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Monday, 15 July 2019 21:18

Use of Kalmanfilter, MEMS Motion Tracking Sensor and ToF sensor to estimate damper displacement

My brake disk temperature sensor is also equipped with a MEMS Motion Tracking Sensor, measuring rotational speed and acceleration in all three axes. Together with a Time-of-Flight (ToF) distance measurement sensor placed on the damper, it will be possible to do analysis on the damper system.

The MEMS sensor can measure at 8000 Hz, while the ToF only at 50Hz. Unfortunately the tolerance of the measurements are not that good. The measurement of a distance of 200mm are typically within +/-20mm, the speed within +/- 0.1 m/s and the acceleration typically within +/-0.2g.

However, the laws of physics when it comes to the relationship between displacement, speed and acceleration, are fairly good known. The laws describes a digital twin of the physical system which can be utilized by estimation techniques as e.g. a Kalmanfilter. By combining the Kalmanfilter, the measurements and a lot of experience with such systems, it seems feasible to achieve estimates of the damper positions within +/- 1mm.  Simulations on a Spring-Damper model seems promising...

In the simulations, shown in Figure 1 and 2  below, it is assumed a car of 1480kg, mass/spring/damper system of 80kg and a suitable applied force. The damper/spring system is assumed KW V3 with spring constant of ~60N/mm. The damping constant is D=4Ns/mm for the close to critical damped system and D=1Ns/mm for the underdamped system.

The MEMS measurement is at 1000Hz and the ToF at 50Hz. The Kalmanfilter is implemented to deal with measurements of differet sampling frequencies.

Figure 1: Critical damped system

 

Figure 2: Underdamped system

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