A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles

Rui Xiong, Fengchun Sun, Hongwen He and Trong Duy Nguyen

Energy, 2013, vol. 63, issue C, 295-308

Abstract: An accurate SoC (state of charge) and SoP (state of power capability) joint estimator is the most significant techniques for electric vehicles. This paper makes two contributions to the existing literature. (1) A data-driven parameter identification method has been proposed for accurately capturing the real-time characteristic of the battery through the recursive least square algorithm, where the parameter of the battery model is updated with the real-time measurements of battery current and voltage at each sampling interval. (2) An adaptive extended Kalman filter algorithm based multi-state joint estimator has been developed in accordance with the relationship of the battery SoC and its power capability. Note that the SoC and SoP can be predicted accurately against the degradation and various operating environments of the battery through the data-driven parameter identification method. The robustness of the proposed data-driven joint estimator has been verified by different degradation states of lithium-ion polymer battery cells. The result indicates that the estimation errors of voltage and SoC are less than 1% even if given a large erroneous initial state of joint estimator, which makes the SoP estimate more accurate and reliable for the electric vehicles application.

Keywords: Electric vehicles; Lithium-ion polymer battery; Data-driven; Adaptive extended Kalman filter; State of charge (SoC); State of power capability (SoP) (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (61)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544213008761
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:63:y:2013:i:c:p:295-308

DOI: 10.1016/j.energy.2013.10.027

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 ().