Tire Pressure Monitoring Research Articles

Development of Communication System between TPMS and Server using Combination of OFDM and Convolutional Code Technique Based on SDR

The Tire Pressure Monitoring System (TPMS) has evolved into an essential element of contemporary vehicles, playing a pivotal role in enhancing road safety and the overall driving experience. Traditionally, TPMS systems rely on dedicated hardware components for the collection and transmission of tire pressure data to the vehicle's onboard computer and the data is visible only to the driver. In this research, we have developed a wireless communication system between TPMS and a server, enabling tire pressure data to be accessible not only to the driver but also remotely traceable by others. To build a reliable communication system, we utilized a combination of Orthogonal Frequency Division Multiplexing (OFDM) and Convolutional Code technologies. This system is implemented using Software-Defined Radio (SDR) technology. This communication method employs OFDM to enhance data throughput and integrates Convolutional Code to mitigate errors in received data. Consequently, this approach achieves a maximum throughput of 119.19MBps when utilizing the OFDM system alongside 16QAM modulation. The bit error rate for received data without coding stands at 5.77%, but the application of Convolutional Code with a 1/2 code rate effectively reduces this error rate to 3.85%. This system improves the reliability of TPMS communication with the server while also ensuring a consistently high throughput. It enhances road safety and remote monitoring capabilities.

Design and Implementation of Tire Pressure and Temperature Monitoring System for Hatchback and Multi-Purpose Vehicle Based on IoT

Tire Pressure Monitoring Systems (TPMS) have developed into an essential element in vehicles to improve safety and driving experience. In general, TPMS systems rely on special hardware to collect and transmit tire pressure data to the vehicle's on-board computer and this data can only be viewed by the driver and passengers in the vehicle. In this study, we developed a remote tire pressure and temperature monitoring system using IoT technology. The MQTT protocol facilitated communication between the cloud server and controller, while the system uses Docker container to simplify program integration. The results revealed the optimal standard deviation for tire pressure in hatchback vehicles to be 2.24, and for Multi-Purpose Vehicles (MPVs), it was 2.97. For tire temperature, the best standard deviation in hatchback vehicles was 1.93, compared to 1.05 in MPVs. This system effectively monitors tire pressure and temperature changes in real time, accessible remotely via smartphones and computers.

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

The future of tire energy: a novel one-end cap structure for sustainable energy harvesting

Piezoelectric energy harvesting is gaining popularity as an eco-friendly solution to harvest energy from tire deformation for tire condition monitoring systems in vehicles. Traditional piezoelectric harvesters, such as cymbal and bridge structures, cannot be used inside tires due to their design limitations. The wider adoption of renewable energy sources into the energy system is increasing rapidly, reflecting a global attraction toward the utilization of sustainable power sources (Aljendy et al. in Int J Power Energy Convers 12(4): 314–337, 2021; Yesner et al. in Evaluation of a novel piezoelectric bridge transducer. In: 2017 Joint IEEE International Symposium on the Applications of Ferroelectric (ISAF)/International Workshop on Acoustic Transduction Materials and Devices (IWATMD)/Piezoresponse Force Microscopy (PFM). IEEE, 2017). The growing interest in capturing energy from tire deformation for Tire Pressure Monitoring Systems (TPMS) aligns with this trend, providing a promising and self-sustaining alternative to traditional battery-powered systems. This study presents a novel one-end cap tire strain piezoelectric energy harvester (TSPEH) that can be used efficiently and reliably inside a tire. The interaction between the tire and energy harvester was analyzed using a decoupled modeling approach, which showed that stress concentration occurred along the edge of the end cap. The TSPEH generated a maximum voltage of 768 V under 2 MPa of load, resulting in an energy output of 32.645 J/rev under 1 MPa. The computational findings of this study were consistent with previous experimental investigations, confirming the reliability of the numerical simulations. The results suggest that the one-end cap structure can be an effective energy harvester inside vehicle tires, providing a valuable solution for utilizing one-end cap structures in high-deformation environments such as vehicle tires.

Monitoring the condition of nitrogen-filled tires using weightless neural networks

The novelty of this paper revolves around monitoring the condition of nitrogen-filled tires through the fusion of features and utilization of weightless neural networks. A tire pressure monitoring system (TPMS) plays a crucial role in ensuring vehicle safety and comfort. Reckless driving, poor road conditions, continual operation, higher road friction and excessive load are certain factors that can degrade the longevity of tires. Such conditions can result in instantaneous fault attacks in tires raising a concern for safety and comfort. To apply instantaneous and accurate fault diagnosis, the present study leverages machine learning techniques through the integration of an adaptive and robust algorithm, namely, the Wilkes, Stonham and Aleksander Recognition Device (WiSARD) classifier. The experiment uses three types of features namely, statistical, histogram and autoregressive moving average (ARMA) features. The J48 decision tree algorithm was used to pinpoint the key attributes crucial for classification. Following this, the identified attributes were segregated into training and testing datasets, facilitating the evaluation of the WiSARD classifier. Hyperparameter tuning was carried out to achieve optimal value for maximizing classification accuracy and minimizing computational time. Among the features considered, ARMA features delivered the best test set accuracy of about 96.18%.

Application of feature fusion strategy for monitoring the condition of nitrogen filled tires using tree family of classifiers

The tire pressure monitoring systems (TPMS) are dedicated vehicle systems that calculate the tire pressure under various conditions. Proper maintenance of tire pressure can have a significant impact on enhancing vehicle handling, vehicle performance, occupant safety, comfort and fuel efficiency. Recent years have seen a shift in the preference for nitrogen-filled tires over air filled due to the superior thermal stability and uniform pressure management characteristics displayed by nitrogen-filled tires. The present article details the application of machine learning in TPMS to provide more insights into the tire behaviour for four different tire conditions (puncture, idle, high and normal). This paper specifically focuses on the collaborative approach that combines various features extracted with tree-based classifiers. Vertical wheel hub vibrations were captured using an accelerometer from which distinct features like autoregressive moving average (ARMA), statistical and histogram were extracted. With the application of J48, the most significant and contributing features were identified for every feature set extracted that was fed into tree-based classifiers. The best-performing classifier for every feature set was determined to be 95.83% (statistical–random forest), 93.75% (histogram-optimized forest) and 93.75% (ARMA–random forest). Furthermore, an extensive analysis was carried out to determine the impact of the feature fusion approach on feature combinations like statistical-histogram, histogram-ARMA, statistical-ARMA and statistical-histogram-ARMA. The experimental results indicate a commendable classification accuracy of 97.92% for a feature fusion of statistical-histogram-ARMA features with a forest penalizing attributes algorithm.

A Blockchain-Based Mutual Authentication Method to Secure the Electric Vehicles’ TPMS

Despite the widespread use of Radio Frequency Identification (RFID) and wireless connectivity such as Near Field Communication (NFC) in electric vehicles, their security and privacy implications in Ad-Hoc networks have not been well explored. This paper provides a data protection assessment of radio frequency electronic system in the Tire Pressure Monitoring System (TPMS). It is demonstrated that eavesdropping is completely feasible from a passing car, at an approximate distance up to 50 meters. Furthermore, our reverse analysis shows that the static <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -bit signatures and messaging can be eavesdropped from a relatively far distance, raising privacy concerns as a vehicles' movements can be tracked by using the unique IDs of tire pressure sensors. Unfortunately, current protocols do not use authentication, and automobile technologies hardly follow routine message confirmation so sensor messages may be spoofed remotely. To improve the security of TPMS, we suggest a novel ultra-lightweight mutual authentication for the TPMS registry process in the automotive network. Our experimental results confirm the effectiveness and security of the proposed method in TPMS.

A Compact and High-Efficiency Antenna Design for Tire Pressure Monitoring System Applications

A Compact and High-Efficiency Antenna Design for Tire Pressure Monitoring System Applications

In-Vehicle Data Communication with CAN &amp;Security Monitoring: A Review

Automobiles are now being created with more electronic components for efficient functioning such as Anti Lock Braking system, Adaptive Cruise Control, Traction control system, Airbag, Power Steering etc. managed by networked controllers that include hundreds of ECUs (electronic control units) that can coordinate, control, and monitor loads of internal vehicle components. Each component, such as ABS, TCS (Traction control system), tire pressure monitoring system and telematics system, may communicate with nearby components over the CAN (Controller Area Network) bus, establishing an in-vehicle communication network. These modern automobile system networks intended for safety with minimal consideration for security have drawn the attention of researchers for providing security in CAN. The Paper reviews the behavior and vulnerabilities of CAN within an in-vehicle network including various attacks possible in CAN along with the proposed solutions in the literature with extensive survey on a security promising approach named as IDS (Intrusion detection system).

Design and Implementation of Intelligent Tire Detection System Based on Esp8266 IoT Platform and Deep Learning Technology

&lt;p&gt;傳統上，市售的胎壓偵測系統(TPMS)僅能透過測量胎壓、胎溫來判斷輪胎狀態，無法即時檢測如：輪框變形、胎面異常剝離、輪胎附著異物、異常抖動及打滑等的輪胎異常狀況，若駕駛有所疏忽，將造成嚴重的危害。因此，我們研究設計一套智慧輪胎系統，透過量測輪胎運行間的慣性感測資訊，包含：三軸加速度、角速度、絕對方向，再結合 AI 演算法之辨識技術，動態檢測出輪胎異常狀況，並實作設計下述物聯網平台元件 : 1.安裝於輪框上之硬體裝置，可偵測輪胎行進間之異常數值，2.具即時監測與異常提醒、維修保養建議、車友討論平台等功能之手機應用程式，3.基於深度學習神經網路技術辨識各式輪胎異常狀況，4.建立跨平台智慧輪胎網頁介面供職業車隊管理停車場。經實驗測試結果，本系統的 AI 判讀準確率高達 99.17%。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;The tire pressure monitoring system (TPMS) sold on the market can only judge the tire status by measuring the tire pressure and tire temperature, and cannot detect such things as: wheel frame deformation, abnormal tread peeling, foreign objects attached to the tire, abnormal vibration and skidding, etc. If the tire is abnormal, if the driver is negligent, it will cause serious harm. Therefore, we design a set of smart tire system, through the measurement of inertial sensing information during tire operation, including: three-axis acceleration, angular velocity, absolute direction, combined with the identification technology of AI algorithm, dynamically detect abnormal tire conditions, and Realize the design of the following IoT platform components: 1. The hardware device installed on the wheel frame can detect abnormal values during tire travel. 2. It has real-time monitoring and abnormal reminders, maintenance suggestions, and a discussion platform for riders, etc. Functional mobile phone application, 3. Based on deep learning neural network technology to identify various tire abnormalities, 4. Establish a cross-platform smart tire web interface for professional fleets to manage parking lots. According to the experimental test results, the AI interpretation accuracy rate of this system is as high as 99.17%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Indirect tire pressure monitoring method based on the fusion of time and frequency domain analysis

Monitoring tire pressure conditions and issuing timely warnings of tire deflation are important to ensure driving safety and avoid unnecessary fuel consumption. Although installing pressure sensors is a direct and accurate monitoring method, it may raise system cost and complexity. The indirect monitoring method with no reliance on pressure sensors is attractive for economical cars. This paper proposes a fusion-based indirect tire pressure monitoring method using existing on-board wheel speed sensors. The screening basis of reasonable wheel speed signals was determined by data mining of typical driving scenarios. Three typical signal distortions were effectively corrected to ensure the signal quality. Tire deflation analysis methods based on time and frequency domain signals were designed and fused using fuzzy logic. Validation results of the proposed two analysis methods and their fusion strategy showed that the proposed fusion strategy can achieve more comprehensive tire deflation monitoring with fast response and high accuracy.

Design of Indirect Tire Pressure Monitoring System Based on Wheel Speed Signal

Keeping the pressure of the automobile tire in the normal working range is an important guarantee for the safety of the automobile. When the tire leaks, the driver should be reminded to check or replace the tire before the potential danger of blowing out. Aiming at the safety problem of commercial vehicle driving, a set of tire pressure monitoring alarm systems is designed by combining the radius method and frequency method. The magneto-electric wheel speed sensor is used to collect and process the tire speed, and then through the data processing, the full range of monitoring each tire’s possible air leakage hazards, such as emergency can be quickly warned, thus providing a reference for the driver’s driving. The experimental results show that the system can effectively improve the accuracy and stability of tire pressure monitoring.

Compressor-Powered Tyre Pressure System for Monitoring

Abstract: In the Indian subcontinent, where one in every three houses owns a two-wheeler, around fifteen million motorcycles are sold each year. Every year, the country manufactures 20 million two-wheelers. Severe underinflation can produce mechanical and thermal stress caused by overheating, which can cause the tyre to blow out soon. Failures of this type can lead to hazards and catastrophes that endanger not just the operator but also people around them or her. In accordance with median nation estimations, under-inflated tyres cause separation of the tread and tyre breakdown, leading to forty thousand mishaps, more than 33,000 damage, and over 700 fatal injuries per year. However, when the population of India has been taken into consideration, these percentages may rise due to an abundance of individuals possessing numerous 2-wheelers, getting little opportunity to personally check, as well as being unable to consistently get their automobiles examined at a maintenance facility. The difficulties will occur with greater regularity if the riders are unaware. The goal of this project is to develop and build an immediate tyre pressure monitoring system (TPMS), that utilises a pressure sensor in order to measure air pressure directly from the tyre.

Design and Verification of a Novel Energy Harvester for Tire Pressure Monitoring Systems

With the development of intelligent tires, the tire pressure monitoring system (TPMS) has become a standard safety feature in cars. However, the existing TPMS has limited ability to monitor tire pressure in real time due to the passive power supply device’s low power output. This work presents a conceptual design for a novel energy harvester for TPMS (NEH-TPMS) based on a mechanical structure to recover energy. The motion of the mechanical structure is driven by the deformation of the tire in contact with the ground. The energy is recovered and released by a spiral spring to accomplish the functions of power generation and charging. Mathematical models are created based on the NEH-TPMS’s movements. The simulation results indicate that the NEH-TPMS’s power generation capacity is greater than that of existing energy harvesters and can satisfy the TPMS’s power supply requirements. This work uses finite element analysis and hierarchical analysis to optimize the shape of the NEH-TPMS. The parameters of the spiral spring are optimized using simulated annealing and genetic algorithms. NEH-TPMS has been enhanced to provide greater energy storage capacity. Finally, a prototype was built to verify the structure’s feasibility. The experimental results are consistent with the simulated results. This NEH-TPMS offers an efficient means of enhancing the power generation efficiency of the passive power supply device for TPMS.

Tyre Pressure Monitoring System with Compressor

Abstract: Around 15 million two-wheelers are sold annually in India, where one in three homes has a two-wheeler. The country produces 20 million units of two-wheelers annually. Severe underinflation can result in mechanical and thermal stress brought on by overheating, which can very quickly cause the tyre to blow out. Failures of this nature may result in dangers and mishaps that jeopardise not only the rider but also those around him or her. Under-inflated tyres cause tread separation and tyre failure, resulting in 40,000 accidents, 33,000 injuries, and over 700 fatalities each year, according to average country estimates. However, when taking into account India's population, these figures may soar due to a high number of people owning more 2- wheelers, having less time to physically check, and being unable to routinely get their cars inspected at a repair facility. Without the riders' knowledge, the aforementioned issues will occur more frequently. This project focuses on designing and creating a direct tyre pressure monitoring system (TPMS), which uses a pressure sensor to measure air pressure taken straight from the tyre. The rider does not need to get out of his vehicle and manually pump air into his tyres when the TPMS detects a reduced tyre pressure because the TPMS and the compressor unit are working together to supply the air to the tyre. Energy is provided to the gadget either by a dynamo or a backup battery. To ensure consistency and accuracy in reporting the pressure and providing enough air to the tyre, the key components must be calibrated. Although there are numerous challenges and constraints, this prototype is a promising product for use in the real world.

Real-Time Monitoring of Tire Condition with Fast Detection Passive and Wireless TPMS

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Accurate tire pressure monitoring system (TPMS) is of great practical importance and the reliability and safety of its power supply module has great concern. The piezoelectric-based surface acoustic wave (SAW) sensor is considered to have great potential in this field because of its passive, wireless and small size advantages. This paper presents the application of passive and wireless SAW sensors for real-time tire condition monitoring. The pressure sensitive structure is optimized and a three-resonator structure is also designed sensing temperature and pressure. Furthermore, a fast detection system is developed to realize high-speed signal acquisition. At last, experiments are executed and the SAW temperature and pressure sensor property is measured. The results show that the designed SAW sensor can realize real-time monitoring of tire condition; the temperature measurement range can reach -40~120°C with an accuracy of ±1°C; the pressure measurement range can reach 0~2MPa with an accuracy of 0.2MPa. This paper indicates the wireless and passive SAW temperature and pressure sensor system has the potential for application in real-time monitoring of tire condition.&lt;/div&gt;&lt;/div&gt;

Enhanced Tyre Pressure Monitoring System for Nitrogen Filled Tyres Using Deep Learning

Tyre pressure monitoring systems (TPMS) are electronic devices that monitor tyre pressure in vehicles. Existing systems rely on wheel speed sensors or pressure sensors. They rely on batteries and radio transmitters, which add to the expense and complexity. There are two types of basic tyres: non-pneumatic and pneumatic tyres. Non-pneumatic tyres lack air and combine the tyre and wheel into a single unit. When it comes to noise reduction, durability, and shock absorption, pneumatic tyres are more valuable than non-pneumatic tyres. In this study, nitrogen-filled pneumatic tyres were considered due to the uniform pressure management property. Additionally, nitrogen has less of an effect on thermal expansion than regular air-filled tyres. This work aimed to offer a deep learning approach for TPMS. An accelerometer captured vertical vibrations from a moving vehicle’s wheel hub, which were then converted in the form of vibration plots and categorized using pretrained networks. The most popular pretrained networks such as AlexNet, GoogLeNet, ResNet-50 and VGG-16 were employed in this study. From these pretrained networks, the best-performing pretrained network was determined and suggested for TPMS by varying the hyperparameters such as learning rate (LR), batch size (BS), train-test split ratio (TR), and solver (SR). Findings: A higher classification accuracy of 97.20% was obtained while using ResNet-50.

EXPERIMENTAL ANALYSIS OF A 2.4 GHZ ANTENNA FOR TYRE PRESSURE MONITORING SYSTEM

Antennas used for tyre pressure monitoring systems are usually enclosed by the tyre. On its own, the antenna may have excellent properties. However, the wheel and tyre may have an impact on the performance of the antenna. This paper investigated the performance of a simple 2.4 GHz whip antenna for a tyre pressure monitoring system. The antenna was modelled using HFSS, while the wheel and tyre were modelled using SolidWorks. The wheel and tyre can have an impact on the antenna performance. The reflection coefficients, VSWR, gain and radiation pattern were determined through simulations, using HFSS. The antenna operation was checked at different positions and configurations to the wheel to determine its’ optimum position for effective signal radiation. The four different configurations were: antenna grounded to the middle of the rim, antenna ungrounded to the wheel’s edge, antenna grounded to the wheel’s edge, and antenna grounded to the wheel’s edge enclosed by the tyre. The result shows that electrically grounding the antenna to the wheel by the edge improves its performance. Also, the tyre did not negatively impact the far-field gain of the antenna.

A low-cost in-tire-pressure monitoring SoC using integer/floating-point type convolutional neural network inference engine

Tire pressure state is crucial for driving performance and fuel efficiency and is the welfare of the driver. Tire inflation pressure greatly affects fuel consumption, driving dynamics and the tire lifespan. Hence, the state of tire pressure must be continuously sensed for comfort riding, optimal vehicle handling, and safety, and it is necessary for all types of transport. The existing detection methods are high cost and without any redundancy, if the sensor gets fails, which retard its wide application. The support vector machine (SVM) learning method is introduced for tire pressure loss detection. But, this method is not so accurate in detecting the normal and pressure loss judgement of tires. This research presents a novel approach for a tire pressure monitoring system (TPMS) to reduce the cost of sensor-based frames. The proposed model comes under three stages: pre-processing, feature extraction and tier pressure detection. Initially, an adaptive and unbiased Kalman filter (KF) with a recursive least square (RLS) algorithm (AUK-RLS) approach is introduced to eliminate manufacturing errors in speed gear. The second stage is the extraction of features. Statistical, frequency domain, and fitted frequency domain characteristics features are extracted from the wheel speed signal in this stage. It is fed to the deep learning (DL) model to perform the judgement of tier state. While using floating point model for the operation and storage process, the hardware cost of the DL model tends to be high. To tackle this issue, the convolutional neural network (CNN) hardware design will be optimized by combining both Integer/Floating-Point type inference engines. The design modules of the CNN interference engine are insisted of buffers, on-chip/off-chip memories, controllers, fine state machine (FSM), processing engine (PE) and routers. The PE module needs several adders and multiplier units, and this performs the convolution operation. Therefore, to diminish the area, delay and power consumption, the modified adder and multiplier design named Vedic multiplier based Carry select adder with simplified combinational logics (CSLA-SCL) is emphasized. The proposed system is implemented using Verilog code synthesis in the Xilinx ISE 14.5 simulator platform. The performance measure of pressure loss detection in terms of accuracy (99.25%) and errors are examined. Also, the performance measure of the detection model with the proposed adder and multiplier design is examined in terms of area utilization, power (145 mW), frequency (756 MHz) and delay (7.12 ns).