Online capacity estimation for lithium-ion batteries through joint estimation method

Quanqing Yu, Rui Xiong, Ruixin Yang and Michael G. Pecht

Applied Energy, 2019, vol. 255, issue C

Abstract: Accurate capacity estimation of lithium-ion batteries is a crucial challenge, especially in the presence of noise in the acquisition sensors. This paper developed an online capacity estimation technique based on the joint estimation algorithms for lithium-ion batteries. The recursive least squares algorithm is used for parameter identification, and the adaptive H∞ filter is responsible for capacity estimation. In order to solve the problem that the capacity and state of charge will affect each other and cause the convergence speed to slow down, the open circuit voltage at the current sampling instant is expressed as the equation of open circuit voltage and capacity at the previous sampling instant. Therefore, the capacity can be treated as a state, as well as the open circuit voltage, rather than state of charge to be estimated through the adaptive H∞ filter. The capacity estimation error based on recursive least squares and adaptive H∞ filter is also deduced in this study. The simulation results indicate that the estimated capacity can quickly converge to the reference capacity in case the initial parameter values are inaccurate. Moreover, the erroneous initial parameters have a greater impact than the sensor noises on the capacity estimation error.

Keywords: Lithium-ion batteries; State of charge; Capacity estimation; Sensor bias noise; Sensor variance noise; Adaptive H∞ filter (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919315041
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:255:y:2019:i:c:s0306261919315041

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.2019.113817

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 ().