contributor author | Chang, Chun | |
contributor author | Dai, Jiuhe | |
contributor author | Pan, Yaliang | |
contributor author | Lv, Lu | |
contributor author | Gao, Yang | |
contributor author | Jiang, Jiuchun | |
date accessioned | 2025-04-21T10:02:25Z | |
date available | 2025-04-21T10:02:25Z | |
date copyright | 10/7/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 2381-6872 | |
identifier other | jeecs_22_3_031008.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4305369 | |
description abstract | This study focuses on the safety and reliability issues of lithium-ion batteries, proposing a fault diagnosis strategy that leverages dual-feature extraction from both the time and frequency domains. Additionally, by modifying the traditional autoencoder, the study proposes a feature-guided autoencoder as an unsupervised model for extracting features in the time domain. Initially, wavelet packet decomposition and its energy-denoising treatment are employed to refine fault information within battery voltage signals. Subsequently, the reconstruction error outputted by the Feature-Guided Autoencoder is utilized as the time-domain fault feature, while the cosine similarity of the energy of signals in various frequency bands obtained after wavelet packet decomposition serves as the frequency-domain fault feature. Ultimately, this article selects the Isolation Forest algorithm for two-dimensional outlier detection of time and frequency features. Experimental results demonstrate that the feature-guided autoencoder proposed in this study not only enhances the sensitivity of time-domain fault features compared to traditional autoencoders and their variants but also optimizes issues related to training time and computational load. The effectiveness of the proposed dual-feature fault diagnosis method in both the time and frequency domains is validated through data from two actual vehicles, showing superior early fault detection capability relative to single-feature fault diagnosis methods. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Fault Diagnosis Method for Electric Vehicle Lithium Power Batteries Based on Dual-Feature Extraction From the Time and Frequency Domains | |
type | Journal Paper | |
journal volume | 22 | |
journal issue | 3 | |
journal title | Journal of Electrochemical Energy Conversion and Storage | |
identifier doi | 10.1115/1.4066479 | |
journal fristpage | 31008-1 | |
journal lastpage | 31008-14 | |
page | 14 | |
tree | Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 003 | |
contenttype | Fulltext | |