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

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 ma...

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Bibliographic Details
Published in:Energies (Basel) 2022-10, Vol.15 (21), p.8035
Main Authors: Jane, Robert, Kim, Tae Young, Rose, Samantha, Glass, Emily, Mossman, Emilee, James, Corey
Format: Article
Language:English
Subjects:
Artificial intelligence
Automobiles
Battlefields
Compression
Control algorithms
Diesel engines
Distance learning
Dynamic programming
Efficiency
energy
Energy conversion
Energy efficiency
Energy management
Engine coolants
Hybrid vehicles
Ignition
Machine learning
Military aspects
Military operations
modeling
Neural networks
Offshore platforms
Online instruction
Optimization
Power management
Powertrain
System effectiveness
Weapon systems
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15218035