A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries

Jingwen Wei and Chunlin Chen

Energy, 2021, vol. 229, issue C

Abstract: Battery State-of-charge (SOC) estimation and lifetime prognosis play important roles in its fuel-gauging and predictive maintenance. However, the inevitable modeling biases and measurement noises caused by the complexity of battery dynamics in nonlinearity, temperature sensitivity, and degradation, still challenge the accuracy and robustness. To bridge this research gap, this paper presents a multi-timescale framework for SOC estimation and lifetime prediction of lithium-ion batteries. In the fast timescale, a recursive-least-square-based parameter identification algorithm is first employed to eliminate the effects of identification errors on SOC estimation and reduce the dimension of the SOC observer. Then, a robust observer and dual extended-Kalman-filter (DEKF) are combined to achieve simultaneous SOC and capacity estimation against bounded modeling errors. In the slow timescale, the battery lifetime is predicted through a particle-filtering based on the capacity. The effectiveness and superiority of the proposed method are demonstrated by both simulation and experimental results conducted on LiCoO2 cells. The results indicate that the root-mean-square-error of the SOC and capacity can be achieved within 3.5% and 0.5%, respectively, in the presence of the model mismatch. In addition, the lifetime prediction framework can also ensure accurate lifetime prediction.

Keywords: Lithium-ion batteries; State-of-charge; Robust observer; Lifetime prognosis (search for similar items in EconPapers)
Date: 2021
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/S0360544221009336
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:229:y:2021:i:c:s0360544221009336

DOI: 10.1016/j.energy.2021.120684

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 ().