一种柔性传感技术，基于机器学习算法，实现对路面信息的识别，系统功能更加完善，不仅考虑轮胎层面，也考虑到道路层面。结合轮胎自带的胎压监测系统获取胎压参数，加速度传感器集成在分压装置上，使用硬件少，系统更加简洁，同时算法更加简单精准，实现的功能更加丰富。
以上所述的具体实施例，对本发明的目的、技术方案和有益效果进行了进一步的详细说明，应当理解，以上所述仅为本发明的具体实施例而已，并不用于限定本发明的保护范围。特别指出，对于本领域技术人员来说，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
Claims (9)
- 一种轮胎路面信息的监测方法，其特征在于，包括：对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将所述特征因子结合机器学习模糊推理算法，识别出路面信息；根据轮胎自由滚动半径和实时采集到所述轮胎的垂向加速度信号，计算车速，并根据所述垂向加速度信号和所述车速，计算接地时间和接地印痕长度，将所述接地印痕长度、所述轮胎自由滚动半径和实时采集到所述轮胎的胎压信息，结合轮胎半经验模型，计算垂向力；根据所述路面信息和轮胎状态数据，实时生成轮胎路面信息；其中，所述轮胎状态数据包括所述车速、所述接地时间、所述接地印痕长度和所述垂向力。
- 如权利要求1所述的轮胎路面信息的监测方法，其特征在于，所述对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将所述特征因子结合机器学习模糊推理算法，识别出路面信息，具体为：通过柔性传感器进行实时采集所述轮胎在滚动过程中的所述柔性传感信号；将所述柔性传感信号的波形图进行频域转换，提取功率谱密度均值，将所述功率谱密度均值作为特征因子；把所述特征因子和路面类型进行模糊化，获得模糊集合；根据所述模糊集合，获得模糊规则，确定当预设输入时的输出模糊集合；根据所述模糊规则和Mamdani型模糊推理算法，对于预设所述特征因 子的隶属度函数，得到路面分类类型，识别出所述路面信息。
- 如权利要求2所述的轮胎路面信息的监测方法，其特征在于，所述通过柔性传感器进行实时采集所述轮胎在滚动过程中的所述柔性传感信号，具体为：将若干柔性压力传感器安装于轮胎内衬层的若干预设位置，将分压设备安装于轮胎内衬层中心部位；通过所述若干柔性压力传感器实时采集所述轮胎在滚动过程中的输出电阻值，将所述若干预设位置对应布置若干通道，通过所述分压设备获取各通道的输出电阻信号，根据所述输出电阻信号，得到所述柔性传感信号。
- 如权利要求3所述的轮胎路面信息的监测方法，其特征在于，所述根据轮胎自由滚动半径和实时采集到所述轮胎的垂向加速度信号，计算车速，具体为：将三轴加速度传感器安装于轮胎内衬层中心部位，集成在所述分压设备的加速度通道，通过所述三轴加速度传感器进行实时采集所述轮胎在滚动过程中的垂向加速度，通过所述分压设备获取所述加速度通道的所述垂向加速度信号；根据所述垂向加速度信号，获得垂向加速度数值；根据所述垂向加速度数值，计算旋转向心加速度；将所述旋转向心加速度和所述轮胎自由滚动半径，进行数值运算，得到所述车速。
- 如权利要求1所述的轮胎路面信息的监测方法，其特征在于，所 述并根据所述垂向加速度信号和所述车速，计算接地时间和接地印痕长度，具体为：根据所述垂向加速度信号，计算信号峰值的间距差，得到所述接地时间；将所述车速和所述接地时间，进行数值运算，得到所述接地印痕长度。
- 如权利要求1所述的轮胎路面信息的监测方法，其特征在于，所述将所述接地印痕长度、所述轮胎自由滚动半径和实时采集到所述轮胎的胎压信息，结合轮胎半经验模型，计算垂向力，具体为：根据实时采集到所述轮胎的胎压信息，计算垂向刚度，将所述接地印痕长度、所述垂向刚度和所述轮胎自由滚动半径，代入所述轮胎半经验模型中，计算所述垂向力；所述轮胎半经验模型，如下式：
其中，a为所述接地印痕长度，Ro为所述轮胎自由滚动半径，Fz为垂向力，Cz为垂向刚度，a1为第一待拟合系数，b1为第二待拟合系数，m为第一待拟合次数，n为第二待拟合次数。 - 一种轮胎路面信息采集传输装置，其特征在于，包括：柔性传感监测模块、加速度监测模块、数据计算模块和信息集成模块；其中，所述柔性传感监测模块用于对实时采集到轮胎的柔性传感信号，进行提取特征因子，并将所述特征因子结合机器学习模糊推理算法，识别出路面信息；所述加速度监测模块用于根据轮胎自由滚动半径和实时采集到所述轮胎的垂向加速度信号，计算车速；所述数据计算模块用于根据所述垂向加速度信号和所述车速，计算接地时间和接地印痕长度，将所述接地印痕长度、所述轮胎自由滚动半径和实时采集到所述轮胎的胎压信息，结合轮胎半经验模型，计算垂向力；所述信息集成模块用于根据所述路面信息和轮胎状态数据，实时生成轮胎路面信息；其中，所述轮胎状态数据包括所述车速、所述接地时间、所述接地印痕长度和所述垂向力。
- 一种轮胎路面信息的监测系统，其特征在于，包括：轮胎路面信息采集传输装置、供电装置、胎压监测装置、无线传输装置和车载终端装置；其中，所述轮胎路面信息采集传输装置执行如权利要求1至6任意一项所述的轮胎路面信息的监测方法；其中，装置的连接如下：所述胎压监测装置与所述轮胎路面信息采集传输装置连接，所述供电装置与所述轮胎路面信息采集传输装置连接，所述无线传输装置与所述轮胎路面信息采集传输装置连接，所述供电装置与所述无线传输装置连接，所述车载终端装置与所述无线传输装置连接。
- 如权利要求8所述的轮胎路面信息的监测系统，其特征在于，所述供电装置用于为轮胎路面信息采集传输装置供电；所述胎压监测装置用于为轮胎路面信息采集传输装置提供轮胎的胎压信息；所述无线传输装置用于实时将轮胎路面信息发送至所述车载终端装置；所述车载终端装置用于接收所述轮胎路面信息，实时显示轮胎在运动过程中的所述轮胎路面信息。
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