储能科学与技术 ›› 2023, Vol. 12 ›› Issue (7): 2211-2219.doi: 10.19799/j.cnki.2095-4239.2023.0286

• 储能锂离子电池系统关键技术专刊 • 上一篇    下一篇

基于视觉特征的动力电池组综合健康评估及分筛方法

陈智伟(), 张维戈(), 张珺玮, 张言茹   

  1. 北京交通大学,国家能源主动配电网技术研发中心,北京 100044
  • 收稿日期:2023-04-27 修回日期:2023-05-16 出版日期:2023-07-05 发布日期:2023-07-25
  • 通讯作者: 张维戈 E-mail:22121439@bjtu.edu.cn;wgzhang@bjtu.edu.cn
  • 作者简介:陈智伟(1999—),男,硕士研究生,研究方向为动力电池健康状态评估,E-mail:22121439@bjtu.edu.cn
  • 基金资助:
    中国国家铁路集团有限公司科技研究开发计划项目(N2022J047)

Comprehensive health assessment and screening method of power battery pack based on visual characteristics of charge curves

Zhiwei CHEN(), Weige ZHANG(), Junwei ZHANG, Yanru ZHANG   

  1. National Energy Active Distribution Network Technology Research and Development Center, Beijing Jiaotong University, Beijing 100044, China
  • Received:2023-04-27 Revised:2023-05-16 Online:2023-07-05 Published:2023-07-25
  • Contact: Weige ZHANG E-mail:22121439@bjtu.edu.cn;wgzhang@bjtu.edu.cn

摘要:

随着电动汽车领域的快速发展,海量电池系统的健康监管和性能评估成为有待解决的关键技术之一。本工作搭建了电池组等效电路模型,依托单体不一致型参数的正交组合模拟多种电池组健康状态,通过模型仿真生成电池组样本数据集。搭建了卷积神经网络模型,以电池组局部充电电压曲线图像为输入,提取能够反映电池组健康状态的形态学特征,对电池组进行快速分类。选用整组可用容量、可用能量、容量利用率和能量利用率四个参数指标,通过层次分析法分配各参数权重,提出一种综合考虑电池组性能表征的健康度评价指标,依据评价指标实现电池组分筛。在仿真生成的数据集上对分类模型进行了训练和测试,结果表明,所构建的电池组分类模型在测试集上能够达到97%以上的准确率。通过分类任务混淆矩阵的一系列模型评价指标,进一步验证了该方法的有效性。本工作提出的基于视觉特征提取的电池组健康状态综合评估和分筛方法有助于推动对电池性能评估的研究,为电池系统的健康监管提供新的理论依据。

关键词: 锂离子电池, 卷积神经网络, 不一致性, 等效电路模型, SOH评估及分筛

Abstract:

With the rapid development of electric vehicles, ensuring the health and performance evaluation of large-scale battery systems has become a crucial technological challenge. This paper focuses on addressing this issue by constructing an equivalent circuit model for battery packs. The health status of multiple battery packs is simulated using a combination of orthogonal parameters, representing different types of inconsistencies. Additionally, a dataset of battery pack samples is generated through model simulations. To classify the battery packs efficiently, a convolutional neural network (CNN) model is established. This model extracts morphological features from the local charging voltage curve images of the battery packs. These features serve as input for the CNN, enabling quick classification. Four parameters, namely available capacity, available energy, capacity utilization rate, and energy utilization rate, are selected for evaluation. The weight of each parameter is determined using the Analytic Hierarchy Process. Consequently, a comprehensive health evaluation index is proposed, which takes into account various performance characteristics of battery packs. This index facilitates the screening of battery components based on their evaluation scores. The classification model is trained and tested using the simulated dataset, yielding promising results. The constructed battery pack classification model achieves an accuracy of over 97% on the test set. The effectiveness of this method is further validated by evaluating the model with various indexes, such as the confusion matrix for classification tasks. Overall, the proposed method, which involves the visual feature extraction-based comprehensive assessment and screening of battery pack health status, contributes to the advancement of battery performance evaluation research. It also provides a new theoretical foundation for the health supervision of battery systems.

Key words: lithium-ion batteries, convolutional neural network, cell inconsistency, equivalent circuit model, SOH assessment screening

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