Industrial LiFePO₄ Battery Management System Dataset for Early Degradation Analysis and Remaining Useful Life Prediction

Description

This dataset contains operational records from a Lithium Iron Phosphate battery pack used in an industrial setting. The pack is rated at 25.6 V and 40 Ah, and the data were supplied by GMP Lithium Ltd., Bangladesh. The records reflect battery operation under practical working conditions. The dataset includes 3,000 charge-discharge cycle records. Each record contains the cycle number, battery capacity, State of Health, Remaining Useful Life, pack voltage, pack current, Depth of Discharge, operating temperature, and individual cell voltages. These data can be used for battery degradation analysis, State of Health estimation, Remaining Useful Life prediction, machine learning and deep learning model development, and battery performance analysis. Both pack-level and cell-level measurements are included, allowing researchers to examine battery ageing, operational behaviour, and performance changes across the recorded lifecycle. The repository provides the dataset in CSV format, along with a codebook that explains each variable, its unit, and the data structure. This documentation is intended to make the dataset easier to understand and reuse.

Steps to reproduce

1. Download the CSV dataset and accompanying codebook. 2. Load the dataset into Python, R, MATLAB, or another data analysis environment. 3. Use the codebook to identify variable names, units, and descriptions. 4. Analyze battery degradation using Capacity(Ah), SOH(%), and RUL(cycles). 5. Use pack-level variables such as Voltage(V), Current(A), DoD(%), and Temperature(C) for operational analysis. 6. Use Cell1_V to Cell8_V for cell-level analysis. 7. Develop machine learning or deep learning models for battery health assessment and Remaining Useful Life prediction as required.

Institutions

• Daffodil International University

  Dhaka Division, Dhaka

Categories

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