Real-Time Knock Characterization Using Adaptive Filters and Power Estimators

We combined adaptive filters associated with power estimators to characterize the knock signal, obtained from a knock sensor, in an internal combustion engine. The filters were implemented using the automotive model-based design methodology, and the resulting software was embedded into hardware for...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.84371-84384
Main Authors: Silva, Rafael Luiz Da, Rossetti, Pedro, Santos, Joao Pedro F., Lagana, Armando Antonio Maria, Colon, Diego, Justo, Joao Francisco
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithms
Automobile industry
automotive electronics
Automotive engines
Convergence
Cost function
Engines
Estimators
Fuels
Integrated circuits
Internal combustion engines
Knock
knock sensor
Microprocessors
model-based development
Real time
Real-time systems
Redundancy
Sparks
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Summary:We combined adaptive filters associated with power estimators to characterize the knock signal, obtained from a knock sensor, in an internal combustion engine. The filters were implemented using the automotive model-based design methodology, and the resulting software was embedded into hardware for a real-time evaluation of the proposed solution. The knock signals could be qualitatively identified in real-time, and thus have the potential to aid in the management of flexible-fuel engines. This approach has an extensive range of applications within the automotive industry, since it can be implemented within any model-based control strategy. For example, this methodology can be applied in commercial ECUs, currently used in most vehicles for knock detection, by simply eliminating an internal, dedicated, integrated circuit for knock identification, or by serving as a redundancy device (i.e: for safety purposes). Finally, this methodology can identify the knock signal, obtained from a knock sensor, just by using an algorithm implemented in the ECU's processor, which we show is identifiable in real-time.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2991664