Development of MEMS Composite Sensor with Temperature Compensation for Tire Pressure Monitoring System

The pressure and temperature inside the tire is mainly monitored by the tire pressure monitoring system (TPMS). In order to improve the integration of the TPMS system, moreover enhance the sensitivity and temperature-insensitivity of pressure measurement, this paper proposes a microelectromechanical (MEMS) chip-level sensor based on stress-sensitive aluminum-silicon hybrid structures with amplified piezoresistive effect and temperature-dependent aluminum-silicon hybrid structures for hardware and software temperature compensations. Two types of aluminum-silicon hybrid structures are located inside and outside the strained menbrane to simultaneously realize the measurement of pressure and temperature. The model of this composite sensor chip is firstly designed and verified for its effectiveness by using finite element numerical simulation, and then it is fabricated based on the standard MEMS process. The experiments indicate that the pressure sensitivity of the sensor is between 0.126 mV/(V·kPa) and 0.151 mV/(V·kPa) during the ambient temperature ranges from -20 ℃ to 100 ℃, while the measurement error, sensitivity and temperature coefficient of temperature-dependent hybrid structures are individually ± 0.91℃, -1.225 mV/(V·℃) and -0.150%/℃. The thermal coefficient of offset (TCO) of pressure measurement can be reduced from -3.553%FS/℃ to -0.375%FS/℃ based on the differential output of the proposed sensor. In order to obtain the better performance of temperature compensation, Elman neural network based on ant colony algorithm is applied in the data fusion of differential output to further eliminate the temperature drift error. Based on which, the overall measured error is within 3.45 kPa, which is less than ±1.15%FS. The thermal coefficient of offset (TCO) is -0.017%FS/℃, and the thermal coefficient of span (TCS) is -0.020%/FS℃. The research results may provide a useful reference for the development of the high-performance MEMS composite sensor for the TPMS system.

Deposition of Energy using Piezoelectric Material and its Application in TPMS

The limited lifespan in portable, remote and implantable devices and the need to recharge or replace batteries periodically has been a consistent issue. Ambient energy can usually be found in the form of thermal energy, vibrational energy and solar energy. Among these energy sources, vibrational energy presents a constant presence in nature and artificial structures. Energy harvesting through piezoelectric materials by extracting power from ambient vibrations is a promising technology. The material is capable to harvest sufficient energy required to make autonomous and self-powered electronic systems. The characteristic of piezoelectric material is electromechanical coupling between electrical and mechanical domains. The design of a piezoelectric device for the purpose of storing the kinetic energy of random vibrations at the wheel of a vehicle is presented. The harvester is optimized to power the Tire Pressure Monitoring System (TPMS). The aim is to make of the value of power and voltage outputs for different input frequency conditions. A typical TPMS system consists of a battery operated one, in this paper bimorph is designed to powering a TPMS commercial feasibility of this option is compared to existing TPMS modules, which require batteries for operation.

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.

An Intelligent Tire Pressure Monitoring System

Pressure of tire is measured using Tire Pressure Monitoring System (TPMS) model. By measuring the tire pressure, the fuel can be saved and the vehicle can be controlled in better way. The real of tire pressure can be monitored in real time and if the pressure of the tire goes out of the threshold this system gives alarm and alert the driver to ensure the driver safety, hence the life of the driver can be safeguard and pre-warn the driver and passengers. TPMS consists of an electronic unit that sense the tire pressure displays it to user in a LABVIEW platform. This unit includes a pressure sensor and Data Acquisition System. The output of the pressure sensor is given to the Data Acquisition System. Then the DAQ is interfaced with PC to display the tire pressure in the dashboard using the LABVIEW platform. If the tire pressure crosses safe pressure level a warning is generated

Lightweight Crypto Stack for TPMS Using Lesamnta-LW

Modern vehicles which have internal sensor networks are one of the examples of a cyberphysical system (CPS). The tire pressure monitoring system (TPMS) is used to monitor the pressure of the tires and to inform the driver of them. This system is mandatory for vehicles in the US and EU. To ensure the security of TPMS, it is important to reduce the cost of the cryptographic mechanisms implemented in resource-constrained devices. To address this problem, previous works have proposed countermeasures employing lightweight block ciphers such as PRESENT, SPECK, or KATAN. However, it is not clear to us that any of these works have addressed the issues of software optimization that considers TPMS packet protection as well as session key updates for architectures consisting of the vehicle TPMS ECU and four low-cost TPMS sensors equipped with the tires. In this paper, we propose the application of ISO/IEC 29192-5 lightweight hash function Lesamnta-LW to address these issues. When we apply cryptographic mechanisms to a practical system, we consider the lightweight crypto stack which contains cryptographic mechanisms, specifications for the implementation, and performance evaluation. Our approach is to apply the known method of converting Lesamnta-LW to multiple independent pseudorandom functions (PRFs) in TPMS. In our case, we generate five PRFs this way and then use one PRF for MAC generation and four for key derivation. We use the internal AES-based block cipher of Lesamnta-LW for encryption. Although we follow the NIST SP 800-108 framework of converting PRFs to key derivation functions, we confirm the significant advantage of Lesamnta-LW-based PRFs over HMAC-SHA-256 by evaluating the performance on AVR 8-bit microcontrollers, on which we consider simulating TPMS sensors. We expect that our method to achieve multiple purposes with a single cryptographic primitive will help us to reduce the total implementation cost required for TPMS security.