A Pipeline for Lithium-Ion Battery Data Integration and Remaining Useful Life Estimation

Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries using data-driven models requires large amounts of clean, diverse data. However, publicly available datasets are often collected under varying and limited testing conditions, which poses challenges for model training and generalization. To address this, we present a pipeline for cleaning, transforming, and integrating two distinct datasets covering four battery specifications and a wide range of test scenarios. We apply filtering and normalization techniques to address noise, outliers, and inconsistencies, and organize the cleaned data into a relational database. Building on this integrated dataset, we train a Conv-LSTM neural network and evaluate its performance against models trained on individual battery groups. Our results show that multisource training improves model generalization, particularly for smaller or more heterogeneous datasets, and can help enhance prediction accuracy in the later stages of battery life