Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory

Zhang Chen, Liqun Chen, Zhengwei Ma, Kangkang Xu, Yu Zhou and Wenjing Shen

Energy, 2023, vol. 277, issue C

Abstract: Accurate early prediction of Li-ion battery aging facilitates new product optimization and application management. Here, a joint modeling scheme is proposed. It is dedicated to decoupling cell-to-cell variability and cycle-by-cycle nonlinear aging in Li-ion batteries, enabling accurate cycle life and capacity trajectory predictions. An aging end-point early predictor is established based on the multiple output Gaussian process (MOGP). It enables better performance by sharing knowledge across outputs, including cycle life and life capacity. For degradation trajectory prediction, an aging process trajectory predictor is constructed based on the ‘prompt-learning’ paradigm neural network, and the life capacity from MOGP is used as the prompt input. Moreover, a Bayesian search strategy is proposed to obtain optimal feature combinations, and feature alignment is used to narrow the gap between source and target domains. The proposed scheme is developed on the source domain with 41 cells and tested on three special target domains with 128 cells. As a result, the predicted error of cycle life can be within 7.87%, and that of the capacity trajectory can be within 6.53%. The prediction results prove its high adaptability and effectiveness. The achievements will promote high-dimensional parameter design and optimization, accelerating battery development and reducing testing costs.

Keywords: Li-ion batteries; Joint modeling; Early prediction; Cycle life; Capacity trajectory (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223010277
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:energy:v:277:y:2023:i:c:s0360544223010277

DOI: 10.1016/j.energy.2023.127633

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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