contributor author | Shi, Junchuan | |
contributor author | Wei, Yupeng | |
contributor author | Wu, Dazhong | |
date accessioned | 2024-12-24T19:03:52Z | |
date available | 2024-12-24T19:03:52Z | |
date copyright | 6/7/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 1530-9827 | |
identifier other | jcise_24_9_090901.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4303224 | |
description abstract | Monitoring the health condition as well as predicting the performance of lithium-ion batteries is crucial to the reliability and safety of electrical systems such as electric vehicles. However, estimating the discharge capacity and end-of-discharge (EOD) of a battery in real-time remains a challenge. Few works have been reported on the relationship between the capacity degradation of a battery and EOD. We introduce a new data-driven method that combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) models to predict the discharge capacity and the EOD using online condition monitoring data. The CNN model extracts long-term correlations among voltage, current, and temperature measurements and then estimates the discharge capacity. The BiLSTM model extracts short-term dependencies in condition monitoring data and predicts the EOD for each discharge cycle while utilizing the capacity predicted by the CNN as an additional input. By considering the discharge capacity, the BiLSTM model is able to use the long-term health condition of a battery to improve the prediction accuracy of its short-term performance. We demonstrated that the proposed method can achieve online discharge capacity estimation and EOD prediction efficiently and accurately. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Data-Driven Online Prediction of Discharge Capacity and End-of-Discharge of Lithium-Ion Batteries | |
type | Journal Paper | |
journal volume | 24 | |
journal issue | 9 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4063985 | |
journal fristpage | 90901-1 | |
journal lastpage | 90901-10 | |
page | 10 | |
tree | Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 009 | |
contenttype | Fulltext | |