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汽车应用中电池数据的未来及锂离子电池的健康状态

The future of battery data and the state of health of lithium-ion batteries in automotive applications.

作者信息

von Bülow Friedrich, Heinrich Felix, Paxton William Arthur

机构信息

Volkswagen AG, Wolfsburg, Germany.

Volkswagen AG, Center of Excellence Battery, Salzgitter, Germany.

出版信息

Commun Eng. 2024 Nov 19;3(1):173. doi: 10.1038/s44172-024-00299-w.

DOI:10.1038/s44172-024-00299-w
PMID:39562828
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11577075/
Abstract

Operational data of lithium-ion batteries from battery electric vehicles can be logged and used to model lithium-ion battery aging, i.e., the state of health. Here, we discuss future State of Health definitions, the use of data from battery production beyond production, the logging & aggregation of operational data and challenges of the State of Health in automotive applications. Our suggestions could improve data transfer efficiency and data storage costs.

摘要

来自电池电动汽车的锂离子电池运行数据可以被记录下来，并用于对锂离子电池老化（即健康状态）进行建模。在此，我们讨论未来健康状态的定义、生产以外阶段电池生产数据的使用、运行数据的记录与汇总以及汽车应用中健康状态面临的挑战。我们的建议可以提高数据传输效率和数据存储成本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/eb08e63fcd17/44172_2024_299_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/21bf53e44e65/44172_2024_299_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/02b0a5cff62c/44172_2024_299_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/0388c8b1451a/44172_2024_299_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/d8db5a71b690/44172_2024_299_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/5c4f500f5539/44172_2024_299_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/eb08e63fcd17/44172_2024_299_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/21bf53e44e65/44172_2024_299_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/02b0a5cff62c/44172_2024_299_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/0388c8b1451a/44172_2024_299_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/d8db5a71b690/44172_2024_299_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/5c4f500f5539/44172_2024_299_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/11577075/eb08e63fcd17/44172_2024_299_Fig6_HTML.jpg

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Nat Commun. 2022 Aug 16;13(1):4806. doi: 10.1038/s41467-022-32422-w.
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Text Data Augmentation for Deep Learning.用于深度学习的文本数据增强
J Big Data. 2021;8(1):101. doi: 10.1186/s40537-021-00492-0. Epub 2021 Jul 19.