{ "source_api_base": "https://data.matr.io/1/api/v1", "project": { "_id": "5d80e633f405260001c0b60a", "access": { "groups": [ { "flags": [], "id": "5c48cd8bc625d70001c32389", "level": 2 } ], "users": [ { "flags": [], "id": "5c48cd8bc625d70001c32386", "level": 2 } ] }, "motivation": null, "objective": "This dataset, used in our publication \u201cClosed-loop optimization of extreme fast charging for batteries using machine learning\u201d, consists of commercial lithium-ion batteries cycled under fast-charging conditions. These lithium-ion phosphate (LFP)/graphite cells, manufactured by A123 Systems (APR18650M1A), were cycled in horizontal cylindrical fixtures on a 48-channel Arbin LBT potentiostat in a forced convection temperature chamber set to 30\u00b0C. The cells have a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V.\n\nThe objective of this work is to optimize fast charging for lithium-ion batteries. As such, all cells in this dataset are charged with one of 224 six-step, 10-minute fast-charging protocols. These protocols have the format \u201cCC1-CC2-CC3-CC4\u201d, as documented in the manuscript. The upper and lower cutoff potentials are 3.6 V and 2.0 V, respectively, which are consistent with the manufacturer\u2019s specifications. These cutoff potentials are fixed for all current steps, including fast charging; after some cycling, the cells may hit the upper cutoff potential during fast charging, leading to significant constant-voltage charging. All cells discharge at 4C.\n\nThe dataset is divided into five \u201cbatches\u201d, representing approximately 48 cells each. Cells in the first four batches were cycled 100-120 times; these batches were used in the CLO test (see manuscript). Cells in the final batch were cycled to failure (or beyond); this batch was used to validate the performance of CLO. Each batch is defined by a \u201cbatch date\u201d, or the date the tests were started. Each batch has a few irregularities, as detailed on the page for each batch.\n\nIn the CLO batches, cells often run past cycle 100 (max 120). However, only the first 100 cycles are used in early prediction. Due to cell-to-cell variation during the constant-voltage hold steps, not all cells finish at exactly the same time. We cycle the cells past cycle 100 to maintain a uniform temperature for the last cells to reach cycle 100 within a batch.\n\nThe data is provided in a MATLAB struct for each batch. The struct provides a convenient data container in which the data for each cycle is easily accessible. This struct can be loaded in either MATLAB or python (via the h5py package); see the below github repository for details on loading these structs via python. Additionally, the raw data for each cell is available as a CSV file.\n\nTemperature data was only collected for the validation batch. The temperature measurements are performed by attaching a Type T thermocouple with thermal epoxy (OMEGATHERM 201) and Kapton tape to the exposed cell can after stripping a small section of the plastic insulation. Note that the temperature measurements are not perfectly reliable; the thermal contact between the thermocouple and the cell can may vary substantially, and the thermocouple sometimes loses contact during cycling.\n\nInternal resistance measurements were obtained during charging at 80% SOC by averaging 10 pulses of \u00b13.6C with a pulse width of 33 ms.\n\nThe following repository contains some starter code to load the datasets in either MATLAB and python: https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation. Other relevant repositories include the data processing and early prediction code (https://github.com/chueh-ermon/BMS-autoanalysis), the charging protocol generation code for automated creation of battery cycler tests (https://github.com/chueh-ermon/automate-Arbin-schedule-file-creation), and the CLO algorithm and paper figures (https://github.com/petermattia/battery-parameter-spaces).\n\nIf using this dataset in a publication, please cite:\nAttia, P.M., Grover, A., Jin, N. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. 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