Convolutional Neural Network-Based Classification of Lithium-Ion Battery CAN Messages

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Tero Niemi;Tomi Pitkäaho;Juha Röning
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引用次数: 0

Abstract

The lithium-ion battery Controller Area Network (CAN) messages are essential to battery monitoring, recycling, and second-life applications. However, the proprietary nature of database connection (DBC) files and the diversity of CAN message encodings across manufacturers pose significant challenges. This study proposes a convolutional neural network (CNN) based approach to classify battery-related CAN messages without reliance on proprietary DBC files. By analyzing data from four manufacturers and categorizing messages into three key groups—voltage and current, temperature and State of Charge (SoC), and configuration or other battery parameters, the CNN achieved an accuracy of 94.87% on unseen data. The model demonstrated robust performance, effectively generalizing across diverse CAN message formats. Practical validation confirmed the model's ability to identify key battery metrics reliably. This publication highlights the potential of deep learning to address proprietary data barriers, facilitating accessible and scalable battery monitoring and health assessment approaches. The findings contribute to advancing sustainable battery management practices, particularly for companies focusing on battery recycling and second-life applications, and pave the way for further research on leveraging temporal and expanded datasets to enhance classification accuracy and scope.
基于卷积神经网络的锂离子电池CAN信息分类
锂离子电池控制器区域网络(CAN)信息对于电池监测、回收和二次使用至关重要。然而,数据库连接(DBC)文件的专有性质和不同制造商之间CAN消息编码的多样性构成了重大挑战。本研究提出了一种基于卷积神经网络(CNN)的方法来分类电池相关的CAN消息,而不依赖于专有的DBC文件。通过分析来自四家制造商的数据,并将信息分为三个关键组——电压和电流,温度和充电状态(SoC),以及配置或其他电池参数,CNN在未见数据上实现了94.87%的准确率。该模型显示了强大的性能,有效地泛化了不同的CAN消息格式。实际验证证实了该模型能够可靠地识别关键电池指标。该出版物强调了深度学习在解决专有数据障碍、促进可访问和可扩展的电池监测和健康评估方法方面的潜力。研究结果有助于推进可持续电池管理实践,特别是对于专注于电池回收和二次使用应用的公司,并为进一步研究利用时间和扩展数据集来提高分类准确性和范围铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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