IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4791-d851812.html
   My bibliography  Save this article

Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles

Author

Listed:
  • Péter Földesi

    (Department of Logistics and Forwarding, Széchenyi István University, 9026 Győr, Hungary)

  • László T. Kóczy

    (Department of Logistics and Forwarding, Széchenyi István University, 9026 Győr, Hungary)

  • Ferenc Szauter

    (Department of Logistics and Forwarding, Széchenyi István University, 9026 Győr, Hungary)

  • Dániel Csikor

    (Department of Logistics and Forwarding, Széchenyi István University, 9026 Győr, Hungary)

  • Szabolcs Kocsis Szürke

    (Department of Logistics and Forwarding, Széchenyi István University, 9026 Győr, Hungary)

Abstract

Hybrid vehicles are gaining increasing global prominence, especially in the military, where unexpected breakdowns or even power deficits are not only associated with greater expense but can also cost the lives of military personnel. In some cases, it is extremely important that all battery cells and modules deliver the specified amount of capacity. Therefore, it is recommended to introduce a new measurement line of rapid diagnostics before deployment, in addition to the usual procedures. Using the results of rapid testing, we recommend the introduction of a hierarchical three-step diagnostics and assessment procedure. In this procedure, the key factor is the building up of a hierarchical tree-structured fuzzy signature that expresses the partial interdependence or redundancy of the uncertain descriptors obtained from the rapid tests. The fuzzy signature structure has two main important components: the tree structure itself, and the aggregations assigned to the internal nodes. The fuzzy signatures that are thus determined synthesize the results from the regular maintenance data, as well as the effects of the previous operating conditions and the actual state of the battery under examination; a signature that is established this way can be evaluated by “executing the instructions” coded into the aggregations. Based on the single fuzzy membership degree calculated for the root of the signature, an overall decision can be made concerning the general condition of the batteries.

Suggested Citation

  • Péter Földesi & László T. Kóczy & Ferenc Szauter & Dániel Csikor & Szabolcs Kocsis Szürke, 2022. "Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles," Energies, MDPI, vol. 15(13), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4791-:d:851812
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4791/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4791/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Marcos & Maitane Garmendia & Jon Crego & José Antonio Cortajarena, 2021. "Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries," Energies, MDPI, vol. 14(21), pages 1-19, October.
    2. Pedram Asef & Marzia Milan & Andrew Lapthorn & Sanjeevikumar Padmanaban, 2021. "Future Trends and Aging Analysis of Battery Energy Storage Systems for Electric Vehicles," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    3. Jaime de la Peña Llerandi & Carlos Sancho de Mingo & José Carpio Ibáñez, 2019. "Continuous Battery Health Diagnosis by On-Line Internal Resistance Measuring," Energies, MDPI, vol. 12(14), pages 1-13, July.
    4. Lidiya Komsiyska & Tobias Buchberger & Simon Diehl & Moritz Ehrensberger & Christian Hanzl & Christoph Hartmann & Markus Hölzle & Jan Kleiner & Meinert Lewerenz & Bernhard Liebhart & Michael Schmid & , 2021. "Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles," Energies, MDPI, vol. 14(18), pages 1-82, September.
    5. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    7. Yi Wu & Saurabh Saxena & Yinjiao Xing & Youren Wang & Chuan Li & Winco K. C. Yung & Michael Pecht, 2018. "Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography," Energies, MDPI, vol. 11(4), pages 1-22, April.
    8. Ming-Hui Chang & Han-Pang Huang & Shu-Wei Chang, 2013. "A New State of Charge Estimation Method for LiFePO 4 Battery Packs Used in Robots," Energies, MDPI, vol. 6(4), pages 1-24, April.
    9. Xinglong Liu & Fuquan Zhao & Han Hao & Kangda Chen & Zongwei Liu & Hassan Babiker & Amer Ahmad Amer, 2020. "From NEDC to WLTP: Effect on the Energy Consumption, NEV Credits, and Subsidies Policies of PHEV in the Chinese Market," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    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. Saleh Mohammed Shahriar & Erphan A. Bhuiyan & Md. Nahiduzzaman & Mominul Ahsan & Julfikar Haider, 2022. "State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence," Energies, MDPI, vol. 15(21), pages 1-26, October.
    2. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    3. Sandoval, Cinda & Alvarado, Victor M. & Carmona, Jean-Claude & Lopez Lopez, Guadalupe & Gomez-Aguilar, J.F., 2017. "Energy management control strategy to improve the FC/SC dynamic behavior on hybrid electric vehicles: A frequency based distribution," Renewable Energy, Elsevier, vol. 105(C), pages 407-418.
    4. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.
    5. Qingshan Gong & Yurong Xiong & Zhigang Jiang & Xugang Zhang & Mingmao Hu & Zhanlong Cao, 2022. "Economic, Environmental and Social Benefits Analysis of Remanufacturing Strategies for Used Products," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    6. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    7. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    8. F. Isorna Llerena & E. López González & J. J. Caparrós Mancera & F. Segura Manzano & J. M. Andújar, 2021. "Hydrogen vs. Battery-Based Propulsion Systems in Unipersonal Vehicles—Developing Solutions to Improve the Sustainability of Urban Mobility," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    9. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    10. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
    11. Kangda Chen & Fuquan Zhao & Xinglong Liu & Han Hao & Zongwei Liu, 2021. "Impacts of the New Worldwide Light-Duty Test Procedure on Technology Effectiveness and China’s Passenger Vehicle Fuel Consumption Regulations," IJERPH, MDPI, vol. 18(6), pages 1-20, March.
    12. Thomas F. Landinger & Guenter Schwarzberger & Guenter Hofer & Matthias Rose & Andreas Jossen, 2021. "Power Line Communications for Automotive High Voltage Battery Systems: Channel Modeling and Coexistence Study with Battery Monitoring," Energies, MDPI, vol. 14(7), pages 1-26, March.
    13. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    14. Ashikur Rahman & Xianke Lin & Chongming Wang, 2022. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer," Energies, MDPI, vol. 15(15), pages 1-19, August.
    15. Bingtao Hu & Yixiong Feng & Hao Zheng & Jianrong Tan, 2018. "Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and Improved Ant Colony Optimization Algorithm," Energies, MDPI, vol. 11(8), pages 1-18, August.
    16. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
    17. Nimalsiri, Nanduni I. & Ratnam, Elizabeth L. & Mediwaththe, Chathurika P. & Smith, David B. & Halgamuge, Saman K., 2021. "Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit," Applied Energy, Elsevier, vol. 291(C).
    18. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach," Sustainability, MDPI, vol. 13(23), pages 1-25, December.
    19. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    20. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).

    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:13:p:4791-:d:851812. 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.