| Summary: | 碩士 === 國立屏東科技大學 === 車輛工程系所 === 104 === The topic of this research is to develop a vehicle dynamics estimation and information system. A tiltable vehicle can be tilted its body by driver to create an anti-rollover torque in high speed cornering maneuvers. However, if the driver is not skillful enough, it will take the risk of rollover because of over-tilting operations. Thus a vehicle body posture estimation system could be useful for developing rollover alarming system for the vehicle.
Nowadays, the common used low-cost sensors for estimating vehicle body dynamics are the 9-axes sensor that provide angular speeds, accelerations, and magnetism on the x-, y, and z-axis. This research is focused on utilizing a low-cost nine-axes sensor to develop the vehicle dynamics estimation and information system. In order to apply such a sensor to determine vehicle posture on-line, a series of techniques should be studied to estimate the body angles on the 3 axes, such as the Quaternion, Butterworth filter, and Mahony complementary filter for error modification. The objective is to obtain high accuracy vehicle dynamic posture estimations under high noise conditions.
The main results of this research was a micro-processor-based information system with capability of measuring, estimating and displaying vehicle dynamics information. This system was equipped on a three-wheeled tiltable motorcycle to verify its performance. The test results are as follows.
For the S-pattern drive tests, all the measurement errors showed Gaussian distribution. The roll angle was with an average measurement error of -0.257 and a standard deviation of 1.7063 in degree, while the yaw angle was with an average measurement error of 2.5071 and a standard deviation of 3.1864.
As for the Constant radius cornering drive tests, the measurement errors also showed Gaussian distribution. The roll angle was with an average measurement error of -0.2914 and a standard deviation of 3.8346 in degree, finally, the yaw angle was with an average measurement error of -3.9687 and a standard deviation of 3.1864. all these results have shown the feasibility of the estimating system.
Besides parameter estimating, a UI interface was also established in the controller, which could communicate with cell phones through Bluetooth to display vehicle dynamic posture using posture picture, and storage the recorded data on-line .
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