Dataset Information

Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries.

ABSTRACT: Fast-charging and high-energy-density batteries pose significant safety concerns due to high rates of heat generation. Understanding how localized high temperatures affect the battery is critical but remains challenging, mainly due to the difficulty of probing battery internal temperature with high spatial resolution. Here we introduce a method to induce and sense localized high temperature inside a lithium battery using micro-Raman spectroscopy. We discover that temperature hotspots can induce significant lithium metal growth as compared to the surrounding lower temperature area due to the locally enhanced surface exchange current density. More importantly, localized high temperature can be one of the factors to cause battery internal shorting, which further elevates the temperature and increases the risk of thermal runaway. This work provides important insights on the effects of heterogeneous temperatures within batteries and aids the development of safer batteries, thermal management schemes, and diagnostic tools.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC6502817 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries.

Zhu Yangying Y Xie Jin J Pei Allen A Liu Bofei B Wu Yecun Y Lin Dingchang D Li Jun J Wang Hansen H Chen Hao H Xu Jinwei J Yang Ankun A Wu Chun-Lan CL Wang Hongxia H Chen Wei W Cui Yi Y

Nature communications 20190506 1

Fast-charging and high-energy-density batteries pose significant safety concerns due to high rates of heat generation. Understanding how localized high temperatures affect the battery is critical but remains challenging, mainly due to the difficulty of probing battery internal temperature with high spatial resolution. Here we introduce a method to induce and sense localized high temperature inside a lithium battery using micro-Raman spectroscopy. We discover that temperature hotspots can induce ...[more]

PMID: 31061393

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Project description:Summary Accurate early detection of internal short circuits (ISCs) is indispensable for safe and reliable application of lithium-ion batteries (LiBs). However, the major challenge is finding a reliable standard to judge whether the battery suffers from ISCs. In this work, a deep learning approach with multi-head attention and a multi-scale hierarchical learning mechanism based on encoder-decoder architecture is developed to accurately forecast voltage and power series. By using the predicted voltage without ISCs as the standard and detecting the consistency of the collected and predicted voltage series, we develop a method to detect ISCs quickly and accurately. In this way, we achieve an average percentage accuracy of 86% on the dataset, including different batteries and the equivalent ISC resistance from 1,000 Ω to 10 Ω, indicating successful application of the ISC detection method. Graphical abstract Highlights • A deep learning method is proposed to predict future voltage and power series• The method can accurately forecast voltage and power series over battery life• Evolution inconsistency of normal and ISC battery voltage is used to detect ISC• Early ISC of different types of batteries can be detected accurately and quickly The bigger picture Lithium-ion batteries are applied in many fields because of their high energy and power density. However, accidents associated with battery fires have raised great public awareness and concerns about safety issues. Internal short circuits (ISCs) are the main reason for battery fires. However, so far, there is still a lack of accurate and quick methods for ISC detection. To address this challenge, we report a method to predict future battery characteristics using deep learning. ISCs can be detected accurately and quickly by inconsistency of the evolution of normal and ISC battery characteristics. The method is demonstrated over the full life cycle of batteries. The general method can also be applied to fault detection of many other mechanical and electronic systems. The long-term goal of this research is to achieve a digital twin with broader applications for intelligent battery management, such as prolonging battery life and optimizing charging profiles. Cui et al. demonstrate the feasibility of deep learning methods for early detection of internal short circuits (ISCs). It provides a deep learning method based on encoder-decoder architecture for forecasting voltage and power sequences by taking the historical time series as input. Through the evolution inconsistency of normal and ISC battery voltage, it offers a new way to accurately detect early ISCs.

Dataset Information

Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries.

Publications

Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries.

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