Automated engine calibration in vehicle to optimize emissions levels using machine learning

Automated engine calibration in vehicle to optimize emissions levels using machine learning

Esteves, Alexandre Tadeu Mencacci; Kawamoto, Alexandre Massayuki; Pelisser, André; Carmelutti, David Gazitto

Artigo completo:

The growing concern with environmental impact is a major drive for the tighter emissions imposed on combustion engines. The compliance with those restrictions is pushing new hardware and software solutions that, nevertheless, increase system complexity le

The growing concern with environmental impact is a major drive for the tighter emissions imposed on combustion engines. The compliance with those restrictions is pushing new hardware and software solutions that, nevertheless, increase system complexity le

Palavras-chave:

DOI: 10.5151/simea2021-PAP32

Referências bibliográficas

• [1] André Pelisser, Alexandre Kawamoto, Wilhelm Vatanabe, Victor Namba. “A Gaussian process model for tires in combined slip case” – SIMEA 2019. São Paulo.
• [2] C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, (Available at http://www.gaussianprocess.org/gpml/chapters/RW.pdf)
• [3] ETAS GmbH. INCA-FLOW Tutorial III - Measurement Data Analysis, 2013.
• [4] ETAS GmbH. ETAS ASCMO Static V5.2 - User’s Guide, 2018.

Como citar:

Esteves, Alexandre Tadeu Mencacci; Kawamoto, Alexandre Massayuki; Pelisser, André; Carmelutti, David Gazitto; "Automated engine calibration in vehicle to optimize emissions levels using machine learning", p-107-120. In: Anais do XXVIII SIMPÓSIO INTERNACIONAL DE ENGENHARIA AUTOMOTIVA. São Paulo: Blucher, 2021.
ISSN 23577592, DOI 10.5151/simea2021-PAP32