Time–Frequency Domain Signal Analysis for Knock Detection in Hydrogen-Fueled Engines.
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| Title: | Time–Frequency Domain Signal Analysis for Knock Detection in Hydrogen-Fueled Engines. |
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| Authors: | Kinkhabwala, Brijesh1 (AUTHOR) brijesh.kinkhabwala@kit.edu, Wagner, Uwe1 (AUTHOR), Koch, Thomas1 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2714. 22p. |
| Subject Terms: | *Time-frequency analysis, *Spectrum analysis, *Internal combustion engines, *Automobile engine performance, *Combustion, *Spark ignition engines |
| Abstract: | Hydrogen is a promising carbon-neutral fuel for future internal combustion engines due to its wide flammability range, high flame speed, and absence of carbon-based emissions. However, its high reactivity significantly increases susceptibility to abnormal combustion phenomena such as knock and pre-ignition, which can compromise engine efficiency, durability, and operational stability. Accurate detection and characterization of knock in hydrogen-fueled spark-ignition engines remain challenging due to the highly transient, broadband, and cycle-dependent nature of abnormal combustion-induced pressure oscillations. Conventional knock indicators based solely on time-domain pressure oscillations or fixed-band frequency analysis are limited in their ability to capture transient resonance behavior and cyclic variability. This study presents an integrated frequency- and time–frequency-domain methodology for knock detection using high-resolution in-cylinder pressure data acquired from a single-cylinder research engine operating under hydrogen port fuel injection (PFI). A discrete Fast Fourier Transform (DFFT) approach applied at stationary points of dynamically windowed pressure signals enables accurate identification of dominant resonance modes while minimizing spectral leakage. A Gaussian-based adaptive windowing strategy is introduced to capture combustion-driven cyclic variations more effectively. Short-Time Fourier Transform (STFT) and sum-based spectral analysis further provide detailed time–frequency localization of transient knock events. The proposed methodology demonstrates a clear separation between normal combustion and knock conditions, enabling reliable cycle-by-cycle identification of abnormal combustion events under varying operating conditions. The experimentally observed resonance frequencies are validated against theoretical predictions using Draper's acoustic resonance equation, supporting the physical interpretation of knock-induced pressure oscillations. The results demonstrate that the proposed adaptive spectral methodology significantly improves knock detection accuracy compared to conventional indicators and provides a robust framework for advanced knock diagnostics, engine calibration, and combustion control in hydrogen-fueled engines. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Hydrogen is a promising carbon-neutral fuel for future internal combustion engines due to its wide flammability range, high flame speed, and absence of carbon-based emissions. However, its high reactivity significantly increases susceptibility to abnormal combustion phenomena such as knock and pre-ignition, which can compromise engine efficiency, durability, and operational stability. Accurate detection and characterization of knock in hydrogen-fueled spark-ignition engines remain challenging due to the highly transient, broadband, and cycle-dependent nature of abnormal combustion-induced pressure oscillations. Conventional knock indicators based solely on time-domain pressure oscillations or fixed-band frequency analysis are limited in their ability to capture transient resonance behavior and cyclic variability. This study presents an integrated frequency- and time–frequency-domain methodology for knock detection using high-resolution in-cylinder pressure data acquired from a single-cylinder research engine operating under hydrogen port fuel injection (PFI). A discrete Fast Fourier Transform (DFFT) approach applied at stationary points of dynamically windowed pressure signals enables accurate identification of dominant resonance modes while minimizing spectral leakage. A Gaussian-based adaptive windowing strategy is introduced to capture combustion-driven cyclic variations more effectively. Short-Time Fourier Transform (STFT) and sum-based spectral analysis further provide detailed time–frequency localization of transient knock events. The proposed methodology demonstrates a clear separation between normal combustion and knock conditions, enabling reliable cycle-by-cycle identification of abnormal combustion events under varying operating conditions. The experimentally observed resonance frequencies are validated against theoretical predictions using Draper's acoustic resonance equation, supporting the physical interpretation of knock-induced pressure oscillations. The results demonstrate that the proposed adaptive spectral methodology significantly improves knock detection accuracy compared to conventional indicators and provides a robust framework for advanced knock diagnostics, engine calibration, and combustion control in hydrogen-fueled engines. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19112714 |
