Intelligent tire is a relatively new technology that provides useful tire-road contact information by directly monitoring the interaction between the tire and the road. Different types of sensors are attached to the tire inner-liner for this purpose; the sensor data then will be used to estimate the tire-road contact parameters as well as to monitor the tire conditions.

In this study, a tri-axial accelerometer was used and a two-steps intelligent tire based pressure monitoring algorithm was developed in this study. First, the angular velocity of the wheel was estimated based on the parameters extracted from the acceleration components through a trained neural network. Then the estimated wheel angular velocity from the first step was used along with the acceleration components to estimate the power of radial acceleration. The estimated power was compared to the actual one and the tire pressure condition was judged to be “normal” or “low”. To train the neural networks, the experimental data collected using an instrumented vehicle was used. A VW Jetta 2003 was used for this purpose and instrumented with appropriate sensors; intelligent tires, steering wheel sensor to measure the steering angle, steering velocity and steering torque, encoders to measure the angular speed of the wheels and an Inertial Measurement Unit (IMU) to measure the vehicle linear and angular acceleration.

Another set of experimental data with different tire pressures and different vehicle velocity was then used to validate the algorithm; good agreements were observed between the estimated tire pressures and the actual ones.

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