COMPREDICT’s purely CAN-based Virtual Sensors

Rudolf Kraft
Apr 8 · 7 min read

Virtual sensors

Sensors are units, that measure physical quantities and transform them into — mostly electrical — signals. Nowadays, sensors are omnipresent in nearly every electronic or mechatronic device we use, from smartphones to TVs going through cars. Sensor signals are used to monitor system states, detect changes in systems and/or control them (like a temperature regulation for an air conditioning system).

  • Limited installation space
  • Unsuitable environment conditions (temperature, chemical conditions, etc.)
  • Wear and/or non-reliability of the sensor
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Figure 1: Left: Classic hardware sensors for two physical quantities, Right: hardware sensor B is replaced with a virtual sensor
  • Virtual sensors do not wear.
  • There is no limitation for environment conditions.
  • They neither require space nor increase the weight of your component or system.

Purely CAN-based sensor fusion

As you have probably already understood, to build a virtual sensor you also need hardware sensors, which are at other places measuring other quantities than the one you need. The good news is that, in most of the cases, you have hardware sensors anyway. Let’s take a look at the situation in the automotive field, which is our core competence area: a modern vehicle is similar to a driving computer. It contains electronic control units (ECUs) for engine, steering, brakes and much more. All these control units require sensor signals to monitor the current state of the vehicle components and take appropriate actions. This means, that there are standardly available sensors in a vehicle, which are installed by default, i.e. independently from the usage of virtual sensors. All these sensor signals are usually transmitted on the so-called CAN bus. CAN is the abbreviation for Controller Area Network and describes the most widespread communication protocol, that is used for signal transmission and communication between control units in a vehicle. Our virtual sensors take part of these signals as inputs to calculate physical quantities for almost all vehicle components (see Figure 2). For our customers this results in minimum effort: no additional sensors, no additional hardware, no specific space requirements, only lines of code. Comparison data coming from a dedicated sensor is only needed during the training phase of the virtual sensors, which are usually measured during development in test vehicles (i.e. measurements of the target signal to calibrate and validate the virtual sensor). We are also currently working intensively on limiting the need for comparison data and are leading research on topics like transfer learning. In any case, the most important thing is that, in operation, no additional physical sensor is required anymore: once the virtual sensor is trained, it is robust and accurate enough to be implemented in every single vehicle from the same type.

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Figure 2: COMPREDICTs virtual sensors, in scopes: example of used CAN signals, in red: target signal
  1. modeling of the dependencies between these CAN signals and the target signal

Implementation

The virtual sensor can be used both offline and online (see Figure 3). Offline means, that the CAN signals are gathered and saved in a database on a server, and then these data are used to calculate the target signals for further analysis. Online means, that the estimation of the target signal is performed embedded, on a vehicle’s Electronic Control Unit (ECU) and in real-time. The advantage of this approach is that the signal can directly be used to take actions during the vehicle operation or share on the CAN bus to other ECUs. On the other side, running the virtual sensor directly on the ECU requires a particular attention to memory and efficiency, and thus limits the use of certain modeling approaches.

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Figure 3: Offline vs online virtual sensor

Example

Let’s take a look at an application example. One interesting physical quantity in automotive vehicles is the dynamic side shaft torque. It can be used not only to estimate the damage and thus the remaining service life of the side shafts and its subcomponents like the joints, but also of drivetrain systems like the gearbox or the tires. Furthermore, it is a useful information for traction and stability control. Unfortunately, measuring the side shaft torque with a dedicated sensor is not that easy. Usually, adhesive strain gauges are applied on the surface of the shafts, but since the shafts rotate during operation, special methods for signal transmission must be considered. Moreover, side shafts can easily come into contact with water, snow, dirt and particles which can damage the strain gauges. This results in expensive, sensitive and unreliable measuring systems that are not suited for series production.

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Figure 4: Comparison of a hardware sensor and a virtual sensor for the side shaft torque of a Tesla Model 3

COMPREDICT

AI-based virtual sensor and data analytics platform for…

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