A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles

Dinghong Chen, Weige Zhang, Caiping Zhang, Bingxiang Sun, XinWei Cong, Shaoyuan Wei and Jiuchun Jiang

Applied Energy, 2022, vol. 327, issue C, No S030626192201371X

Abstract: Life prediction of lithium-ion batteries is vital for battery system utilization and maintenance. Especially, the accurate life prediction in early cycles can accelerate the battery design, production, and optimization. However, diverse aging mechanisms, various cycle profiles, and negligible capacity degradation in the early cycling stages pose significant challenges. This paper proposes a novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles, where the battery lifetime model is formulated by a two-dimensional and one-dimensional parallel hybrid neural network. Firstly, the input data is constructed by a five-step streamlined preprocessing approach. Secondly, two-dimensional and one-dimensional convolutional neural networks are respectively used to extract the underlying associations between the data. Then, the long short-term memory network is employed to learn the time-sequential relationships among the extracted features. Ultimately, the diagnosis for the current cycle life and the prognostic on the remaining useful life of the battery are performed. A well-known dataset is utilized to validate the accuracy and generalization performance of the proposed method. Comparison results with other methods show that the proposed model has strong generalization capability. For the test set composed of data from 31 cells under 25 different cycle profiles, its mean absolute percentage error in early lifetime prediction and remaining useful life prediction is merely 1.47% and 2.85%.

Keywords: Lithium-ion battery; Life prediction; Deep learning; Multiple cycle profiles; Convolutional neural network; Long short-term memory (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192201371X
Full text for ScienceDirect subscribers only

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:eee:appene:v:327:y:2022:i:c:s030626192201371x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.120114

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().