{"data":{"id":"10.5281/zenodo.18328700","type":"dois","attributes":{"doi":"10.5281/zenodo.18328700","prefix":"10.5281","suffix":"zenodo.18328700","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Xie, Shaohua","nameType":"Personal","givenName":"Shaohua","familyName":"Xie","affiliation":["Harbin Institute of Technology"],"nameIdentifiers":[]}],"titles":[{"title":"Dataset for : Realistic Multi-Fault Diagnostics of Millions-Scale Li-ion batteries with Rapid Unsupervised Learning"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-01-21","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.18328701","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"},{"rights":"Copyright (c) 2026 Shaohua Xie and co-authors","rightsUri":"http://rightsstatements.org/vocab/InC/1.0/"}],"descriptions":[{"description":"Dataset for : Realistic Multi-Fault Diagnostics of Millions-Scale Li-ion batteries with Rapid Unsupervised Learning\n\nAbstract\n\n\n\nThe rapid deployment of battery swapping stations necessitates scalable and reliable fault diagnosis, yet massive, sparse operational data and scarce labeled samples make this challenging. Here, we report a rapid unsupervised learning framework for realistic  multi-fault diagnosis in million-scale battery fleets. Our approach employs a double-layer mechanism. First, we rapidly screen for abnormal devices by extracting features from voltage-envelope sequences. Subsequently, we pinpoint faulty cells and types using an enhanced two-stage unsupervised clustering combined with rule-based fault tracing. The framework is validated on a production dataset of over 128,000 devices, achieving 97.33% device-layer and 99.66% cell-layer accuracy. Laboratory tests on recalled batteries further confirm the detection of low-capacity and micro-short-circuit faults. These results demonstrate scalability and robustness under sparse-data conditions, enabling reliable operations for large-scale energy storage systems.\n\n\nDataset Structure\n\n \n\nDataRepo/\n\n├── fullDataset/\n\n│   └── fullDataset.json             # Feature data for all devices\n\n└── predefinedDataset/\n\n    ├── data/                        # Raw data for predefined devices\n\n    └── processedData/\n\n        ├── predefinedFeatures.json  # Extracted features for predefined devices\n\n        ├── device_level_info.csv    # Device-level information extracted from JSON\n\n        └── cell_level_info.csv      # Cell-level information extracted from JSON\n\nDataset Description\n\n1. Predefined Dataset (predefinedDataset/)\n\nThe predefined dataset contains data for a selected set of devices used in preliminary research:\n\n Raw Data ( data/ )\n\n\n\nContains raw voltage data files for predefined devices\n\nEach file represents voltage measurements from a single device\n\nData format: CSV files with timestamp and voltage readings for each cell Processed Data ( processedData/ )\n\nContains feature-extracted data from the raw measurements\n\npredefinedFeatures.json :\n\n\n\nJSON file with device filenames as keys\n\nEach value contains comprehensive feature information for the corresponding device\n\nFeatures include both device-level and cell-level characteristics\n\n\n\ndevice_level_info.csv :\n\n\n\nCSV file extracted from the JSON\n\nContains only device-level information for easier visualization and analysis\n\n\n\ncell_level_info.csv :\n\n\n\nCSV file extracted from the JSON\n\nContains only cell-level information for focused analysis\n\n\n\n\n2. Full Dataset (fullDataset/)\n\nThe full dataset contains feature-extracted data for all devices:\n\n\n\n\n\nfullDataset.json :\n\n\n\n\n\n\n\n\n\nJSON file with the same structure as predefinedFeatures.json\n\nContains feature data for all devices in the dataset\n\nFormat and feature definitions are consistent with the predefined dataset","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://zenodo.org/doi/10.5281/zenodo.18328700","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-01-22T07:53:11.000Z","registered":"2026-01-22T07:53:12.000Z","published":"2026","updated":"2026-01-22T07:53:12.000Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}},"provider":{"data":{"id":"cern","type":"providers"}},"media":{"data":{"id":"10.5281/zenodo.18328700","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[{"id":"10.5281/zenodo.18328701","type":"dois"}]},"versionOf":{"data":[]}}}}