Developing AI/ML Based Predictive Capabilities for a Compression Ignition Engine Using Pseudo Dynamometer Data

Robert Jane, Tae Young Kim, Samantha Rose, Emily Glass, Emilee Mossman and Corey James ()
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Robert Jane: DEVCOM Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783, USA
Tae Young Kim: Department of Chemistry and Life Science, United States Military Academy, Bldg. 753, West Point, NY 10996, USA
Samantha Rose: Department of Chemistry and Life Science, United States Military Academy, Bldg. 753, West Point, NY 10996, USA
Emily Glass: Department of Chemistry and Life Science, United States Military Academy, Bldg. 753, West Point, NY 10996, USA
Emilee Mossman: Department of Chemistry and Life Science, United States Military Academy, Bldg. 753, West Point, NY 10996, USA
Corey James: Department of Chemistry and Life Science, United States Military Academy, Bldg. 753, West Point, NY 10996, USA

Energies, 2022, vol. 15, issue 21, 1-49

Abstract: Energy and power demands for military operations continue to rise as autonomous air, land, and sea platforms are developed and deployed with increasingly energetic weapon systems. The primary limiting capability hindering full integration of such systems is the need to effectively and efficiently manage, generate, and transmit energy across the battlefield. Energy efficiency is primarily dictated by the number of dissimilar energy conversion processes in the system. After combustion, a Compression Ignition (CI) engine must periodically continue to inject fuel to produce mechanical energy, simultaneously generating thermal, acoustic, and fluid energy (in the form of unburnt hydrocarbons, engine coolant, and engine oil). In this paper, we present multiple sets of Shallow Artificial Neural Networks (SANNs), Convolutional Neural Network (CNNs), and K-th Nearest Neighbor (KNN) classifiers, capable of approximating the in-cylinder conditions and informing future optimization and control efforts. The neural networks provide outstanding predictive capabilities of the variables of interest and improve understanding of the energy and power management of a CI engine, leading to improved awareness, efficiency, and resilience at the device and system level.

Keywords: energy; efficiency; modeling; machine learning; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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