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

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  • Robert Jane

    (DEVCOM Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783, USA
    Department of Chemistry and Life Science, United States Military Academy, Bldg. 753, West Point, NY 10996, USA
    These authors contributed equally to this work.)

  • 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
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Robert Jane & Tae Young Kim & Samantha Rose & Emily Glass & Emilee Mossman & Corey James, 2022. "Developing AI/ML Based Predictive Capabilities for a Compression Ignition Engine Using Pseudo Dynamometer Data," Energies, MDPI, vol. 15(21), pages 1-49, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8035-:d:956634
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    References listed on IDEAS

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    1. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    2. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    3. Robert Jane & Tae Young Kim & Emily Glass & Emilee Mossman & Corey James, 2021. "Tailoring Mission Effectiveness and Efficiency of a Ground Vehicle Using Exergy-Based Model Predictive Control (MPC)," Energies, MDPI, vol. 14(19), pages 1-39, September.
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