bsebench-org/tri-attia-fast-charge-2020-raw

该数据集是一个关于电池极端快速充电闭环优化的原始镜像数据集，来源于TRI Energy & Materials Data / data.matr.io项目。它描述了在快速充电条件下循环的商业磷酸铁锂（LFP）/石墨电池数据，关联于Nature出版物（Attia et al., 2020）。数据集包含多种文件类型，如MATLAB结构文件、JSON结构ZIP文件、原始CSV数据文件、元数据CSV文件和调度文件，总计718个文件，大小约22.1 GB。数据由斯坦福大学、MIT和丰田研究所提供，用于机器学习和电池充电协议优化研究。This dataset is a raw mirror of the TRI Energy & Materials Data / data.matr.io project Closed-loop optimization of extreme fast charging for batteries using machine learning. It describes commercial lithium-ion phosphate (LFP)/graphite cells cycled under fast-charging conditions for the associated Nature publication (Attia et al., 2020). The dataset includes various file types such as MATLAB struct files, JSON structure ZIP files, raw CSV data files, metadata CSV files, and schedule files, totaling 718 files with approximately 22.1 GB in size. The data is provided by Stanford University, MIT, and Toyota Research Institute for machine learning and battery charging protocol optimization research.