Battery remaining useful life estimation process design in hybrid ships: a case of data-driven algorithms

Hybrid propulsion systems increase ship energy efficiency by allowing the sharing of power between diesel engines and battery energy storage systems. However, the long-term efficiency of these types of systems depends on accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries to allow effective charge scheduling, maintenance planning, and reliable navigation. This study uses nine data-driven algorithms, including ensemble methods, recurrent neural networks, and linear models, to examine the RUL of a lithium-ion battery pack installed on a hybrid cargo ship. A 5-fold cross-validation structure was used to preprocess, normalize, and analyze actual operational data gathered during the vessel's service life. To improve the accuracy of predictions, hyperparameter optimization was performed out. Long Short-Term Memory (LSTM), which reduced MAE from 2.87 to 1.46 and RMSE from 12.57 to 6.34 after optimization while retaining a high coefficient of determination (R² = 0.9999), performed the best among the models that were evaluated. The results obtained indicate that condition-based maintenance and energy utilization methods on hybrid ships can be effectively supported by data-driven RUL estimation. In order to enhance generalization and assess integration with real-time propulsion control systems, future research will expand the analysis to multi-vessel datasets.