Capacity Estimation of Lithium-Ion Battery Systems in Fuel Cell Ships Based on Deep Learning Model

Abstract

The capacity estimation of lithium-ion batteries, serving as an auxiliary power source in fuel cell vessels, is crucial for ensuring system stability and enhancing operational efficiency. Accurate capacity estimation technology not only helps extend battery lifespan but also enhances the energy management and scheduling capabilities of the entire vessel. To address the challenge of accurately estimating lithium-ion battery capacity under complex operating conditions, this study extracts universal health factors from battery data under varied charging and discharging scenarios and combines these with a deep learning model to enhance prediction accuracy. First, battery data from three complex conditions are analyzed, extracting partial charge and discharge data. The distance correlation coefficient calculates the correlation between each factor and the capacity sequence, informing the priority of universal health factors. A TCN-BiGRU model is then developed, with hyperparameters determined by the Kepler optimization algorithm (KOA). Cells from a battery pack under consistent conditions are used for training, while other cells in the same pack serve as the test set. Evaluation metrics include mean absolute error (MAE) and root-mean-square error (RMSE). The testing shows that the MAE and RMSE for full-life capacity estimation remain around 1%, with most cells achieving values under 1%. The results indicate that the proposed method effectively aids in accurate capacity estimation for individual cells in complex operating environments.

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