Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method

Jian Ma, Shu Xu, Pengchao Shang, Yu Ding, Weili Qin, Yujie Cheng, Chen Lu, Yuzhuan Su, Jin Chong, Haizu Jin and Yongshou Lin

Applied Energy, 2020, vol. 262, issue C, No S0306261920300027

Abstract: Cycle life testing in battery development is crucial for the selection of a formulation, but it is time-consuming and costly for battery enterprises. A test optimization approach for different Li-ion power battery formulations is designed based on a hybrid remaining-useful-life prediction method to reduce the high cost of constant temperature–stress testing. The test life is replaced by an accurately predicted lifespan to end the testing early and shorten the cycle number. Batteries having the same formulation and tested at different temperatures are integrally optimized for more test savings. Firstly, high-temperature stress testing is stopped early at a preset threshold, and an instance-based transfer learning method is used to predict the battery lifespan by transferring similar historical test samples of different battery formulations to train a highly robust deep learning prediction model. Standard-temperature testing is completely eliminated by utilizing a modified Arrhenius model to estimate the battery lifespan. The model improvements include replacing the high-temperature stress test lifespan with the abovementioned prediction and introducing a prediction error correction coefficient to increase prediction accuracy. The accuracy of the prediction is verified using actual test data from a battery company, resulting in a time savings of nearly 60%. The optimization strategy has extensive application prospects for other constant-stress tests for batteries and other products.

Keywords: Li-ion power battery; Remaining useful life prediction; Cycle life test optimization; Deep learning; Transfer learning; Arrhenius model (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920300027
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:262:y:2020:i:c:s0306261920300027

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.2020.114490

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 ().