A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

Zhengyi Bao, Jiahao Jiang, Chunxiang Zhu and Mingyu Gao
Additional contact information
Zhengyi Bao: School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China
Jiahao Jiang: School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China
Chunxiang Zhu: School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China
Mingyu Gao: School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China

Energies, 2022, vol. 15, issue 12, 1-16

Abstract: Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset.

Keywords: lithium-ion battery; state-of-health; dilated convolutional neural networks; bidirectional gated recurrent units; hybrid network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/12/4399/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/12/4399/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:12:p:4399-:d:840636

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().