IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i7p1687-d1622332.html
   My bibliography  Save this article

Development of a Fault Prediction Algorithm for Marine Propulsion Energy Storage System

Author

Listed:
  • Jaehoon Lee

    (Alternative Fuel Technology Research Team, Korean Register, Busan 46762, Republic of Korea)

  • Sang-Kyun Park

    (Division of Maritime AI & Cyber Security, Korea Maritime and Ocean University, Busan 49112, Republic of Korea)

  • Salim Abdullah Bazher

    (Department of Naval Architecture and Ocean Engineering, Kunsan National University, Gunsan 54151, Republic of Korea)

  • Daewon Seo

    (Department of Naval Architecture and Ocean Engineering, Kunsan National University, Gunsan 54151, Republic of Korea)

Abstract

The transition to environmentally sustainable maritime operations has gained urgency with the International Maritime Organization’s (IMO) 2023 GHG reduction strategy, aiming for net-zero emissions by 2050. While alternative fuels like LNG and methanol serve as transitional solutions, lithium-ion battery energy storage systems (ESSs) offer a viable low-emission alternative. However, safety concerns such as thermal runaway, overcharging, and internal faults pose significant risks to marine battery systems. This study presents an AI-based fault prediction algorithm to enhance the safety and reliability of lithium-ion battery systems used in electric propulsion ships. The research employs a Long Short-Term Memory (LSTM)-based predictive model, integrating electrochemical impedance spectroscopy (EIS) data and voltage deviation analyses to identify failure patterns. Bayesian optimization is applied to fine-tune hyperparameters, ensuring high predictive accuracy. Additionally, a recursive multi-step prediction model is developed to anticipate long-term battery performance trends. The proposed algorithm effectively detects voltage deviations and pre-emptively predicts battery failures, mitigating fire hazards and ensuring operational stability. The findings support the development of safer and more reliable energy storage solutions, contributing to the broader adoption of electric propulsion in maritime applications.

Suggested Citation

  • Jaehoon Lee & Sang-Kyun Park & Salim Abdullah Bazher & Daewon Seo, 2025. "Development of a Fault Prediction Algorithm for Marine Propulsion Energy Storage System," Energies, MDPI, vol. 18(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1687-:d:1622332
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/7/1687/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/7/1687/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ren, Dongsheng & Feng, Xuning & Lu, Languang & He, Xiangming & Ouyang, Minggao, 2019. "Overcharge behaviors and failure mechanism of lithium-ion batteries under different test conditions," Applied Energy, Elsevier, vol. 250(C), pages 323-332.
    2. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    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. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
    2. Ranjit Kumar Paul & Md Yeasin & C. Tamilselvi & A. K. Paul & Purushottam Sharma & Pratap S. Birthal, 2025. "Can Deep Learning Models Enhance the Accuracy of Agricultural Price Forecasting? Insights From India," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 32(1), March.
    3. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    4. Jaydip Sen & Sidra Mehtab & Abhishek Dutta & Saikat Mondal, 2022. "Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model," Papers 2203.01326, arXiv.org.
    5. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    6. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    7. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    8. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    9. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    10. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    11. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    12. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    13. Sangseop Lim & Chang-hee Lee & Won-Ju Lee & Junghwan Choi & Dongho Jung & Younghun Jeon, 2022. "Valuation of the Extension Option in Time Charter Contracts in the LNG Market," Energies, MDPI, vol. 15(18), pages 1-14, September.
    14. Doğan, Murat & Sayılır, Özlem & Komath, Muhammed Aslam Chelery & Çimen, Emre, 2025. "Prediction of market value of firms with corporate sustainability performance data using machine learning models," Finance Research Letters, Elsevier, vol. 77(C).
    15. Bontempi, Gianluca & Ben Taieb, Souhaib, 2011. "Conditionally dependent strategies for multiple-step-ahead prediction in local learning," International Journal of Forecasting, Elsevier, vol. 27(3), pages 689-699, July.
    16. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    17. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.
    18. Antoine Proteau & Antoine Tahan & Ryad Zemouri & Marc Thomas, 2023. "Predicting the quality of a machined workpiece with a variational autoencoder approach," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 719-737, February.
    19. Yang Qiao & Yiping Xia & Xiang Li & Zheng Li & Yan Ge, 2023. "Higher-order Graph Attention Network for Stock Selection with Joint Analysis," Papers 2306.15526, arXiv.org.
    20. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:7:p:1687-:d:1622332. 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.