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

Jaehoon Lee, Sang-Kyun Park (), Salim Abdullah Bazher and Daewon Seo ()
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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

Energies, 2025, vol. 18, issue 7, 1-20

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.

Keywords: marine propulsion; energy storage system; lithium-ion battery; battery management system; fault prediction technology (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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