Open access dataset, code library and benchmarking deep learning approaches for state-of-health estimation of lithium-ion batteries

标杆管理 计算机科学 可解释性 源代码 水准点(测量) 预处理器 规范化(社会学) 深度学习 机器学习 人工智能 数据预处理 数据挖掘 业务 社会学 营销 大地测量学 地理 操作系统 人类学
作者
Fujin Wang,Zhi Zhai,Bingchen Liu,Shiyu Zheng,Zhibin Zhao,Xuefeng Chen
出处
期刊:Journal of energy storage [Elsevier BV]
卷期号:77: 109884-109884 被引量:28
标识
DOI:10.1016/j.est.2023.109884
摘要

Great progress has been made in deep learning (DL) based state-of-health (SOH) estimation of lithium-ion batteries, which helps to provide recommendations for predictive maintenance and replacement of lithium-ion batteries. However, despite the abundance of articles, few open-source codes are publicly available. While there are several public datasets, they tend to be more oriented toward simulating laboratory environments rather than real-world usage scenarios. Moreover, they solely provide raw data without any corresponding preprocessing codes, resulting in inconsistencies in preprocessing methods across different papers. These reasons lead to unfair comparisons and ineffective improvements. In response to these problems, this paper publishes a large-scale lithium-ion battery run-to-failure dataset, consisting of 55 batteries, and provides a unified data preprocessing method. Besides, we comprehensively evaluate 5 well-known DL-based models to provide benchmark research. To be specific, first, the existing DL-based SOH estimation methods are reviewed in detail. Second, we provide a comprehensive evaluation of DL-based models on 2 large-scale datasets, including 100 batteries, with 3 input types and 3 normalization methods. Third, we make the complete evaluation codes and dataset publicly available for better comparison and model improvement. Fourth, we discuss future DL-based SOH estimation, including unsupervised learning, transfer learning, interpretability, and physics-informed machine learning. We emphasize the importance of open-source code, provide baseline estimation errors (error upper bounds), and discuss existing issues in this field. The code library is available at: https://github.com/wang-fujin/SOHbenchmark.
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