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A Novel Method for State of Charge Estimation in Lithium-Ion Batteries Using Temporal Convolutional Network and Multi-Verse Optimization.

Authors :
Li, Yuanmao
Liu, Guixiong
Deng, Wei
Source :
Batteries; Jan2024, Vol. 10 Issue 1, p12, 19p
Publication Year :
2024

Abstract

This study presents a novel data-driven method for state-of-charge estimation in lithium-ion batteries. It integrates a temporal convolutional network with multi-verse optimization to enhance the accuracy of predicting the state of charge. The temporal convolutional network possesses advantages such as an extended memory window and efficient parallel computation, exhibiting exceptional performance in time-series tasks for state of charge estimation. Its hyperparameters are optimized by adopting multi-verse optimization to obtain better model performance. The driving model utilizes various measurable data as inputs, including battery terminal voltage, current, and surface temperature. To validate the effectiveness of the proposed method, extensive datasets from diverse dynamic working conditions at different ambient temperatures are employed for model training, validation, and testing. The numerical outcomes provide evidence of the proposed method's superior performance compared to the other two methods, providing a more robust and accurate solution for the state of charge estimation in lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23130105
Volume :
10
Issue :
1
Database :
Complementary Index
Journal :
Batteries
Publication Type :
Academic Journal
Accession number :
175050997
Full Text :
https://doi.org/10.3390/batteries10010012