A self-generating intelligent tire system and a whole vehicle stability control method based on the system

Abstract

The application discloses a self-power generation intelligent tire system and a whole vehicle stability control method based on the system, a signal acquisition module is used for collecting electric signals of a sensor assembly, and the collected electric signals are transmitted to a filtering module for filtering processing, and the processed signals are transmitted to a whole vehicle ECU through a Bluetooth signal emission module; when the tire rolls, an induction coil cuts a magnetic induction line of a vehicle-mounted magnetic field emission module, current is generated, rectification processing is performed on the current through a power generation module of a signal processor, and the signal processor is provided with electric energy required for working. The intelligent tire can realize self-power generation in the process of vehicle driving, simultaneously provides road surface identification, tire load estimation and accurate measurement of tire mechanical parameters, provides data for whole vehicle stability control, and makes vehicle driving safer.

Description

Technical Field

[0001] This invention relates to a self-generating intelligent tire system and a method for vehicle stability control based on this system. Background Technology

[0002] With the development of technology and the increasing number of cars on the road, people are paying more and more attention to car safety. Tires, as the displacement medium between the car and the ground, are responsible for transmitting all the forces required for driving, braking, and steering, directly affecting the car's braking performance, acceleration, ride comfort, and handling stability. Smart tires are an inevitable trend in tire intelligence and a technology that domestic and international tire companies are vying to develop, including Michelin, Continental, and Goodyear. However, these smart tire systems all require external power supplies, which significantly impacts the power, accuracy, and continuity of signal processing, necessitating frequent power supply replacements and severely hindering the commercialization of smart tire technology.

[0003] This invention designs a self-generating intelligent tire system that also features road surface recognition, tire load estimation, precise measurement of tire mechanical parameters, and a vehicle stability control method. Summary of the Invention

[0004] The present invention provides a self-generating intelligent tire system and a method for vehicle stability control based on the system in order to solve the problems existing in the prior art.

[0005] The technical solutions adopted in this invention are as follows:

[0006] A self-generating intelligent tire system includes a rim and a tire carcass mounted on the rim, and further includes...

[0007] The sensor assembly includes a first acceleration sensor, a second acceleration sensor, and a tread acceleration sensor. The first and second acceleration sensors are respectively disposed on the inner sidewall of the tire body, and the tread acceleration sensor is disposed on the inner side of the tread of the tire body. The first and second acceleration sensors are used to measure tire load, and the tread acceleration sensor is used to measure road surface information.

[0008] The signal processor includes a base and an induction coil. Inside the base are a power generation module, a signal acquisition module, a filtering module and a Bluetooth signal transmission module. The signal acquisition module is used to acquire electrical signals from the sensor components and transmit the acquired electrical signals to the filtering module for filtering. The processed signal is then transmitted to the vehicle ECU via the Bluetooth signal transmission module.

[0009] The vehicle-mounted magnetic field emitting module is fixed on the vehicle frame and placed above the tire. When the tire rolls, the induction coil cuts the magnetic field lines of the vehicle-mounted magnetic field emitting module, generating current. This current is rectified by the power generation module of the signal processor to provide the signal processor with the power required for its operation.

[0010] Furthermore, the first acceleration sensor, the second acceleration sensor, and the tread acceleration sensor are all piezoelectric thin film sensors.

[0011] This invention also discloses a method for vehicle stability control using a self-generating intelligent tire system, including...

[0012] 1) Construct an intelligent tire dynamics model using signals obtained from the first acceleration sensor, the second acceleration sensor, and the tread acceleration sensor;

[0013] 2) Road surface identification and hydroplaning estimation are performed using signals obtained from the tire tread acceleration sensor;

[0014] 3) The amount of deformation caused by the load during tire rolling is obtained by the first and second acceleration sensors, and the tire load is estimated.

[0015] 4) Estimate the tire slip angle and tire slip ratio using the acceleration signals obtained by the first acceleration sensor and the tread acceleration sensor respectively;

[0016] 5) Perform vehicle stability analysis based on the estimated structure in 2)-4), and then adjust the vehicle lateral instability control.

[0017] Furthermore, the road surface identification process is as follows:

[0018] 1) Wavelet transform is used to preprocess the vibration signal acquired by the tire tread acceleration sensor to obtain the vibration time domain signal, detail signal and approximate signal, and statistical features are introduced to extract the key features of the vibration signal;

[0019] 2) Normalize the filtered vibration signal according to:

[0020] ,

[0021] in, The original vibration signal, The normalized vibration signal and These represent the maximum and minimum values ​​of the vibration signal;

[0022] 3) Principal component analysis is used to reduce the dimensionality of the extracted key features.

[0023] 4) Based on the feature parameters, use the trained support vector machine classification model to perform road surface discrimination and classification.

[0024] Furthermore, the process for estimating the hydroplaning state of the road surface is as follows:

[0025] Using signals obtained from a tire tread acceleration sensor, the acceleration in three directions of a wet road surface with different water film thicknesses is obtained. The three directions are the vertical, lateral, and longitudinal directions of the tire.

[0026] By analyzing and considering the changes in longitudinal acceleration, the longitudinal displacements of the leading and trailing edges of the tire tread in contact with the water film were detected. Then, based on the longitudinal acceleration curve, the positions of the leading and trailing edges in contact with the water film (x1), the trailing edge (x2), and the tire separation point (x3) were determined. The tire hydroplaning length is:

[0027] l aq = x2- x 1，

[0028] If l aq =0, then the tire is not hydroplaning;

[0029] If l aq If the value is >0, the tire is in a hydroplaning state;

[0030] If l aq If x3 - x1, then the tire is in a fully hydroplaned state and the contact force with the road surface is 0.

[0031] The contact length between the tire and the road surface is:

[0032] l ar = x3- x2=(x3- x1)-(x2- x1),

[0033] By analyzing l aq or l ar It obtains the contact state between the tires and the road surface, providing hydroplaning information for vehicle stability control.

[0034] Furthermore, the tire load estimation process is as follows:

[0035] The amount of deformation caused by load during tire rolling is obtained using a first accelerometer and a second accelerometer.

[0036] The length and width of the tire contact patch are influenced by the tire's vertical load and tire pressure, and their functional relationship is as follows:

[0037] ,

[0038] Where: a is the longitudinal length of the tire contact patch, R0 is the tire's free rolling radius, and C... z For vertical stiffness, Fz The vertical force is represented by a1 and b1, which are the fitting coefficients, and m and n are the number of times the data is to be fitted.

[0039] The longitudinal length of the tire contact mark is:

[0040] ,

[0041] Where: R f This refers to the tire's inflation radius. For the tire rolling cycle, The time required for the tire to roll over the contact patch.

[0042] Furthermore, the process of estimating the tire slip angle is as follows:

[0043] The relationship between variables was determined using regression analysis methods based on the lateral acceleration signals of the tire obtained by the first acceleration sensor and the tread acceleration sensor respectively.

[0044] ,

[0045] ,

[0046] in, Let a be the tire slip angle. 1y and a 2y Lateral acceleration of the tire, v 1y and v 2y It is to accelerate a 1y and a 2y Integral over time;

[0047] d 12 The distance between the mounting positions of the first acceleration sensor and the tread acceleration sensor on the tire axle;

[0048] t 11 and t 12 These are the start and end times of the sudden signal generated by the first accelerometer;

[0049] t 21 and t 22 These are the start and end times when the tread acceleration sensor generates a breakthrough signal;

[0050] Let p be a constant term function, where p c φ represents tire pressure, and φ represents the steering wheel angle.

[0051] Furthermore, the tire slip ratio estimation process is as follows: the longitudinal acceleration 'a' of the tire is obtained through the first acceleration sensor and the tread acceleration sensor, respectively. 1x and a 2x and the lateral acceleration a of the tire1y and a 2y ,

[0052] Analysis and calculation to obtain the longitudinal acceleration difference function and lateral acceleration difference function ;

[0053] The time t taken for the tire to complete one revolution at time n is obtained using a wheel speed sensor. n Thus, the longitudinal slip ratio is obtained. The expression:

[0054] ,

[0055] In the formula, Let n be the rolling radius of the tire at time n. Let n be the longitudinal acceleration difference function. Let n be the lateral acceleration difference function. Let n be the longitudinal slip ratio. Let be the lateral slip ratio at time n.

[0056] Furthermore, the intelligent tire dynamics model is constructed as follows:

[0057]

[0058] ,

[0059] ,

[0060] ,

[0061] in, The longitudinal force experienced by the intelligent tire. For the longitudinal stiffness of intelligent tires. The coefficient of friction of the road surface. For slip ratio, The critical value of slip ratio for the intelligent tire as it changes from a longitudinal semi-slip linear state to a full-slip nonlinear state;

[0062] F y M represents the lateral force experienced by the intelligent tire. z For the restoring torque, C y For lateral stiffness, β slip The nonlinear limiting sideslip angle from a semi-sideslip linear state to a full sideslip state. The tire slip angle is calculated to account for tire deformation. Linear range tread slip angle Lateral deformation of the tire body.

[0063] Furthermore, the process of adjusting the vehicle's lateral instability control is as follows:

[0064] Based on the signal parameters acquired by the sensor components, the longitudinal force of the tire is determined. Lateral force and vertical force To solve this problem, a smart tire margin model is established and divided into three parts: the linear region, the transition region, and the slip region.

[0065] ,

[0066] ,

[0067] in, and It depends on the tire pressure, wear, tread pattern, and road surface type;

[0068] When the stability margin of the smart tire is at In the linear region, the yaw rate of the entire vehicle is controlled to ensure good vehicle handling performance;

[0069] When the stability margin of the smart tire is at and The stability control objective should be prioritized.

[0070] Based on the different control priorities of vehicle handling and stability under different working conditions, and taking into full account the motor driving force limit and tire adhesion state constraints, a multi-objective control adaptive weight allocation scheme is established.

[0071] From a control perspective, tracking the two state variables, sideslip angle and yaw rate, requires two independent inputs. The yaw moment is coupled from the four independently braking wheel torques and used as the control input. A trade-off is made between the handling and stability control states based on a weighting scheme, resulting in a state function S*. First, adaptive weighting of handling and stability is performed:

[0072] ,

[0073] Where k1, k2, and k3 are all constants less than 1. Weights for tracking the centroid sideslip angle. For yaw rate tracking weights, The car is in an unstable region at that time. The car is in the stable region;

[0074] Four-wheel slip ratio weighting design :

[0075] ,

[0076]

[0077] in, , , , , Undetermined parameters related to road conditions, tire type, and vertical load. For composite slip ratio, It represents the lateral slip ratio;

[0078] When the slip ratio of a certain tire is less than 0.05, the control weight of this wheel is small, and no slip ratio control is performed;

[0079] When the slip ratio is higher than 0.05, the control weight of this wheel increases rapidly, and effective control of the slip ratio of this wheel is required.

[0080] The present invention has the following beneficial effects:

[0081] The smart tire of this invention can generate its own electricity during vehicle operation, while providing functions such as road surface recognition, tire load estimation, and precise measurement of tire mechanical parameters, providing data for vehicle stability control and making vehicle driving safer. Attached Figure Description

[0082] Figure 1 This is a schematic diagram of a smart tire.

[0083] Figure 2 This is a schematic diagram of the power generation principle of a smart tire.

[0084] Figure 3 Intelligent Tire Mechanical Signal Acquisition and Processing Flowchart

[0085] Figure 4 and Figure 5 The figures show the distribution of the induced voltage and the change in magnetic flux of the induction coil 42, respectively.

[0086] Figure 6 Graph showing the variation of longitudinal acceleration on a hydroponic surface.

[0087] Figure 7 Diagram of tire ground contact imprint area and piezoelectric film voltage signal.

[0088] Figure 8 Framework for dynamic model construction and stability analysis.

[0089] Figure 9 Instability control and four-wheel torque distribution. Detailed Implementation

[0090] The following is in conjunction with the appendix Figures 1 to 9 The present invention will be further described below.

[0091] like Figure 1 As shown, the present invention provides a self-generating intelligent tire system, including a rim 1, a tire body 2, a sensor assembly 3, a signal processor 4, and an on-board magnetic field emitting module 5.

[0092] The rim 1 is the support unit of the smart tire, bearing various loads during tire operation; the tire body 2 is the rubber elastic component of the smart tire, serving as the connection between the road surface and the rim 1 during tire rolling.

[0093] The sensor assembly includes a first acceleration sensor 31, a second acceleration sensor 32, and a tread acceleration sensor 33, all of which are piezoelectric thin-film sensors. The first acceleration sensor 31 and the second acceleration sensor 32 are respectively located on the inner side of the tire wall of the tire body 2, and the tread acceleration sensor 33 is located on the inner side of the tread of the tire body 2.

[0094] The first and second acceleration sensors are used to measure tire load, and the tread acceleration sensor is used to measure road surface information.

[0095] The signal processor 4 includes a base 41 and an induction coil 42. Inside the base 41 are a power generation module, a signal acquisition module, a filtering module and a Bluetooth signal transmission module. The signal acquisition module is used to acquire the electrical signals of the sensor components and transmit the acquired electrical signals to the filtering module for filtering. The processed signal is then transmitted to the vehicle ECU via the Bluetooth signal transmission module.

[0096] like Figure 2 and Figure 3 The vehicle-mounted magnetic field emitting module 5 is fixed on the vehicle frame and positioned above the tire body 2. When the tire rolls, the induction coil 42 cuts the magnetic field lines of the vehicle-mounted magnetic field emitting module 5, generating a current. This current is rectified by the power generation module of the signal processor 4, providing the signal processor 4 with the electrical energy required for its operation. Figure 4 and Figure 5 The figures show the distribution of the induced voltage and the change in magnetic flux of the induction coil 42, respectively.

[0097] The method for vehicle stability control based on a self-generating intelligent tire system is as follows:

[0098] 1) Construct an intelligent tire dynamics model using signals obtained from the first acceleration sensor 31, the second acceleration sensor 32, and the tread acceleration sensor 33;

[0099] 2) Road surface identification and hydroplaning estimation are performed using signals obtained from the tire tread acceleration sensor 33;

[0100] 3) The amount of deformation caused by the load during tire rolling is obtained by the first acceleration sensor 31 and the second acceleration sensor 32, and the tire load is estimated.

[0101] 4) The tire slip angle and tire slip ratio are estimated by using the acceleration signals obtained by the first acceleration sensor 31 and the tread acceleration sensor 33 respectively.

[0102] 5) Perform vehicle stability analysis based on the estimated structure in 2)-4), and then adjust the vehicle lateral instability control.

[0103] The method of the present invention will be described in detail below.

[0104] The process of road surface identification is as follows:

[0105] 1) The vibration signal acquired by the tire tread acceleration sensor 33 is preprocessed using wavelet transform to obtain the vibration time-domain signal, detail signal, and approximate signal. Statistical features are then introduced to extract key features of the vibration signal. These key features include maximum value, minimum value, mean, variance, peak value, root mean square, waveform factor, peak factor, impulse factor, skewness, and margin factor.

[0106] 2) Normalize the filtered vibration signal according to:

[0107] ,

[0108] in, The original vibration signal, The normalized vibration signal and These represent the maximum and minimum values ​​of the vibration signal.

[0109] 3) Dimensionality reduction of the feature parameters of the extracted key features is achieved through principal component analysis.

[0110] 4) Based on the feature parameters, use the trained support vector machine classification model to perform road surface discrimination and classification.

[0111] The process for estimating the hydroplaning state of a road surface is as follows:

[0112] Among various road surface types, low-traction surfaces cause a sharp decrease in tire grip, increasing vehicle braking distance and seriously affecting driving safety. Wet and slippery surfaces are the most common type of low-traction surface, thus possessing significant research value.

[0113] First, using the signal acquired by the tread acceleration sensor 33, the acceleration in three directions (vertical, lateral, and longitudinal) of the wet road surface with different water film thicknesses is obtained. Since the presence of the water film significantly affects the longitudinal acceleration of the tread, by analyzing and considering the changes in longitudinal acceleration, the longitudinal displacement of the leading and trailing edges of the tread in contact with the water film can be detected. Figure 6 As shown.

[0114] Then, based on the longitudinal acceleration curve, determine the leading edge position x1, the trailing edge position x2, and the tread separation position x3 from the road surface. The tire hydroplaning length is:

[0115] l aq = x2- x 1，

[0116] If l aq =0, then the tire is not hydroplaning;

[0117] If l aq If the value is >0, the tire is in a hydroplaning state;

[0118] If l aq If x3 - x1, then the tire is in a fully hydroplaned state and the contact force with the road surface is 0.

[0119] The contact length between the tire and the road surface is:

[0120] l ar = x3- x2=(x3- x1)-(x2- x1),

[0121] By analyzing l aq or l ar It obtains the contact state between the tires and the road surface, providing hydroplaning information for vehicle stability control.

[0122] The process of tire load estimation is as follows:

[0123] The deformation caused by the load during tire rolling is obtained using the first acceleration sensor 31 and the second acceleration sensor 32; the functional relationship between the length and width of the tire's contact patch length and width, which are influenced by the tire's vertical load and tire pressure, is as follows:

[0124] ,

[0125] Where: a is the longitudinal length of the tire contact patch, R0 is the tire's free rolling radius, and C... z For vertical stiffness, F z denoted as vertical force, a1 and b1 are fitting coefficients, and m and n are the number of iterations to be fitted.

[0126] Estimating the length of the ground contact mark on a rolling tire is a crucial issue. When the tire contacts the ground, it causes the piezoelectric film attached to the inside of the tire to bend and deform, resulting in a significant voltage signal abrupt change. Analyzing the piezoelectric film sensor signal in the ground contact mark area, such as... Figure 7 As shown, Let OA be the angle between the two endpoints of the longitudinal length of the tire contact patch and the center of the rim. If the influence of the compression zone on "OA" is ignored, the length of "OA" can be approximated as equal to the tire's inflation radius R. f When the inner tread of the tire is compressed into a plane and "OB" is perpendicular to the ground, the longitudinal length 'a' of the tire's contact patch can be expressed as:

[0127] ,

[0128] The ground contact angle cannot be monitored in real time during actual tire movement. It needs to be based on the tire rolling cycle. The time required to roll over the ground imprint is calculated by the horizontal interval between the two peaks of the second derivative curve of the piezoelectric signal. The length of the grounding imprint is obtained. The longitudinal length of the grounding imprint can then be further expressed as:

[0129] ,

[0130] Where: R f This refers to the tire's inflation radius. For the tire rolling cycle, The time required for the tire to roll over the contact patch.

[0131] The process of estimating tire slip angle is as follows:

[0132] The lateral acceleration signals of the tire are acquired by the first acceleration sensor 31 and the tread acceleration sensor 33 respectively. Taking into account tire pressure, vertical force, steering wheel angle and speed, the relationship between the variables is determined by regression analysis.

[0133] ,

[0134] ,

[0135] in, Let a be the tire slip angle. 1y and a 2y Lateral acceleration of the tire, v 1y and v 2y It is to accelerate a 1y and a 2y Integral over time;

[0136] d 12The distance between the mounting positions of the first acceleration sensor (31) and the tread acceleration sensor (33) on the tire axle;

[0137] t 11 and t 12 These are the start and end times of the sudden change signal generated by the first accelerometer (31);

[0138] t 21 and t 22 These are the start and end times of the breakthrough signal generated by the tread acceleration sensor (33);

[0139] Let p be a constant term function, where p c Let φ be the tire pressure and φ be the steering wheel angle. Considering that the model formula for estimating the tire slip angle is a nonlinear function, a nonlinear least squares method is used to fit the data.

[0140] The process of estimating tire slip ratio is as follows:

[0141] The tire slip ratio is essentially caused by the relative elastic misalignment between the tread rubber and the tire body in the circumferential direction. The longitudinal acceleration 'a' of the tire is obtained by the first acceleration sensor 31 and the tread acceleration sensor 33, respectively. 1x and a 2x and the lateral acceleration a of the tire 1y and a 2y Analyze and calculate to obtain the longitudinal acceleration difference function and lateral acceleration difference function .

[0142] The time t taken for the tire to complete one revolution at time n is obtained using a wheel speed sensor. n Thus, the longitudinal slip ratio is obtained. The expression:

[0143] ,

[0144] In the formula, Let n be the rolling radius of the tire at time n. Let n be the longitudinal acceleration difference function. Let n be the lateral acceleration difference function. Let n be the longitudinal slip ratio. Let be the lateral slip ratio at time n.

[0145] The intelligent tire dynamics model is constructed as follows:

[0146] Based on the signal parameters acquired by sensor component 3, the force of the intelligent tire is solved using the brush model theory. When the tire rolls on the road surface, the frictional force exerted on the tire by the road surface mainly consists of two parts: static friction in the adhesion zone and dynamic friction in the sliding zone. During the contact process, the tire tread is compressed and bears vertical force. The generated force and torque are calculated by integrating the stress of all brush bristles in the contact area, thus constructing a dynamic model of the intelligent tire.

[0147]

[0148] ,

[0149] ,

[0150] ,

[0151] in, The longitudinal force experienced by the intelligent tire. For the longitudinal stiffness of intelligent tires. The coefficient of friction of the road surface. For slip ratio, The critical value of slip ratio for the intelligent tire as it changes from a longitudinal semi-slip linear state to a full-slip nonlinear state;

[0152] F y M represents the lateral force experienced by the intelligent tire. z For the restoring torque, C y For lateral stiffness, β slip The nonlinear limiting sideslip angle from a semi-sideslip linear state to a full sideslip state. The tire slip angle is calculated to account for tire deformation. Linear range tread slip angle Lateral deformation of the tire body.

[0153] Vehicle stability analysis based on multidimensional dynamic information of intelligent tires

[0154] A machine learning method based on neural networks is used to estimate the vehicle's motion state. Neural networks themselves possess nonlinearity, self-organization, and self-learning capabilities, making them suitable for solving nonlinear system control problems. This method can achieve vehicle state estimation by simulating the intelligent activities of the human nervous system.

[0155] ,

[0156] In the formula, The signal parameters acquired by sensor component 3 Here, w represents the parameters that need to be output by sensor component 3, M represents the number of nodes in the output layer, H represents the number of nodes in the hidden layer, N represents the number of nodes in the input layer, and b represents the threshold within the neuron.

[0157] By analyzing the multidimensional dynamic information of smart tires and the basic dynamic characteristics of the vehicle itself, a neural network architecture suitable for vehicle motion state estimation is designed to achieve deep learning estimation of the vehicle's longitudinal velocity, center of gravity sideslip angle, and yaw rate. The model is trained and optimized on a dataset consisting of various complex working conditions. Specific analysis steps are as follows: Figure 8 As shown.

[0158] Determining the driving state of a vehicle under different operating conditions is a key issue. This paper analyzes vehicle stability by establishing a phase plane method based on the center of gravity sideslip angle and the center of gravity sideslip angular velocity. It combines the double straight line method and the limit cycle method to determine the stable region and boundary. Considering the different variation patterns of the phase trajectory of the stable state under complex driving environments, the paper constructs a dynamic stability boundary, taking into account the effects of longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient.

[0159] The dynamic stability domain is divided into the absolute stability domain, the transition domain, and the instability domain.

[0160] The absolutely stable region is located inside the elliptical boundary, and the transition region is located in the region between the elliptical boundary and the double straight lines; the unstable region is located in the region outside the double straight lines. The transition region and the unstable region are defined as unstable regions.

[0161] The process of adjusting the vehicle's lateral instability control is as follows:

[0162] Based on the signal parameters acquired by the sensor components, the longitudinal force of the tire is determined. Lateral force and vertical force To solve this problem, a smart tire margin model is established and divided into three parts: the linear region, the transition region, and the slip region.

[0163] ,

[0164] ,

[0165] in, and Depending on tire pressure, wear, tread pattern, and road surface type, this information can be obtained using intelligent tire multidimensional dynamic information and machine learning methods.

[0166] When the stability margin of the smart tire is at In the linear region, the yaw rate of the entire vehicle is controlled to ensure good vehicle handling performance;

[0167] When the stability margin of the smart tire is at and Therefore, stability control objectives should be prioritized.

[0168] Based on the different control priorities of vehicle handling and stability under different operating conditions, and taking into full account the motor driving force limit and tire adhesion state constraints, a multi-objective control adaptive weight allocation scheme is established.

[0169] From a control perspective, tracking the two state variables, sideslip angle and yaw rate, requires two independent inputs. The yaw moment is coupled from the four independently braking wheel torques and used as the control input. A trade-off is made between the handling and stability control states based on a weighting scheme, resulting in a state function S*. First, adaptive weighting of handling and stability is performed:

[0170] ,

[0171] Where k1, k2, and k3 are all constants less than 1. Weights for tracking the centroid sideslip angle. For yaw rate tracking weights, The car is in an unstable region at that time. The car is in the stable region;

[0172] Four-wheel slip ratio weighting design :

[0173] ,

[0174] ,

[0175] in, , , , , Undetermined parameters related to road conditions, tire type, and vertical load. For composite slip ratio, It represents the lateral slip ratio;

[0176] When the slip ratio of a certain tire is less than 0.05, the control weight of this wheel is small, and no slip ratio control is performed;

[0177] When the slip ratio is higher than 0.05, the control weight of this wheel increases rapidly, requiring effective control of the slip ratio. Specific steps are as follows: Figure 9 As shown.

[0178] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A method for vehicle stability control using a self-generating intelligent tire system, characterized in that: The method configures a self-generating intelligent tire system, which includes a rim (1), a tire carcass (2) mounted on the rim, a sensor assembly, a signal processor (4), and an on-board magnetic field emitting module (5). The sensor assembly includes a first acceleration sensor (31), a second acceleration sensor (32), and a tread acceleration sensor (33). The first acceleration sensor (31) and the second acceleration sensor (32) are respectively located on the inner side of the tire wall of the tire body (2), and the tread acceleration sensor (33) is located on the inner side of the tread of the tire body (2). The signal processor (4) includes a base (41) and an induction coil (42). Inside the base (41) are a power generation module, a signal acquisition module, a filtering module and a Bluetooth signal transmission module. The signal acquisition module is used to acquire the electrical signals of the sensor components and transmit the acquired electrical signals to the filtering module for filtering. The processed signal is transmitted to the vehicle ECU through the Bluetooth signal transmission module. The vehicle-mounted magnetic field emitting module (5) is fixed on the vehicle frame and placed above the tire body (2). When the tire rolls, the induction coil (42) cuts the magnetic field lines of the vehicle-mounted magnetic field emitting module (5) to generate current. The current is rectified by the power generation module of the signal processor (4) to provide the signal processor (4) with the power required for operation. The first acceleration sensor (31), the second acceleration sensor (32), and the tread acceleration sensor (33) are all piezoelectric thin film sensors; The method includes the following steps: 1) Construct an intelligent tire dynamics model using signals obtained from the first acceleration sensor (31), the second acceleration sensor (32), and the tread acceleration sensor (33); 2) Road surface identification and hydroplaning estimation are performed using signals obtained from the tire tread acceleration sensor (33); 3) Obtain the amount of deformation caused by the load during tire rolling by the first acceleration sensor (31) and the second acceleration sensor (32) to estimate the tire load; 4) The tire slip angle and tire slip ratio are estimated by using the acceleration signals obtained by the first acceleration sensor (31) and the tread acceleration sensor (33) respectively. 5) Based on the estimated structure in 2)-4), perform vehicle stability analysis, and then adjust the vehicle lateral instability control. The process of road surface identification is as follows: 1) The vibration signal obtained by the tread acceleration sensor (33) is preprocessed by wavelet transform to obtain the vibration time domain signal, detail signal and approximate signal, and statistical features are introduced to extract the key features of the vibration signal; 2) Normalize the filtered vibration signal according to: ， in, The original vibration signal, The normalized vibration signal and These represent the maximum and minimum values ​​of the vibration signal; 3) Principal component analysis is used to reduce the dimensionality of the extracted key features. 4) Based on the feature parameters, use the trained support vector machine classification model to perform road surface discrimination and classification.

2. The method for vehicle stability control using the self-generating intelligent tire system as described in claim 1, characterized in that: The process for estimating the hydroplaning state of a road surface is as follows: Using the signal obtained by the tread acceleration sensor (33), the acceleration in three directions of the wet road surface with different water film thicknesses is obtained, namely the vertical, lateral and longitudinal directions of the tire; By analyzing and considering the changes in longitudinal acceleration, the longitudinal displacements of the leading and trailing edges of the tire tread in contact with the water film were detected. Then, based on the longitudinal acceleration curve, the positions of the leading and trailing edges in contact with the water film (x1), the trailing edge (x2), and the tire separation point (x3) were determined. The tire hydroplaning length is: l aq = x2- x 1， If l aq =0, then the tire is not hydroplaning; If l aq If the value is >0, the tire is in a hydroplaning state; If l aq If x3 - x1, then the tire is in a fully hydroplaned state and the contact force with the road surface is 0. The contact length between the tire and the road surface is: l ar = x3- x2=( x3- x1)-( x2- x1), By analyzing l aq or l ar It obtains the contact state between the tires and the road surface, providing hydroplaning information for vehicle stability control.

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