***An accelerated Life Testing Dataset for Lithium-Ion Batteries with Constant and Variable Loading Conditions***

We present an accelerated Li-ion battery life cycle dataset focused on a large range of load levels and the characterization of the life cycle of a battery pack composed of two 18650 battery cells. The life cycle study is conducted with a total of 26 battery packs that are grouped by constant and random loading conditions, loading levels and number of load level changes. Furthermore, we conducted load cycling on second-life batteries, where surviving cells from previously aged battery packs were assembled to second- life packs.

The dataset was generated from a custom-made testbed to cycle battery packs designed and developed by Kajetan Fricke, Renato Nascimento, and Prof. Felipe Viana, from the Probabilistic Mechanics Laboratory at the University of Central Florida (UCF).

Data from capacitors subjected to electrical stress at 10V.

Data collected from the simulated experiments on small satellite BP930 batteries using the MACCOR system.

Fatigue experiments were conducted on aluminum lap-joint specimens, and lamb wave signals were recorded for each specimen at several time points (i.e., defined as number of cycles in fatigue testing). Signals from piezo actuator-receiver sensor pairs were reported and it was observed that these signals were directly related to the crack lengths developed during fatigue testing. Optical measurements of surface crack lengths are also provided as the ground truth. The data set is split in training and validation to facilitate the application of data-driven methods.

This data set was generated at Arizona State University by Prof. Yongming Liu, Dr. Tishun Peng, and their collaborators. The data set was used for the Prognostics Health Management (PHM) Data Challenge for the 2019 Conference on Prognostics and Health Management. Other than the data set authors, the following individuals helped put together the 2019 PHM data challenge and make the data set publicly available: Matteo Corbetta and Portia Banerjee (KBR, Inc, NASA Ames), Kurt Doughty (Collins Aerospace), Kai Goebel (PARC), and Scott Clements (Lockheed Martin).

Data Set Citation:
Peng T, He J, Xiang Y, Liu Y, Saxena A, Celaya J, Goebel K. Probabilistic fatigue damage prognosis of lap joint using Bayesian updating. Journal of Intelligent Material Systems and Structures. 2015 May;26(8):965-79.

Publication Citation:
He J, Guan X, Peng T, Liu Y, Saxena A, Celaya J, Goebel K. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves. Smart Materials and Structures. 2013 Sep 4;22(10):105007.

This dataset describes the degradation of an aircraft engine. The dataset was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08). The challenge is still open for the researchers to develop and compare their efforts against the winners of the challenge in 2008.

Data sets consist of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different aircraft engine – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data are contaminated with sensor noise.

Preliminary data from thermal overstress accelerated aging using the aging and characterization system. The data set contains aging data from 6 devices, one device aged with DC gate bias and the rest aged with a squared signal gate bias. Several variables are recorded and, in some cases, high-speed measurements of gate voltage, collector-emitter voltage, and collector current are available. The data set is provided by the NASA Prognostics Center of Excellence (PCoE).

Experiments on a milling machine for different speeds, feeds, and depth of cut. Records the wear of the milling insert, VB. The data set was provided by the UC Berkeley Emergent Space Tensegrities (BEST) Lab.

Experiments were conducted on 3 small raceways in which spirulina was inoculated. The growth and, ultimately, decline of the algae biomass was recorded along with several environmental parameters. Experiments were conducted by the Exobiology group at NASA Ames.

Data Set Citation: Brad Bebout, Leslie Profert-Bebout, Erich Fleming, Angela Detweiler, and Kai Goebel "Algae Raceway Data Set", NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health.

In this dataset, four 18650 Li-ion batteries (Identified as RW9, RW10, RW11 and RW12) were continuously operated using a sequence of charging and discharging currents between -4.5A and 4.5A. This type of charging and discharging operation is referred to here as random walk (RW) operation. Each of the loading periods lasted 5 minutes, and after 1500 periods (about 5 days) a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health.

In this dataset, four 18650 Li-ion batteries (Identified as RW3, RW4, RW5 and RW6) were continuously operated by repeatedly charging them to 4.2V and then discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 4A. This type of discharging profile is referred to here as random walk (RW) discharging. After every fifty RW cycles a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.