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

作　　者：Niankai Yang Ziyou Song Mohammad Reza Amini Heath Hofmann 

机构地区：[1]Department of Naval Architecture and Marine Engineering,University of Michigan,Ann Arbor,MI 48109,USA [2]Department of Mechanical Engineering,National University of Singapore,Singapore 117575,Singapore [3]Department of Electrical Engineering and Computer Science,University of Michigan,Ann Arbor,MI 48109,USA

出　　处：《Automotive Innovation》2022年第2期107-120,共14页汽车创新工程（英文）

摘　　要：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 tem-perature 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.

关 键 词：Internal short circuit Parallel-connected battery cells Convolutional neural network Cell temperature distribution 

分 类 号：TM91[电气工程—电力电子与电力传动]