Use an oscilloscope to create a chain of measurement parameters and math operators for analyzing SENT sensor information.
Modern automotive systems use serial data transmissions to enable the rapid transfer of information. Single edge nibble transmission (SENT) bus, a serial data standard managed by SAE International, is a lower-cost alternative to both CAN and LIN standards. SENT is gaining popularity for transiting high resolution data between automotive sensors and controllers; it provides digital readings of temperature, pressure, throttle position, and mass air flow.
This article outlines new techniques for analyzing SENT sensor information using oscilloscopes. Using a chain of measurement parameters and math operators in a novel way, a throttle body sensor and differential pressure sensor are analyzed to reveal chronological changes in real-time.
SENT for differential pressure sensors
A differential pressure sensor serving automotive applications is shown in Figure 1. This sensor can be used, for example, to measure the differential pressure at the gasoline particulate filter and output the pressure result as a digital data stream. The yellow and grey wires supply power to the active circuitry of the sensor, while the green wire outputs SENT serial data, which encodes the differential pressure values.
Figure 1 The green wire contains digital SENT data that is probed by the oscilloscope. Source: Teledyne LeCroy
When testing the output of the sensor, an automotive engineer’s first task is to make sense of the digitally-encoded pressure data. The captured SENT messages are contained in the yellow waveform located in the upper-left grid of Figure 2. With SENT decoding enabled, the individual fast channel SENT words are extracted and listed in the decode table (Figure 2, lower middle).
Figure 2 The SENT bus digital data is shown in the top left, while measurements to extract the decoded values are displayed in the middle table. The bottom of the image shows parameter rescale coefficients, and a graph of the rescaled differential pressure results is exhibited in the top right. Source: Teledyne LeCroy
Since the pressure sensor readings are distributed across three different encoded data fields, the digital pressure values must be recombined in order to determine the composite analog pressure value. In the measurement table (Figure 2 upper middle), measurement parameters P1, P2, and P3 extract the digital values from data fields D0, D1, and D2. To recombine the hexadecimal pressure data into a single decimal result, parameter measurement rescale operators P4 and P5 are used to multiply P1 and P2 by 256 and 16, respectively. Parameter P6 outputs the composite pressure in units of kilopascals.
Since the differential pressure varies as a function of time and each pressure value computed in P6 is a static value comprising a dynamic event, it is useful to graph the computed results to determine how differential pressure varies over time. A track math operator (Figure 2, top right, green) graphs P6 to reveal the shape of the differential pressure values over time, with peaks and troughs, followed by a return to baseline pressure.
Longer chains of math and measurement operators needed for complex computations can often be simplified by using graphical programming. Figure 3 shows the measurement chain used to recombine and rescale the SENT data results of parameter P6. Each stage in the chain is configured, and Figure 3 shows the final slope-intercept equation and “output units” field selected, whereby the output units are selected as kilopascals (units of pressure). The output result of the measurement chain detailed in Figure 3 provides the numerical result shown in Figure 2’s measurement parameter P6.
Figure 3 Graphical connectivity is used to simplify the chain of math and measurement operators used to combine and rescale the data. The selection field is also highlighted to show the overridden unit of kilopascals. Source: Teledyne LeCroy
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SENT for throttle body position sensors
Throttle body position sensors are used to monitor the air intake of an engine. Figure 4 shows the physical connectivity of a SENT throttle body position sensor, in which the position is manually manipulated by a test engineer while an oscilloscope with a scope probe monitors the output. As the throttle position changes, the encoded SENT values reflect the change. A large amount of data is collected in Figure 5 (top left triple grid), with a zoom trace showing a slice of the data on the bottom left triple grid in yellow.
Using a similar technique described earlier for the differential pressure sensor application, data fields corresponding to throttle body position are extracted from the fast channel digital data fields and recombined using measurement parameter values and rescalers, culminating in the P8 operator output. The graphical representation of throttle body position as a function of time is plotted in Figure 5 (right triple grid, red), where the position reaches full rail open-and-closed positions along with many discretized values in between.
Figure 5 The SENT waveform data is shown in yellow on the upper left, while the extracted measurement data is displayed on the middle and lower parts. The graph of the throttle body position over time is exhibited on the top right. Source: Teledyne LeCroy
Detailed analysis of sensor behavior
The detailed analysis of SENT sensor behavior can be characterized by combining waveform protocol decodes, measurement parameters and math operators and graphing tools, allowing for the analysis of complex automotive systems. Note that since multiple protocol decoders can operate simultaneously on one oscilloscope, the analysis of both the differential pressure sensor and the throttle body position sensor can be performed simultaneously on the same oscilloscope.
Also, since the output pressure and position are both displayed as track waveforms, they can be superimposed onto the same grid using the same scaling to reveal correlations between position and pressure. It also shows cause-and-effect relationships with other signals that may be probed using the other available channels. The debug possibilities grow exponentially when adding additional automotive signals into the analysis.
This article was originally published on EDN.
Mike Hertz has been a field applications engineer with Teledyne LeCroy in Michigan for 20 years.
Dave Van Kainen, a founding partner of Superior Measurement Solutions, teaches measurement courses at Teledyne LeCroy.