Niclas Persson

Indirect Tire Pressure Monitoring Using Sensor Fusion

Vehicle handling depends critically on the tire-road contact patch. When the tire inflation pressure changes the contact patch is no longer optimal and the handling properties deteriorate. Furthermore fuel consumption increases and the lifetime of the tires decreases. Therefore it is very important that the tires are correctly inflated.We here focus on an indirect tire pressure monitoring system, where no pressure sensors are needed. The system is based on vibration and wheel radius analysis. These two approaches are combined for optimal performance concerning sensitivity to detect pressure losses and robustness to different driving conditions. When these two approaches are combined, it is possible to detect pressure losses larger than 15% in one, two, three, or four (diffusion) tires within 1 minute. It is also possible to detect which of the tires that are underinflated.

Virtual Sensors of Tire Pressure and Road Friction

The idea of a virtual sensor is to extract information of parameters that cannot be measured directly, or at least would require very costly sensors, by only using available information. Virtual se ...

Sensor Fusion for Accurate Computation of Yaw Rate and Absolute Velocity

In the presented sensor fusion approach, centralized filtering of related sensor signals is used to improve and correct low price sensor measurements. From this, we compute high-quality state information as drift-free yaw rate and exact velocity (accounting for unknown tire radius and slipping wheels on 4WD vehicles). The basic tool here is a Kalman filter supported by change detection for sensor diagnosis. Results and experience of real-time implementations are presented.

Event based sampling with application to vibration analysis in pneumatic tires

Event-based sampling occurs when the time instants are measured everytime the amplitude passes certain pre-defined levels. This is in contrast with classical signal processing where the amplitude is measured at regular time intervals. The signal processing problem is to separate the signal component from noise in both amplitude and time domains. Event-based sampling occurs in a variety of applications. The purpose here is to explain the new types of signal processing problems that occur, and identify the need for processing in both the time and event domains. We focus on rotating axles, where amplitude disturbances are caused by vibrations and time disturbances from measurement equipment. As one application, we examine tire pressure monitoring in cars where suppression of time disturbance is of utmost importance.

Low Tire Pressure Warning System Using Sensor Fusion

Sensor fusion is a signal processing discipline wherein one tries to merge – or fuse – sensor data information coming from different physical sensors. In this paper we illustrate how sensor fusion can be used to design a low tire pressure warning system using existing sensors on a modern vehicle. Results from real-world tests on a car are given as illustration of the method’s feasibility.

Virtual Sensors for Vehicle Dynamics Applications

This paper discusses sensor fusion as a means to compute virtual sensor signals for certain vehicle attitude quantities, in particular vehicle yaw rate. It is shown how sensor fusion can be used to increase the performance and availability of standard sensors commonly available in a modern car. Test results from tests performed with a real vehicle are presented.