A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries

Yuchen Song, Datong Liu, Haitao Liao and Yu Peng

Applied Energy, 2020, vol. 261, issue C, No S0306261919320951

Abstract: Lithium-ion batteries are increasingly being used as the energy storage systems in electric vehicles, smart grid and aerospace systems. Estimating the state-of-charge and state-of-health of lithium-ion battery is essential to the operational safety and reliability of a system. However, some useful battery parameters, such as capacity and impedance, are not easy to measure because of the complex testing procedure and conditions. This makes the on-line state-of-health estimation suffer from low accuracy and further impacts the estimation accuracy of state-of-charge. This paper proposes a joint lithium-ion battery state estimation approach that takes advantage of the data-driven least-square-support-vector-machine and model-based unscented-particle-filter. The indicator of battery performance degradation is extracted for state-of-health estimation based on the measurable terminal voltage and electric current. Then, the least-square-support-vector-machine is implemented to provide direct and nonlinear mapping models for state-of-health and state-of-charge. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Unscented-particle-filter is utilized to take the least-square-support-vector-machine estimates as the temporal measurements for optimal state-of-health and state-of-charge estimation. The state-of-health correction in state-of-charge estimation achieves the joint estimation with different time scales. An experimental study on battery dynamic stress tests illustrates that the life cycle maximum state-of-charge estimation error is less than 2% and the root-mean-square-error of state-of-health estimation is less than 4%, which mean both state-of-charge and state-of-health can be estimated with high accuracy and robustness using the proposed hybrid joint state estimation method.

Keywords: Lithium-ion battery; Joint state estimation; State-of-charge; State-of-health; Statistical data-driven model fusion (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919320951
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:261:y:2020:i:c:s0306261919320951

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.2019.114408

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 ().