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

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.

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

“…By modeling or conducting experiments under different abuse conditions, a variety of ISC degrees (normally represented as the value of short-circuit resistance) of LIBs have been evaluated. Taking advantage of ML techniques, features were extracted from the battery current and voltage data or operating conditions during cycling to be mapped with targets that could qualitatively [135][136][137] or quantitatively [138][139][140][141] manifest the severity of ISC. Nevertheless, the criteria for ISC severity have not been unified, which is necessary to be taken into consideration for determining the reliable target in this diagnosis scenario.…”