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This document summarizes a study on estimating the state of health (SoH) of lithium-ion batteries used in autonomous underwater vehicles (AUVs) using machine learning algorithms. The performance of AUV batteries is affected by temperature variations in underwater environments. The study compares linear regression, polynomial regression, random forest regression, and LSTM networks to predict battery SoH based on temperature data. The goal is to determine the most suitable algorithm for SoH estimation to help

Author

monaim bensabuer

Language

EN