contributor author | Chen, Jianlong | |
contributor author | Zhang, Chenghao | |
contributor author | Chen, Cong | |
contributor author | Lu, Chenlei | |
contributor author | Xuan, Dongji | |
date accessioned | 2023-11-29T19:02:05Z | |
date available | 2023-11-29T19:02:05Z | |
date copyright | 11/11/2022 12:00:00 AM | |
date issued | 11/11/2022 12:00:00 AM | |
date issued | 2022-11-11 | |
identifier issn | 2381-6872 | |
identifier other | jeecs_20_3_031010.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4294530 | |
description abstract | State of charge (SOC) of lithium-ion batteries is an indispensable performance indicator in a battery management system (BMS), which is essential to ensure the safe operation of the battery and avoid potential hazards. However, SOC cannot be directly measured by sensors or tools. In order to accurately estimate the SOC, this paper proposes a convolutional neural network based on self-attention mechanism. First, the one-dimensional convolution is introduced to extract features from battery voltage, current, and temperature data. Then, the self-attention mechanism can reduce the dependence on external information and well capture the internal correlation of features extracted by the convolutional layer. Finally, the proposed method is validated on four dynamic driving conditions at five temperatures and compared with the other two deep learning methods. The experimental results show that the proposed method has good accuracy and robustness. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | State-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism | |
type | Journal Paper | |
journal volume | 20 | |
journal issue | 3 | |
journal title | Journal of Electrochemical Energy Conversion and Storage | |
identifier doi | 10.1115/1.4055985 | |
journal fristpage | 31010-1 | |
journal lastpage | 31010-9 | |
page | 9 | |
tree | Journal of Electrochemical Energy Conversion and Storage:;2022:;volume( 020 ):;issue: 003 | |
contenttype | Fulltext | |