A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method

Kuo Li, Xiao Gao, Caixia Liu, Chun Chang and Xiaoyu Li

Energy, 2023, vol. 269, issue C

Abstract: A battery management system can intelligently manage and maintain battery systems by effectively estimating and predicting battery internal states. Owing to battery nonlinear characteristics related to various influence factors, the estimation of battery internal states should consider the available capacity and ohmic internal resistance. This paper proposes a co-estimation framework for state of charge (SOC), state of power (SOP) and battery available capacity. Firstly, the first-order equivalent model method is used to identify the battery parameters by recursive least squares algorithm with variable forgetting factor, and the SOC-OCV curve of the battery is obtained by combining the ampere-time integration method. Secondly, three Kalman filters are utilized to estimate battery SOCs and the maximum available capacity and internal resistance are estimated by a forgetting factor recursive least square algorithm. Then peak current and power are estimated under the composite constraints of the estimated capacity and internal resistance. Finally, the experimental data are collected at temperatures 25 °C and 40 °C to verify and analyze the proposed method. The results of battery state estimation indicate that the proposed framework can accurate estimation battery internal states and also provide an effective reference for the driving of powered vehicles.

Keywords: State of charge; Capacity estimation; Ohmic resistance estimation; SOP estimation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223002141
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:269:y:2023:i:c:s0360544223002141

DOI: 10.1016/j.energy.2023.126820

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 ().