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BatteryLife|电池寿命预测数据集|电化学数据集
收藏arXiv2025-02-27 更新2025-02-28 收录
下载链接：
https://github.com/Ruifeng-Tan/BatteryLife
下载链接
链接失效反馈资源简介：
BatteryLife数据集是由香港科技大学（广州）等机构提出的一个全面电池寿命预测数据集。该数据集整合了16个数据集，包含超过90,000个样本，是迄今为止最大的电池寿命数据集。它提供了包括锂离子、锌离子和钠离子电池在内的多种类型电池，覆盖了8种格式、80种化学系统、12种操作温度和646种充放电协议，具有前所未有的多样性。该数据集既包括实验室测试数据，也包括工业测试数据，为电池寿命预测研究提供了丰富的资源。
提供机构：
香港科技大学（广州）
创建时间：
2025-02-26
AI搜集汇总
数据集介绍
构建方式
BatteryLife 数据集的构建是通过整合16个数据集完成的，这些数据集包含了实验室测试和工业测试的数据。该数据集的规模是之前最大数据集的2.4倍，提供了超过90,000个样本，覆盖了8种电池格式、80种化学系统、12种操作温度和646种充放电协议。此外，BatteryLife 是第一个发布锌离子电池、钠离子电池和工业测试的大容量锂离子电池寿命数据集。
特点
BatteryLife 数据集的特点是规模大、多样性高。它提供了前所未有的多样性，包括4倍的格式、16倍的化学系统、2.4倍的运行温度和3.4倍的充放电协议，相比于之前的最大数据集 BatteryML [50]。此外，BatteryLife 还包含了实验室测试的锂离子、钠离子和锌离子电池寿命数据，以及工业测试的大容量锂离子电池寿命数据。
使用方法
BatteryLife 数据集的使用方法包括以下步骤：首先，将数据集分为训练集、验证集和测试集。然后，对原始循环数据进行线性插值和归一化处理。接下来，使用机器学习模型对处理后的数据进行训练，并使用平均绝对百分比误差 (MAPE) 和 15% 准确率 (15%-Acc) 两个指标来评估模型性能。此外，BatteryLife 还提供了一个 CyclePatch 插件技术，可以应用于一系列神经网络中，以改进模型性能。
背景与挑战
背景概述
在当前社会，可充电电池广泛应用于电动汽车、电网和便携式设备等领域。然而，由于电池内部的电化学反应机制，电池在循环使用过程中会不可避免地发生退化，导致可用时间缩短，并可能引发安全问题。为了确保电池的安全和可持续运行，电池工程师和科学家需要进行退化测试以测量电池寿命。然而，这些测试通常耗时较长，因为电池寿命的下降是非线性的，需要数月甚至数年才能达到电池寿命的终点。因此，电池寿命预测（BLP）变得至关重要。BLP研究依赖于电池退化测试产生的时间序列数据，对于电池的利用、优化和生产具有重要意义。BatteryLife数据集的创建旨在解决现有数据集规模有限、数据多样性不足和基准不一致的问题，从而为电池寿命预测提供更全面、更准确的数据支持。
当前挑战
BatteryLife数据集面临的主要挑战包括：1)数据集规模有限，难以深入了解现代电池寿命数据；2)数据多样性不足，大多数数据集仅限于实验室测试的小型锂离子电池，且测试条件单一，难以推广；3)基准不一致，现有研究采用不同的数据集和实验设置，难以评估基准的有效性。为了解决这些挑战，BatteryLife数据集整合了16个数据集，样本量是之前最大数据集的2.4倍，并提供了最多样化的电池寿命资源，包括8种电池格式、80种化学体系、12种工作温度和646种充放电协议。BatteryLife数据集还首次发布了锌离子电池、钠离子电池和工业测试的大型锂离子电池的电池寿命数据集。此外，BatteryLife数据集还提供了一个统一的实验设置，支持从1到100个循环的电池寿命预测，覆盖了广泛的应用场景。
常用场景
经典使用场景
BatteryLife数据集是电池寿命预测领域的重要资源，它通过整合16个数据集，提供了超过90,000个样本，是迄今为止最大的电池寿命数据集。这使得研究人员能够深入了解现代电池寿命数据，并推动电池寿命预测模型的研发。此外，BatteryLife数据集提供了最多样化的电池寿命资源，涵盖了8种电池格式、80种化学系统、12种操作温度和646种充放电协议，包括实验室和工业测试。这使得模型能够在不同的测试条件下学习电池寿命的基本模式，并提高其泛化能力。
衍生相关工作
BatteryLife数据集的提出，为电池寿命预测领域的研究提供了新的思路和方向。基于BatteryLife数据集，研究人员可以开发更精确、更有效的电池寿命预测模型，并探索新的研究问题。此外，BatteryLife数据集还可以与其他数据集结合，构建更全面的电池寿命预测模型，推动电池寿命预测领域的发展。
数据集最近研究
最新研究方向
BatteryLife数据集为电池寿命预测领域带来了突破性的进展，它不仅提供了迄今为止最大规模的电池寿命数据集，还涵盖了多种电池类型和测试条件，极大地丰富了数据多样性。该数据集的发布解决了现有数据集规模有限、数据多样性不足以及缺乏统一基准的问题，为电池寿命预测研究提供了强有力的支持。基于BatteryLife数据集的研究表明，传统的时序分析方法并不适用于电池寿命预测，而CyclePatch等新型插接技术能够显著提高模型性能，为该领域的研究提供了新的思路和方法。此外，BatteryLife数据集还揭示了模型在不同老化条件和领域中的可迁移性，为未来研究指明了方向。
相关研究论文
- 1BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction香港科技大学(广州) · 2025年
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