Realistic fault detection of Li-ion battery via dynamical deep learning approach

Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we release three datasets comprising over 690,000 LiB charging snippets from 347 EVs. <br> The dataset is released as part of our paper " Realistic fault detection of Li-ion battery via dynamical deep learning approach ".精准评估锂离子电池（Li-ion battery，LiB）的安全状态，能够减少意外的电芯失效事件，推动电池的规模化部署，并助力低碳经济发展。尽管人工智能领域近年来取得了显著进展，但受限于复杂的失效机制，且缺乏搭载大规模数据集的真实测试框架，现有异常检测方法并未针对真实电池场景进行定制化开发与有效性验证。本次研究发布三个数据集，涵盖来自347辆电动汽车（Electric Vehicle，EV）的超过69万个锂离子电池充电数据片段。 本数据集随我们的论文《基于动态深度学习方法的锂离子电池真实故障检测》同步发布。

搜集汇总

背景与挑战

背景概述

该数据集包含超过690,000个锂离子电池充电片段，来自347辆电动汽车，旨在通过动态深度学习方法进行电池安全状况的准确评估。数据集支持电池故障检测研究，特别关注现实世界中的电池安全评估。