An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model

Xu Zhang, Yujie Wang, Duo Yang and Zonghai Chen

Energy, 2016, vol. 115, issue P1, 219-229

Abstract: Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and single cell is put forward to analyze the energy efficiency influenced by battery inconsistency, then a lumped parameter battery model is built up to describe the dynamic behavior of battery pack. Furthermore, the extend Kalman filter-unscented Kalman filter algorithm is developed to identify the parameters of battery pack and forecast state-of-charge concurrently. The extend Kalman filter is applied to update the battery pack parameters by real-time measured data, while the unscented Kalman filter is employed to estimate the battery pack state-of-charge. Finally, the proposed approach is verified by experiments operated on the lithium-ion battery under constant current condition and the dynamic stress test profiles. Experimental results indicate that the proposed method can estimate the battery pack state-of-charge with high accuracy.

Keywords: Battery pack model; Extend Kalman filter-unscented Kalman filter; State-of-charge; Battery inconsistency (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:115:y:2016:i:p1:p:219-229

DOI: 10.1016/j.energy.2016.08.109

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