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 ".

准确评估锂离子电池（Li-ion battery, LiB）的安全状态，可降低意外的电芯失效风险、促进电池的规模化部署，并助力低碳经济发展。尽管人工智能领域近期已有显著进展，但受限于复杂的失效机理与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实际电池应用场景完成定制化开发与验证。
在此我们发布三个数据集，包含来自347台电动汽车（Electric Vehicle, EV）的超过69万条锂离子电池充电片段。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》一同公开发布。

搜集汇总

背景与挑战

背景概述

该数据集包含来自347辆电动汽车的超过690,000个锂离子电池充电片段，总大小为1.38 GB，旨在通过动态深度学习方法进行锂离子电池的现实故障检测，以评估电池安全状况并减少意外故障。