A vehicle shortest braking distance control method based on intelligent tires, application and program product

Abstract

The present invention relates to vehicle control methods, and more particularly to a method, application, and program product for controlling the shortest braking distance of a vehicle based on intelligent tires. This method primarily relies on an embedded sensor intelligent system to detect real-time load, tire pressure and speed changes, wear, and road conditions during tire contact. The method then enables the vehicle control system to make appropriate adjustments based on these measurements, specifically shortening braking distance during braking. Therefore, by accurately predicting the friction between each tire and the road surface and the road surface conditions (friction coefficient or slip ratio) during braking, the different braking forces of each tire are rationally coordinated and distributed. This method overcomes the problem of relying solely on optimized tread structure design and improved tread materials to shorten vehicle braking distance. By using new thinking and new methods to break through the bottlenecks of traditional technologies, this method establishes the core key technologies of vehicle optimization control methods based on intelligent tires, innovates new theories and methods for vehicle braking, and improves driving safety.

Description

Technical Field

[0001] The present invention relates to a vehicle control method, and in particular to a vehicle shortest braking distance control method based on intelligent tires, an application and a program product. Background Art

[0002] my country's domestic automobile production is currently stabilizing, while the production and sales of new energy vehicles are steadily increasing, leaving significant room for growth in vehicle ownership. In 2021, global automobile production reached 80.16 million units, a year-on-year increase of 3.26%. China's automobile production reached 26.06 million units, a year-on-year increase of 3.52%. Regarding vehicle ownership, China's vehicle ownership continues to rise, reaching 302 million in 2021. my country is a major tire producer, and tire sales are closely correlated with both vehicle production and vehicle ownership. With the development of the tire industry, the research and development of smart tires, a new product transforming the traditional tire industry, is one of the latest developments in tire technology. Smart tires are a product of the integration of traditional industries with emerging electronics and information technologies. Their real-time sensing of road conditions can significantly improve vehicle safety. However, because smart tires involve the integration of advanced technologies from multiple fields and disciplines, mature products are currently unavailable. Therefore, vigorously developing smart tires can effectively enhance my country's core position in the automotive sector, strengthen technological innovation among Chinese auto companies, and promote the efficient and rapid development of my country's tire industry.

[0003] Smart tires are intelligent tires. "Intelligent" means that future tires will no longer be simple, passive rubber composites, but will become a crucial component of vehicle control systems. They can automatically acquire and transmit information about themselves and their environment, make accurate judgments and decisions based on this information, and then execute corresponding actions based on the results, thereby improving vehicle safety, economy, and comfort. Tire information includes tire pressure, temperature, friction with the road, vibration, wear, aging, and other status information, as well as the tire's identity. Environmental information includes road conditions and vehicle speed.

[0004] However, the development of smart tire technology has been relatively slow in recent years. This is due to the complexity of the technology involved, including tire materials, structures, and manufacturing processes, high-precision sensors, microprocessors, digital signal processing, artificial intelligence, energy management, antennas, and wireless communications. These challenges pose significant challenges to the design, manufacturing, and implementation of smart tires. However, smart tires, which integrate advanced key technologies such as sensing, signal conditioning, real-time communication, and algorithms for predicting dynamic characteristics like tire contact deformation, hold enormous potential for future applications. Advanced sensors and algorithms are essential for sensing and determining the real-time status of tires and roads, eliminating reliance on traditional manual experience. Therefore, intelligent perception of tire safety status, as well as road and vehicle safety status, is a crucial technical challenge that must be overcome not only today but also in the future as autonomous vehicles become commercially viable.

[0005] Tires are the part of the car that comes into direct contact with the road, primarily responsible for transmitting the vehicle's driving, braking, and steering forces. Because it's impossible to accurately measure the actual ground contact friction and road friction conditions of each tire, traditional control forces (driving, braking, and steering) output to each tire are pre-allocated according to a specific ratio. This makes it difficult to achieve optimal control of braking forces that vary randomly with load and road conditions. Improving tire handling stability and braking performance typically relies solely on optimized tire tread design and advancements in tread materials, but this approach is limited, and achieving significant performance improvements is quite difficult.

[0006] Recent international research indicates that accurately measuring the contact conditions between tires and the road—for example, the friction coefficient, slip ratio, and frictional force—can reduce braking distances by over 30%. Therefore, to improve driving safety, developing intelligent tires that can provide real-time tire characteristics such as load and wear during driving is crucial.

[0007] The core of smart tire technology lies in converting the physical quantities measured by smart tire sensors into tire dynamics information truly needed by the chassis control system of new energy vehicles and establishing a road surface perception system. In order to address the problems of inaccurate vehicle perception of road surface information and slow response speed, we study multi-parameter perception methods such as temperature, pressure, and strain suitable for vehicle tires, as well as design technologies for high-precision, high-sensitivity multi-parameter sensor arrays. Through array sensor design technology (the applicant has applied for a Chinese invention patent, application number: 202211270200X), the signal acquisition sample and sensitivity are greatly improved, and accurate prediction of load and road type status is achieved, which becomes the core research technology. Summary of the Invention

[0008] In order to solve the above-mentioned technical problems, the purpose of the present invention is to provide a method for controlling the shortest braking distance of a vehicle based on an intelligent tire. The method is mainly based on the load, tire pressure and speed changes, wear degree, and road conditions detected in real time by the embedded sensor intelligent system when the tire is grounded during driving, so that the vehicle control system makes appropriate adjustments based on these measurement data, especially shortening the braking distance during braking. Therefore, by accurately predicting the friction between each tire and the road surface and the road surface conditions (friction coefficient or slip rate) during braking, the different braking forces of each tire are reasonably coordinated and distributed, solving the problem of shortening the vehicle braking distance by simply relying on the optimized design of the pattern structure and the advancement of the crown material. New thinking and new methods are used to break through the bottleneck of traditional technology, establish the core key technology of the vehicle optimization control method based on intelligent tires, innovate new theories and methods of vehicle braking, and improve driving safety performance.

[0009] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0010] A method for controlling the shortest braking distance of a vehicle based on an intelligent tire. The intelligent tire comprises at least one or more strain gauge MEMS sensors mounted on the tire's inner side, along both the transverse and circumferential directions of the tire cross section; at least one or more temperature, tire pressure, and acceleration MEMS sensor assemblies mounted on the tire's inner side, along both the transverse and circumferential directions of the tire cross section; and a simple information collection and analysis processing device mounted within the tire. The simple information collection and analysis processing device collects data from the strain gauge MEMS sensor and the temperature, tire pressure, and acceleration MEMS sensor assemblies and feeds this data back to the vehicle control system. The method comprises the following steps:

[0011] 1) Calculate the vehicle's forward speed V0 based on the acceleration MEMS sensor measurement data;

[0012] 2) Calculate the tire angular velocity ω and speed V1 based on the periodic changes in the contact waveform of each tire;

[0013] 3) The slip rate S of each tire during braking is calculated according to the following formula:

[0014] S=(V0-V1) / V0

[0015] Substitute the slip ratio S obtained from the above formula into the friction coefficient-slip ratio relationship database to obtain the corresponding dynamic friction coefficient

[0016] f i (i=1,2……n);

[0017] 4) The onboard intelligent tire control system algorithm calculates the load N of each tire i (i=1,2……n);

[0018] 5) Further calculate the ground friction force F of each tire i =N i *f i (i=1,2……n);

[0019] 6) Calculate the result F i Feedback to vehicle control system;

[0020] 7) The vehicle control system predicts the friction force F i The size of the brake is allocated to the most appropriate braking force.

[0021] Preferably, the method further includes calculating the braking distance L based on the mechanical principle of vehicle motion energy balance. The specific process steps are as follows:

[0022] 1) Algorithm predicts vehicle speed V0 and friction coefficient f i ;

[0023] 2) From f i Select the maximum value of the friction coefficient f max ;

[0024] 3) According to the principle of kinematic energy balance, the braking distance L = (V0) 2 / (2g×f max );

[0025] 4) The predicted braking distance L is uploaded to the terminal display system.

[0026] Preferably, n=4-6.

[0027] Preferably, the method selects measured data under different road surface types, different road surface conditions, and different speed conditions to update the friction coefficient-slip rate relationship database.

[0028] Preferably, the specific process steps of step 4) the vehicle-mounted intelligent tire control system predicting the load borne by each tire are as follows:

[0029] 4.1) Real-time acquisition of tire acceleration or strain gauge sensor waveforms during tire rotation;

[0030] 4.2) Extracting waveform features between the tire contact area and non-contact area;

[0031] 4.3) Constructing the optimal feature subset through filtering and transformation techniques;

[0032] 4.4) Establish the relationship between tire load and ground contact waveform characteristics under standard operating conditions;

[0033] 4.5) Establishing an intelligent tire force prediction fusion model through parameter iteration;

[0034] 4.6) Use the improved neural network algorithm based on the fusion model to predict the load borne by each tire.

[0035] Preferably, the method also includes an on-board intelligent tire control system that collects information including vehicle speed, tire speed of each tire, contact mechanical properties between tire and road surface, and road surface type and condition in real time. After analysis by artificial intelligence and machine learning algorithms, it accurately predicts parameter information including the load and wear degree borne by each tire, friction coefficient, and slip rate, and feeds it back to the vehicle control system. After calculation and analysis, the control system optimally distributes braking force to each tire to achieve the shortest braking distance.

[0036] As a preference, the algorithm analysis of artificial intelligence and machine learning includes effective methods such as fully connected neural networks, improved neural networks, convolutional neural networks, support vector machines, random forests, fault trees, logistic regression and linear regression, as well as the cross-combination of various methods into new artificial intelligence algorithms and computing programs.

[0037] Furthermore, the present invention also discloses a vehicle control system, which uses the method to control the shortest braking distance of the vehicle.

[0038] Furthermore, the present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method.

[0039] Furthermore, the present invention also discloses a computer-readable storage medium having a computer program or instruction stored thereon, which implements the method when the computer program or instruction is executed by a processor.

[0040] Furthermore, the present invention also discloses a computer program product, comprising a computer program or instructions, which implement the method when executed by a processor.

[0041] The present invention utilizes the aforementioned technical solution, using microscopic MEMS sensors placed within the tire to collect real-time information such as vehicle speed, each tire's speed, tire-to-road contact mechanical properties, and road surface conditions. Through artificial intelligence and machine learning algorithm analysis, the system accurately predicts the load and wear level, friction coefficient, slip rate, and other parameters borne by each tire, and feeds this information back to the vehicle control system. The control system then calculates and analyzes this information to optimally distribute braking force to each tire, achieving the shortest braking distance (braking distance) and achieving the highest level of safety. The present invention is applicable to all types of tires, including all-steel radial tires, semi-steel radial tires, bias tires, and OTR tires, and includes devices with built-in sensors (acceleration, temperature, pressure, stress / strain, friction coefficient, slip rate, etc.), intelligent sensors, and other devices with signal acquisition and processing capabilities. It also incorporates intelligent tire integrated design and manufacturing technology with real-time online detection and algorithm analysis devices, vehicle chassis optimization design technology that takes intelligent tire feedback information into account, and a signal data detection, transmission, and feedback system that integrates and shares information between the intelligent tire and vehicle chassis control systems.

[0042] The present invention is particularly effective for passenger cars traveling at high speeds and heavy-duty freight vehicles on long downhill roads. Since the friction force and friction coefficient between the tire and the road surface will change dramatically during braking, the best control effect can be achieved only if the control force can be adjusted at any time as the tread wear changes. Therefore, compared with the existing technology, the tire applied to the vehicle of the present invention is no longer a single, passive rubber composite on the vehicle, but also an important component of the vehicle control system. It can automatically obtain and transmit information about itself and its environment through its own intelligent sensing system, and can make correct judgments and decisions on this information, and then feed back the decision results to the operation of the vehicle control system, thereby improving the safety, economy and comfort of car driving. Therefore, smart tire technology is one of the key technologies and latest development directions of current and future automobile autonomous driving technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Control flow chart for optimally distributing the braking force to each tire;

[0044] Figure 2 Smart tires are embedded with various sensors and simple CPU device system distribution;

[0045] Figure 3 Calculate the tire speed ω (rad / s) based on the periodic changes in the tire contact waveform;

[0046] Figure 4 Flowchart of the braking distance calculation prediction process;

[0047] Figure 5 Waveform characteristics of the contact area and non-contact area when the tire touches the ground;

[0048] Figure 6 The braking distance is shortened when the tire braking force is optimally distributed;

[0049] Figure 7 Comparison of tire braking force predictions from sensor algorithms and finite element simulation results. DETAILED DESCRIPTION

[0050] The Chinese invention patent application filed by the applicant (application number: 202211270200X) discloses a high-sampling rate processing method for accurately predicting load and road conditions based on the periodic changes in the tire deformation (strain) waveform characteristics and the non-ground contact areas of the tire, and a method for optimal configuration of a multi-parameter embedded intelligent MEMS sensor array along the transverse and circumferential directions of the tire section (the number of circumferential sensors configured is calculated based on the length of the ground contact area).

[0051] A Chinese invention patent application (Application No.: 202211270200X) describes a tire contact state multi-parameter high-sampling rate contact information acquisition method, which includes the following steps:

[0052] 1) For the selected tire under the specified load, obtain the tire contact length C through indoor bench testing or finite element contact simulation;

[0053] 2) Calculate the number N of contact areas that appear when the tire rotates one circle;

[0054] 3) Based on the number N of contact areas under the specified tire load, calculate the circumferential MEMS sensor array spacing M, where M = L / N, where L is the tire circumference.

[0055] 4) Place a MEMS sensor at every interval M along the inner circumference of the tire, and N MEMS sensors are required around the tire.

[0056] 5) When predicting handling forces, N MEMS sensors are placed in a row along the circumferential direction on the left and right tire shoulders of the cross section;

[0057] 6) When predicting the braking state, N MEMS sensors are arranged in a row along the circumferential direction at the center of the cross section and on the left and right tire shoulders;

[0058] 7) Each MEMS sensor constitutes its own independent system and uploads the monitoring information to the host computer.

[0059] The above-mentioned MEMS sensors include one or more of acceleration, temperature, pressure, stress / strain, noise and sound field sensors.

[0060] The present invention is based on the above-mentioned new method for controlling the shortest braking distance of a vehicle based on intelligent tires. The microscopic system MEMS sensor placed in the tire collects information such as the contact between the tire and the road and the road condition in real time. After analysis by algorithms such as artificial intelligence and machine learning, the load and wear degree, friction energy, friction coefficient and slip rate of each tire are accurately predicted, and the parameter information is fed back to the vehicle control system. After calculation and analysis, the control system optimally allocates braking force to each tire to achieve the shortest braking distance (braking distance). The new braking method for achieving the highest safety includes the following system processes (such as Figure 1 shown):

[0061] (1) The strain gauge sensor is installed at least once on the inner side of the tire along the transverse direction and circumferential direction of the tire section (embedded intelligent sensor, such as Figure 2 shown);

[0062] (2) After the temperature, tire pressure and acceleration sensors are integrated, at least one or more sensors (embedded intelligent sensors, such as Figure 2 shown);

[0063] (3) A simple information collection and analysis processing device (including power supply, simple CPU calculation and storage, data wireless transmission, etc.) is installed in the tire;

[0064] (4) Calculate the vehicle's forward speed V0 (m / s) based on the acceleration sensor data.

[0065] (5) Based on the periodic variation of the ground contact waveform of each tire (such as Figure 3 As shown), calculate the tire angular velocity ω (rad / s, radians per second) and speed V1 (m / s);

[0066] (6) The slip rate S of each tire during braking is calculated according to the following formula:

[0067] S=(V0-V1) / V0

[0068] (7) The slip rate S obtained from the above formula is substituted into the friction coefficient-slip rate relationship database (shown in Table 1) to obtain the corresponding dynamic friction coefficient f i (i=1,2,,,n, usually n=4 for passenger cars);

[0069] (8) The vehicle-mounted intelligent tire control system algorithm calculates the load N of each tire i (i=1,2,,,n, usually n=4 for passenger cars); Figure 5 The specific process steps are as follows:

[0070] 8.1) Real-time acquisition of tire acceleration or strain gauge sensor waveforms during tire rotation;

[0071] 8.2) Extracting waveform features between the tire contact area and the non-contact area;

[0072] 8.3) Constructing the optimal feature subset through filtering and transformation techniques;

[0073] 8.4) Establish the relationship between tire load and ground contact waveform characteristics under standard operating conditions;

[0074] 8.5) Establish an intelligent tire stress (wear) prediction fusion model through parameter iteration;

[0075] 8.6) Using an improved neural network algorithm based on the fusion model to predict the load on each tire;

[0076] (9) Further calculate the ground friction force F of each tire i =N i *f i (i=1,2,,,n, usually n=4 for passenger cars);

[0077] (10) Calculation result F i Feedback to vehicle control system (CAN bus, etc.);

[0078] (11) The vehicle control system is based on the predicted friction force F i The size of the brake is allocated to the most appropriate braking force.

[0079] Under driving conditions, the present invention uses the intelligent sensors embedded in the tires to collect the contact information between the tires and the road in real time, and uses artificial intelligence algorithms to analyze and process the information to predict the load, slip rate and friction coefficient of each tire. Finally, the braking distance L (m) is calculated based on the mechanical principle of vehicle motion energy balance. The specific process steps are as follows (see Figure 1). Figure 4 shown):

[0080] (1) Algorithm predicts vehicle speed V0 and friction coefficient f i ;

[0081] (2) From f i Select the maximum value of the friction coefficient f max ;

[0082] (3) According to the kinematic energy balance principle, the braking distance L = (V0) 2 / (2g×f max );

[0083] (4) The predicted braking distance L is uploaded to the terminal display system.

[0084] The friction coefficient between the tire and the road is f iThe prediction method is based on a database of the friction coefficient-slip ratio relationship when the tire rotates. In order to improve the prediction accuracy of the friction coefficient, measured data under different road types, different road conditions, and different speeds are selected to update the database.

[0085] Table 1 Friction coefficient-slip ratio relationship database (partial representation)

[0086]

[0087] Furthermore, the present invention's onboard intelligent tire control system collects real-time information, including vehicle speed, each tire's speed, tire-road contact mechanical properties, and road surface conditions. Using artificial intelligence and machine learning algorithms, it accurately predicts parameters such as each tire's load and wear, friction coefficient, and slip ratio, and feeds this information back to the vehicle control system. The control system then calculates and analyzes this information to optimally distribute braking force to each tire, achieving the shortest braking distance. The artificial intelligence and machine learning algorithm analysis includes effective methods such as fully connected neural networks, improved neural networks, convolutional neural networks, support vector machines, random forests, fault tree analysis, logistic regression, and linear regression, as well as cross-combinations of these methods to create new artificial intelligence algorithms and computational programs.

[0088] In order to more clearly demonstrate the purpose and technical solution of the present invention, the present invention is further described in detail below with reference to the 205 / 55R16 tire specification, through the braking distance shortening effect when the braking force is optimally distributed to each tire. Figure 6 The implementation results shown show that the braking effect of installing smart sensors is more than 20% higher in dry braking and more than 25% higher in wet braking than the existing traditional methods. Figure 7 The tire braking force predicted by the sensor algorithm is shown to be quite consistent with the comparison results of the finite element simulation, further proving the effectiveness of the proposed method.

[0089] The above is a description of the embodiments of the present invention. The above description of the disclosed embodiments will enable professionals in the field to implement or use the present invention. Various modifications to these embodiments will be apparent to professionals in the field. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the shortest braking distance of a vehicle based on smart tires, characterized in that: The smart tire comprises at least one strain gauge MEMS sensor installed on the inside of the tire, along the transverse direction and circumferential direction of the tire cross section, and at least one temperature, tire pressure, and acceleration MEMS sensor assembly installed on the inside of the tire, along the transverse direction and circumferential direction of the tire cross section, and a simple information collection and analysis processing device installed inside the tire. The simple information collection and analysis processing device collects data from the strain gauge MEMS sensor and the temperature, tire pressure, and acceleration MEMS sensor assembly and feeds it back to the vehicle control system. The method comprises the following steps: 1) Calculate the vehicle's forward speed V0 based on the acceleration MEMS sensor measurement data; 2) Calculate the tire angular velocity ω and speed V1 based on the periodic changes in the contact waveform of each tire; 3) The slip rate S of each tire during braking is calculated according to the following formula; S=(V0-V1) / V0 Substitute the slip rate S obtained from the above formula into the friction coefficient-slip rate relationship database to obtain the corresponding dynamic friction coefficient f i , i=1,2……n; 4) Calculate the load N of each tire based on the onboard intelligent tire control system algorithm i， i=1,2……n; the specific process steps are as follows: 4.1) Real-time acquisition of tire acceleration or strain gauge sensor waveforms during tire rotation; 4.2) Extracting waveform features between the tire contact area and non-contact area; 4.3) Constructing the optimal feature subset through filtering and transformation techniques; 4.4) Establish the relationship between tire load and ground contact waveform characteristics under standard operating conditions; 4.5) Establishing an intelligent tire force prediction fusion model through parameter iteration; 4.6) Calculate the load on each tire using an improved neural network algorithm based on the fusion model; 5) Further calculate the ground friction force F of each tire i =N i *f i (i=1,2……n); 6) Calculate the result F i Feedback to vehicle control system; 7) The vehicle control system predicts the friction force F i The size of the brake is used to distribute the most appropriate braking force required; The method also includes calculating the braking distance L based on the mechanical principle of vehicle motion energy balance. The specific process steps are as follows: 1) Algorithm predicts vehicle speed V0 and friction coefficient f i ; 2) From f i Select the maximum value of the friction coefficient f max ; 3) According to the principle of kinematic energy balance, the braking distance L = (V0) 2 / (2g×f max ); 4) The predicted braking distance L is uploaded to the terminal display system.

2. The method for controlling the shortest braking distance of a vehicle based on smart tires according to claim 1, characterized in that: The friction coefficient-slip rate relationship database is updated by selecting measured data under different road types, different road conditions, and different speed conditions.

3. The method for controlling the shortest braking distance of a vehicle based on smart tires according to claim 1, characterized in that: The method also includes the on-board intelligent tire control system collecting real-time information including vehicle speed, tire speed of each tire, contact mechanical characteristics between tire and road surface, and road surface type and condition information. After artificial intelligence and machine learning algorithm analysis, it accurately predicts parameter information including the load and wear degree borne by each tire, friction coefficient and slip rate, and feeds it back to the vehicle control system. After calculation and analysis, the control system optimally distributes braking force to each tire to achieve the shortest braking distance.

4. The method for controlling the shortest braking distance of a vehicle based on smart tires according to claim 3, characterized in that: The algorithm analysis of artificial intelligence and machine learning includes fully connected neural networks, improved neural networks, convolutional neural networks, support vector machines, random forests, fault trees, logistic regression and linear regression methods, as well as the cross-combination of various methods into new artificial intelligence algorithms and computing programs.

5. A vehicle control system, characterized in that: The vehicle control system adopts the method according to any one of claims 1 to 4 to control the shortest braking distance of the vehicle.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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