Mechanical energy scavenging for in-wheel sensors : car wheel motion and data

Car wheel motion  - go straight to data

The performance of all in-wheel energy harvesters is reliant on the rotation and vibration motion of the car wheel s the energy source input. Harvester power output will vary significantly with car speed and the road surface the wheel is travelling over. To

aid in the modelling of the performance and to assess the viability of the clock work energy harvester then the car wheel dynamics of a typical small car have been measured. Wheel rotation speed has been measured using a hall sensor and attaching twelve metal disks to the inside of the wheel rim, fig 1a. A bracket was designed, fig 1b, to allow the hall sensor to be attached to the shock absorber and positioned inside the wheel rim close to the metal disks, Fig 1c.
Wheel speed measurement system

Figure 1. Wheel speed measurement system (a) disks on wheel rim, (b) hall sensor bracket design, (c) hall sensor mounted on shock absorber.  

Although it was not possible to directly measure the vibration of the wheel rim where the TPMS system would sit, it can be inferred from the motion of the axel as the wheel is robustly attached to the axel. A DJB 100mV/g A/130/V Tri-axis Piezoelectric Accelerometer was used to measure the vibration. It was housed in a die cast box to protect it from the elements (fig 2a) and the box attached to the car axel swing arm (fig 2b). The accelerometer was mounted on a wedge in the box to ensure that it was level when the car was stationary. 

Figure 2. (a) tri-axial accelerometer mounted in die cast aluminium box; (b) accelerometer box attached to front swing arm.

The output signals from the tri-axial accelerometer and also the hall sensor were monitored using a SoMat eDAQ-lite mobile data acquisition unit, Fig 3a. The eDAQ-lite was fitted with a digital IO and a simultaneous high level analogue layer.

The digital IO layer also allowed input from a GPS sensor, Fig 3b, so that the car position could be mapped and wheel motion could be related to urban or extra urban road conditions. The accelerometer output was measured at 2khz sampling rate with a ±12g range. The hall sensor was sampled at 500Hz using a pulse counter input on the I/O layer. The GPS reported position at a fixed 5Hz rate.

 Figure 3. (a) SoMat eDAQ-lite data acquisition system attached to sensors

Figure 3. (a) SoMat eDAQ-lite data acquisition system attached to sensors; (b) GPS 18x 5HzTM receivers from Garmin magnetically attached to car roof.

Data - route 1

Experimental details:
Car: 2005 Ford Fiesta 1.25L Zetec; Front tyre: 95/50 R15 82V; Radius: 29cm

  • Accelerometer: DJB instruments A/130/V ICP; sensitivity: 100mV/g; Range: ±12g; sampling: 2khz
  • Hall sensor: Honeywell 1GT101DC; sampling: 500Hz
  • GPS: Garmin GPS 18x 5HzTM

Route: The car was driven from the University of Birmingham in a circuitous route through Edgbaston and Harborne, back to join the A38 through Selly Oak before returning to the University campus, Figure 1. The speed limit on all roads was 30mph. The car was stopped in a traffic jam in Selly Oak towards the end of the journey. The whole journey lasted about 16mins.
Figure 1: Anticlockwise route from university campus through Edgbaston, Harborne and Selly Oak

Figure 1: Anticlockwise route from university campus through Edgbaston, Harborne and Selly Oak.

Data files: The sensor data is stored in separate zip files dependent on the data acquisition rate. Start and end times are identical for all the data files.

Data Analysis

Figure 2 shows the output from the hall sensor. The first plot is the raw frequency data produced by the 12 metal discs on the wheel rim passing the hall sensor. The second plot shows the calculated car speed based on a 29cm radius tyre. There are 11 stop start motions in this 16min urban journey.

Figure 2: Wheel speed data

 

Figure 2: Wheel speed data; (a) pulse frequency from hall sensor, (b) calculated wheel speed (tyre radius =29cm).

Figure 3 shows the acceleration amplitude for the x,y,z directions during the course of the journey. The Z data exhibits the highest accelerations as expected. X accelerations are significantly lower than the Y and Z direction accelerations.
Figure 3: Measured acceleration amplitude vs. time

Figure 3: Measured acceleration amplitude vs. time for the x,y,z accelerometers.

The FFT of the accelerations has been calculated to determine the spectral power of the vibrations. Figure 4 shows the spectral power for the X, Y, Z accelerations. The Z (vertical ) direction FFT shows that the vibration power is all concentrated in a small band around 20Hz and is 5x higher than the Y direction power and 50x higher than the X direction power. This vibration frequency is associated with the natural frequency of the car suspension. The Y power is spread over a wider band with a second peak at ~50Hz. The X acceleration power is spread over the whole range out to 800Hz.

Figure 4: FFT power spectrum

Figure 4: FFT power spectrum for the X, Y, Z direction accelerations.

Wheel speed data allows the accelerations to be matched to the speed of the car and also provides information for in wheel rotational energy harvesters.