IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p8035-d956634.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/8035/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/21/8035/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    2. Jiang, Yue & Meng, Hao & Chen, Guanpeng & Yang, Congnan & Xu, Xiaojun & Zhang, Lei & Xu, Haijun, 2022. "Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle," Energy, Elsevier, vol. 254(PA).
    3. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2020. "Comparison of four-wheel-drive hybrid powertrain configurations," Energy, Elsevier, vol. 209(C).
    4. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    5. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    6. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    7. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    8. Daeil Lee & Seoryong Koo & Inseok Jang & Jonghyun Kim, 2022. "Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation," Energies, MDPI, vol. 15(8), pages 1-25, April.
    9. Yang, Dongpo & Liu, Tong & Song, Dafeng & Zhang, Xuanming & Zeng, Xiaohua, 2023. "A real time multi-objective optimization Guided-MPC strategy for power-split hybrid electric bus based on velocity prediction," Energy, Elsevier, vol. 276(C).
    10. Zhengyu Yao & Hwan-Sik Yoon & Yang-Ki Hong, 2023. "Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-18, January.
    11. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    12. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    13. Yao, Yongming & Wang, Jie & Zhou, Zhicong & Li, Hang & Liu, Huiying & Li, Tianyu, 2023. "Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles," Energy, Elsevier, vol. 262(PA).
    14. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    15. Guo, Ningyuan & Zhang, Xudong & Zou, Yuan & Guo, Lingxiong & Du, Guodong, 2021. "Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation," Energy, Elsevier, vol. 214(C).
    16. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Yue, Fenglai, 2023. "A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model," Energy, Elsevier, vol. 265(C).
    17. Liu, Bo & Sun, Chao & Wang, Bo & Liang, Weiqiang & Ren, Qiang & Li, Junqiu & Sun, Fengchun, 2022. "Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections," Energy, Elsevier, vol. 252(C).
    18. Connor Scott & Mominul Ahsan & Alhussein Albarbar, 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings," Sustainability, MDPI, vol. 13(7), pages 1-22, April.
    19. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    20. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8035-:d:956634. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.