Accelerated Aging Test Data for Battery Full Life Cycle Collection

This paper presents collected data from accelerated aging tests for battery full life cycles, representing significant research for battery life prediction and diagnostics. During these experiments, we performed charge-discharge cycles under different temperature conditions and gathered extensive datasets. The data is stored in MATLAB .mat format, which natively supports matrix operations and time-series analysis through functions like plot() and interp1(). Each file contains comprehensive experimental metadata including sampling rates, calibration parameters, and test protocols, facilitating straightforward data processing using MATLAB's table arrays or custom scripts for feature extraction. Researchers can leverage this structured dataset to deeply analyze battery degradation patterns, implement machine learning algorithms like SVM or LSTM for lifetime prediction, and develop reliable diagnostic models. The data organization allows efficient access to cycle-specific parameters through indexing operations, enabling quantitative analysis of capacity fade and internal resistance growth. These resources aim to support battery lifespan studies by providing validated experimental data for algorithm development and model validation, ultimately contributing to advancements in battery technology innovation and application reliability.