Work

Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks

Year: 2024

Type: article

Abstract: This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.5 kHz high-frequency square wave signal is input into the battery module and recorded using a high-speed data acquisition card. The signal is processed by the SDP method to generate characteristic images for fault diagnosis. Finally, a deep learning algorithm is used to evaluate the state of the lithium battery. A total of 3000 samples were collected, with 400 samples used for training and 200 for testing for each fault type, achieving an overall identification accuracy of 99.9%, demonstrating the effectiveness of the proposed method.

Source: Sensors

DOI: 10.3390/s25010094

Authors: Meng-Hui Wang, Jing-Xuan Hong, Shiue-Der Lu

Institution: National Chin-Yi University of Technology

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,955