Indirect Tire Pressure Monitoring Research Articles

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.

Application of Machine Learning Technique for Development of Indirect Tire Pressure Monitoring System

<div class="section abstract"><div class="htmlview paragraph">Tire inflation pressure has a significant impact over vehicle driving dynamics, fuel consumption as well as tire life. Therefore, continuous monitoring of tire pressure becomes imperative for ride comfort, safety and optimum vehicle handling performance. Two types of tire pressure monitoring systems (TPMS) used by vehicles are - direct and indirect TPMS. Direct systems deploy pressure sensors at each wheel and directly send pressure value to the vehicle Controller Area Network (CAN). Indirect sensors on the other hand use the information from already existing sensors and some physics-based equations to predict the value of tire pressure. Direct TPMS tend to be more accurate but have higher cost of installation while indirect TPMS comes with a minimum cost but compromised accuracy.</div><div class="htmlview paragraph">A digital proof-of-concept study for indirect TPMS development of a non-ESP vehicle based on machine learning (ML) technique is elaborated in this paper. The study aims to propose a methodology for development of an indirect TPMS having an accuracy equivalent to that of a direct TPMS. A full vehicle model designed in Amesim software is used to extract data to train the machine-learning algorithm for different test cases. Simulation model is validated against the test data of vehicle dynamics parameters to ensure the accuracy of data extracted for ML model training. Multilayered feed forward, back-propagation artificial neural network is trained using three prediction algorithms and sensitivity of different algorithms, network parameters is analyzed against selected driving scenarios.</div><div class="htmlview paragraph">Proof-of-concept study suggests that the proposed tire pressure prediction algorithm has a potential to predict tire pressure accurately at par with Direct TPMS. It lays a foundation for developing ML based indirect TPMS using physical testing data by providing assistance in test plan preparation, exploring data pre-processing techniques and algorithm selection. Furthermore, the generic methodology mentioned in this paper can be referred for initial development of any ML based project.</div></div>

New Indirect Tire Pressure Monitoring System Enabled by Adaptive Extended Kalman Filtering of Vehicle Suspension Systems

This paper presents a new indirect tire pressure monitoring system (TPMS) based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested methodology is based on the explicit correlation between tire pressure and tire stiffness and is available in real time. AEKF-UI is used to simultaneously estimate the time-varying parameter (tire stiffness) of vehicle suspension systems and the road roughness using an unknown input estimator. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate tire stiffness (i.e., tire inflation pressure) variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm are verified through a laboratory-level experiment. This study offers a potential application for an alternative indirect TPMS and the estimation of unknown road roughness used for automotive controller design.

Robust estimation method of tire torsional resonance frequency to detect decrease in tire inflation pressure

Indirect tire pressure monitoring system monitors inflation pressures in pneumatic tires using wheel speed sensor (WSS) signals acquired from anti-lock braking system. In order to monitor the tire pressures indirectly, frequency analysis and wheel radius analysis are operating in parallel. This paper focuses on the frequency analysis. The resonance frequency of tire torsional vibration decreases as the tire pressure decreases. Therefore, the resonance frequency needs to be estimated to detect tire pressure decrease. This paper describes the resonance frequency estimation method with the WSS. Firstly, toothed wheel error of the WSS is calculated from the wheel speed signals and the wheel speed is corrected with the toothed wheel error. A notch filter is designed based on the engine rotational speed to eliminate the engine noise. The resonance frequency is estimated by model parameter estimation based on auto regressive model. Finally, compensation of the estimated frequency, detection of snow chain and monitoring of severe driving environments have been proposed to prevent false alarms at normal tire pressures. Vehicle tests have been conducted to evaluate the proposed estimation method of the resonance frequency. It has been shown that the proposed method provides satisfactory performance for warning indication of tire pressure loss from the test results.

New Hybrid Wavelet and CNN-Based Indirect Tire-Pressure Monitoring System for Autonomous Vehicles

Since the tire pressure has a significant influence on driving safety, even self-driving vehicles need to be aware of their current tire pressures. Two major types of methods for estimating tire pressures exist: direct and indirect methods. In spite of recent advancements in direct Tire Pressure Monitoring Systems (TPMSs), indirect pressure monitoring systems still play a significant role due to their low costs. Indirect systems rely on the processing of signals from wheel speed sensors. In most cases, a transformation is applied to generate a frequency spectrum from which the tire pressure-dependent eigenfrequency can be extracted. The most accurate methods apply the Fourier transform, but these require the highest computational power. After the spectrum of signals from the wheel speed sensor is created, the eigenfrequency must be extracted. Several methods are available to extract significant frequency components. One of the easiest methods is peak searching, however, it is susceptible to noise. On the other hand, more accurate methods that are less sensitive to noise require more computational power. If a transform that consumes less computational power can be applied, then the freed resources can be used by a better eigenfrequency identification method. In this paper, a Hybrid Wavelet-Fourier Transform and Convolutional Neural Network-based method is presented, which exhibits a promising level of noise tolerance.

Artificial Neural Network-Based Method for Identifying Under-Inflated Tire in Indirect TPMS

Tire pressure monitoring is essential to driving safety. Indirect tire pressure monitoring system (TPMS) is a cost-effective alternative to direct tire pressure monitoring system. Its performance depends on the algorithm for data pre-processing and analysis which is normally complicated, sensitive to initial calibration with limited working range. In this work, four tests were carried out with Baojun 530 with a different deflated tire in each case. Speed data read through ABS CAN bus was analyzed and traditional frequency based method was employed to identify the deflated tire. To simplify the data pre-processing and improve response speed and working range, a new artificial neural network (ANN) based method was also proposed to identify deflated tire based on speed data point collected through antilock brake system (ABS) sensors in tests. A long short-term memory (LSTM) network was developed to locate the deflated tire with an accuracy of 0.83 after training for individual data points. And performance of this method can be further improved by employing a soft voting mechanism with 3 LSTM networks. In proposed ANN based method, benchmark data from properly inflated tire is not required, which makes it a promising solution for multiple deflated tires cases which is challenging for traditional frequency-based method.

타이어 저압감지를 위한 타이어 공진 주파수 추정

Indirect tire pressure monitoring system(i-TPMS) monitors the inflation pressures in pneumatic tires by using wheel speed signals acquired from the anti-lock braking system(ABS). Frequency analysis and wheel radius analysis are performed at the same time in order to monitor the tire pressures indirectly. The resonance frequency of the tire torsional vibration decreases as the tire pressure decreases. It is necessary to estimate the resonance frequency in order to detect a reduction in tire pressure. This paper discusses the method of estimating the resonance frequency with the rotational wheel speed signal. First, the toothed wheel error of the wheel speed sensor and the wheel speed corrected with the toothed wheel error are calculated from the wheel speed signals. The engine noise caused by the explosion in the four-stroke of engine is eliminated by the notch filter, which is designed by using the engine rotational speed. Finally, the resonance frequency is evaluated via model parameter estimation based on the autoregressive(AR) model. Vehicle tests have been conducted in order to evaluate the proposed estimation method of resonance frequency. The findings of the vehicle test have shown that the proposed method provides satisfactory performance for the warning indication of tire pressure loss.

Technical Review: Indirect Tire Pressure Monitoring Systems and Tire Vibrations

ABSTRACT Indirect tire pressure monitoring systems (ITPMSs) have been an active area of research for the past 2 decades. Researchers worldwide have strived to develop estimation techniques for the detection of the change in tire pressure by using the vibration information present in the speed signal. Different groups have used a torsional vibration model for the tire, owing to its torsional stiffness and rotational moment of inertia. The standard antilock braking system (ABS) speed sensor signal is analyzed for these vibrations. Different estimation algorithms try to detect the change in this vibration frequency, which indicates the change in the torsional stiffness of the tire as a result of variation in the pressure. Tire vibrations have been studied in great detail for the past 5 decades, and there are various models of tire vibrations available in the literature. These models range from physics-based analytical models to finite element models (FEMs). Analytical models take benefit from the mathematics developed for rotating elastic thin shells and plates, whereas FEMs use simulation tools to develop vibration models of the tire. A detailed literature survey of ITPMSs and tire vibration models reveals that there is no correlation between the vibrations detected in the speed signal and the vibrations predicted in the tire vibration models. Researchers have developed tire vibration models that do not take into consideration the effects of vibrations on the speed signal; although, to the best of our knowledge, signal processing and estimation experts who have developed methods for ITPMSs have not validated the true source of observed vibrations in the speed signal and could not present a viable theoretical explanation. In this review, a comprehensive study of the ITPMS techniques and tire vibration models is presented, with an aim to find a correlation between them. The review begins with a brief introduction to the topic followed by state of the art, then a detailed review of ITPMSs and the methods for their realizations in the automotive industry. Finally, tire vibration models are presented in detail, and possible links between vibration models and ITPMS vibrations are sorted.

Comparative Analysis in Indirect Tire Pressure Monitoring Systems in Vehicles

Abstract Tire Pressure Monitoring Systems (TPMS) are modules implemented in highly instrumented vehicles to analyze the tire pressure condition at any time. When the vehicle is lack of this instrumentation level, indirect TPMS can be used by considering the vehicle dynamics behavior through conventional sensors. In this paper, two different approaches have been studied to monitor indirectly the tire pressure condition in a vehicle. The first approach one is based on the angular speeds of the four wheels by considering the vehicle load transfer, while the second method is based on the vertical vehicle dynamics by using each corner as an independent monitoring system. Both approaches have been compared in different simulation scenarios in order to highlight their main advantages and drawbacks to consider for their implementation.

Applying Neural Networks for Tire Pressure Monitoring Systems

A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with MATLAB and Simulink to generate... | Find, read and cite all the research you need on Tech Science Press

Research on the compensation method of Indirect Tire Pressure Monitoring under Sinusoidal Driving Condition

Indirect tire pressure monitoring method (ITPMS) is based on the wheel speed signal differences indirectly infer that the change of the tire pressure. In the steering mode, there is a large yaw rate, and there is a big deviation between the four wheels’ speed. The traditional indirect tire pressure monitoring method based on wheel speed comparison method is invalid. That the automobiles which is matched electronic stability control system estimate the four-wheel speed according to the yaw rate and steering wheel angle become an important method to improve the accuracy of Indirect tire pressure monitoring. Four-wheel vehicle kinematics model for turning condition is established to compensate for the wheel speed in the steering mode, avoid the misjudgment of indirect tire pressure monitoring system. The seven degree of freedom vehicle model and the four wheel speed model are established firstly, and analyse the four wheel speed change of the vehicle under the condition of a certain corner input in the simulation. Then conduct a test on the vehicle road, calculated using the four wheel speed distribution module model of vehicle wheel speed on the theoretical value of the actual value of the wheel speed compensation, so it can monitor the tire pressure in turning vehicle. When the error of the wheel speed is beyond the threshold, the wheel can be judged to be in the state of lack of gas.

Monitoring Method of Indirect TPMS Under Steering Situation

Conventional indirect tire pressure monitoring system (TPMS) is mainly designed according to the wheel speed signal of anti-lock brake system (ABS). Four wheels’ velocity change because yaw rate exists when vehicles are under steering conditions. The original tire pressure monitoring method failed which is based on the wheel speed comparison method. As the electronic stability program (ESP) is applied in vehicle, inertial measurement unit that can obtain vehicle’s yaw rate are widely used. In this passage, four-wheel trajectory model is established. Every wheel’s side-slip angle and steering radius can be deduced by using this model when the steering wheel in sinusoidal input and step input. Four wheels’ velocity can be quantified through turning radius, steering angle and yaw rate. On the basis of the original wheel speed comparison method, the indirect TPMS under steering situation can be achieved.

ParEGO extensions for multi-objective optimization of expensive evaluation functions

This paper deals with multi-objective optimization in the case of expensive objective functions. Such a problem arises frequently in engineering applications where the main purpose is to find a set of optimal solutions in a limited global processing time. Several algorithms use linearly combined criteria to use directly mono-objective algorithms. Nevertheless, other algorithms, such as multi-objective evolutionary algorithm (MOEA) and model-based algorithms, propose a strategy based on Pareto dominance to optimize efficiently all criteria. A widely used model-based algorithm for multi-objective optimization is Pareto efficient global optimization (ParEGO). It combines linearly the objective functions with several random weights and maximizes the expected improvement (EI) criterion. However, this algorithm tends to favor parameter values suitable for the reduction of the surrogate model error, rather than finding non-dominated solutions. The contribution of this article is to propose an extension of the ParEGO algorithm for finding the Pareto Front by introducing a double Kriging strategy. Such an innovation allows to calculate a modified EI criterion that jointly accounts for the objective function approximation error and the probability to find Pareto Set solutions. The main feature of the resulting algorithm is to enhance the convergence speed and thus to reduce the total number of function evaluations. This new algorithm is compared against ParEGO and several MOEA algorithms on a standard benchmark problems. Finally, an automotive engineering problem allowing to illustrate the applicability of the proposed approach is given as an example of a real application: the parameter setting of an indirect tire pressure monitoring system.

Comparative Analysis of Tire Evaluation Methods for an indirect Tire Pressure Monitoring System (iTPMS)

Comparative Analysis of Tire Evaluation Methods for an indirect Tire Pressure Monitoring System (iTPMS)

Test Model Designed for Tire Pressure Monitoring Control System

In order to monitor tire pressure changes all the time, prevention of blowout and other causes of traffic accidents. By using of Single chip microcomputer(MCU) multitasking technology design of control system for the indirect tire pressure monitoring model. Through the photoelectric sensors to collect four groups tire speed information and develop a series of algorithms and determination method to judge the working state of the tires. It realizes the abnormal tire pressure alarm, and has made up the traditional indirect tire pressure monitoring method insufficiency. Draw the following conclusion: Through use of MCU multitasking technology, taking the radius of the changes caused by the tires pressure changes before blowout for comparison and analysis, getting several situations of tire burst judging method, and producing instruction and alarm that plays the purpose of real-time monitoring of tire blowout to prevent the sudden traffic accidents.

Indirect Vehicle Tire Pressure Monitoring with Wheel and Suspension Sensors

Monitoring of the tire pressure is usually performed by direct pressure measurement and wireless transmission to the vehicle ECU's. In order to reduce cost and avoid battery tire sensors, so called indirect tire pressure monitoring is an alternative. First the wheel speeds can be compared to each other. However, this is not sufficient. Therefore, additional measurements are used in this contribution: the vertical wheel speed oscillation and torsional wheel speed oscillation which both depend on the tire pressure. Therefore three methods for pressure loss detection in tires due to punctures (faults) are developed and compared to each other. Finally, an appropriate combination (fusion) of these methods allows to detect small pressure changes in tires. This is shown by experimental test drives with a research car.