Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network

IF 4.8 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Automotive Innovation Pub Date : 2022-04-20 DOI:10.1007/s42154-022-00180-6
Niankai Yang, Ziyou Song, Mohammad Reza Amini, Heath Hofmann
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引用次数: 5

Abstract

Reliable and timely detection of an internal short circuit (ISC) in lithium-ion batteries is important to ensure safe and efficient operation. This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation (i.e., no independent current sensors for individual cells in a parallel string). To characterize ISC-related signatures in battery string responses, an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC. By analyzing the data generated from the electro-thermal model, the distribution of surface temperature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations. A convolutional neural network (CNN) is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs. Based on the estimated ISC resistance from CNN, the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery. The algorithm is evaluated in the presence of signal noises in terms of accuracy, false alarm rate, and missed detection rate, verifying the effectiveness and robustness of the proposed approach.

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基于卷积神经网络的并联电池内部短路检测
可靠、及时地检测锂离子电池中的内部短路(ISC)对于确保安全高效运行至关重要。本文通过考虑电池单元的不均匀性和传感器限制(即,并联串中的单个电池单元没有独立的电流传感器)来研究并联电池单元的ISC检测。为了表征电池串响应中与ISC相关的特征,首先建立了并联电池单元的电热模型,该模型明确地捕捉ISC。通过分析从电热模型生成的数据,在传感器限制的约束下,电池串内单个电池之间的表面温度分布被识别为ISC检测的指标。然后设计卷积神经网络(CNN),通过使用电池表面温度和串的总容量作为输入来估计ISC电阻。根据CNN估计的ISC电阻,将串分为故障或非故障,以指导电池的检查或更换。在存在信号噪声的情况下,从精度、虚警率和漏检率等方面对该算法进行了评估,验证了该方法的有效性和鲁棒性。
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
Automotive Innovation
Automotive Innovation Engineering-Automotive Engineering
CiteScore
8.50
自引率
4.90%
发文量
36
期刊介绍: Automotive Innovation is dedicated to the publication of innovative findings in the automotive field as well as other related disciplines, covering the principles, methodologies, theoretical studies, experimental studies, product engineering and engineering application. The main topics include but are not limited to: energy-saving, electrification, intelligent and connected, new energy vehicle, safety and lightweight technologies. The journal presents the latest trend and advances of automotive technology.
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