Browse Publications Technical Papers 2021-01-0424
2021-04-06

Proof of Concept for Hardware-in-the-Loop Based Knock Detection Calibration 2021-01-0424

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point.
Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.
This article presents a black box calibration approach for knock detection with minimal software documentation where the Engine Control Unit is operated on a Hardware-in-the-Loop rig. For this specific approach, a playback of the recorded engine knock traces is fed sequentially into the Engine Control Unit. To achieve fast runtimes, an automated Hardware-in-the-Loop environment is implemented with the help of MATLAB, ETAS INCA-MIP and IPG RealtimeMaker. This setup allows to evaluate variations of knock window lengths, knock window start angles and filter combinations. This paper describes a method to calibrate the Engine Control Unit’s function, without detailed architectural knowledge of the software, to accurately detect the knocking combustion cycles.
The validation of the calibration from the introduced Hardware-in-the-Loop method reveals an acceptable accuracy (<5% error) and is therefore suitable for knock detection. This method significantly decreases the required engine test-bench time and human effort in comparison to conventional approaches and is better suited than the model-based approach in some cases.

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