![]() ![]() In the CALCE CS2 dataset, 15 prismatic LCO cells with a nominal capacity of 1.1 Ah were cycled at room temperature. The battery research group at the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland published a battery dataset widely used for SOH estimation. The dataset was first used in to adapt a battery model to account for degradation under random loads. Randomized loads were applied during discharge in an effort to simulate more realistic usage. In pulsed load cycling, the discharge profile consisted of a rest period of 20 minutes followed by a loading period of 10 minutes at 1 A. In RW cycling, the current profile for both discharge and charge was changed every five minutes to a value randomly selected from the set of 0.5, 1, 1.5, 2, 2.5, 3, 3.5, or 4 A. For each cell, after every set of 50 random walk cycles, a pulsed load characterization cycle was performed. The dataset was recorded in 2014.Ĭycling for each cell consisted of two types of charge/discharge cycles: Random Walk (RW) cycling and pulsed load characterization cycling. Five groups of cells were cycled at room temperature and the other two groups were cycled at 40 ☌. This dataset presents data for 28 18650 LCO cells with 2.1 Ah capacity in seven groups. However, because of continuous quality and technology improvements, today’s batteries typically have longer lifetimes. This dataset has been widely used and among the experimental groups, Group 6 is the most commonly used dataset. The table below summarizes this experimental dataset, which consists of six groups of cells. EIS was conducted with a frequency sweep from 0.1 Hz to 5 KHz. However, different discharging profiles were applied to induce degradation based on more realistic usage. All experiments used a charging profile of CC-CV at 1.5 A to 4.2 V with a cutoff current of 20 mA. Cycling consisted of three operational profiles: charging, discharging, and electrochemical impedance spectroscopy (EIS). In this dataset, 34 18650 cells with 2 Ah capacity have been cycled to 70% or 80% of initial capacity at different temperatures using a custom-built battery tester. The last two datasets contain data from testing of battery packs for a small aircraft and a small satellite. The two first datasets provide cycling data for commercial cells and will be described in further detail below. These datasets can be accessed at NASA Datasets. Small Satellite Power Simulation Data Set This data repository is intended for developing prognostic algorithms and includes the following four battery datasets: The Prognostic Center of Excellence (PCoE) at NASA Ames publishes the Prognostic Data Repository. Please help us update it with new datasets and descriptions! NASA Datasets This table is available here as a Google spreadsheet. This article introduces several of the most well-known open datasets for battery testing. Several battery research groups have made their Li-ion datasets publicly available for further analysis and comparison by the greater community as a whole. Not only is specialized equipment, such as multi-channel cyclers, potentiostats, and thermal chambers, needed, but a typical reliability test for battery degradation may require more than six months of uninterrupted cycling. Validation of the results from ongoing research requires a significant amount of experimental testing, and conducting such tests is resource intensive and time-consuming. A vast amount of research has been conducted to address these challenges, including battery design, modeling, state estimation, and lifetime diagnosis and prognosis. However, many challenges remain before they can be used reliably towards promoting a sustainable future through electrification. Lithium-ion (Li-ion) batteries are widely used in different aspects of our lives including in consumer electronics, transportation, and the electrical grid. A Google spreadsheet of the open datasets is provided here as a resource to be updated continuously as a comprehensive table of open datasets.Fourteen publicly available datasets are reviewed in this article and cell types, testing conditions, charge/discharge profiles, recorded variables, dates of experiments, and links to the datasets are provided.Testing of Li-ion batteries is costly and time-consuming, so publicly available battery datasets are a valuable resource for comparison and further analysis.This story is contributed by Abolfazl Shahrooei. Comparison of Open Datasets for Lithium-ion Battery Testing ![]()
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