Load-responsive model switching estimation for state of charge of lithium-ion batteries

Xiaopeng Tang, Furong Gao, Changfu Zou, Ke Yao, Wengui Hu and Torsten Wik

Applied Energy, 2019, vol. 238, issue C, 423-434

Abstract: Accurately estimating state of charge (SoC) is very important to enable advanced management of lithium-ion batteries, however technical challenges mainly exist in the lack of a high-fidelity battery model whose parameters are sensitive to changes of the state and load condition. To address the problem, this paper explores and proposes a model switching estimation algorithm that online selects the most suitable model from its model library based on the relationship between load conditions for calibration and in practice. By leveraging a high-pass filter and the Coulomb counting, an event trigger procedure is developed to detect the estimation performance and then determine timely switching actions. This estimation algorithm is realized by adopting a gradient correction method for system identification and the unscented Kalman filter and H∞ observer for state estimation. Experimental results illustrate that the proposed algorithm is able to reproduce SoC trajectories under various operating profiles, with the root-mean-square errors bounded by 2.22%. The efficacy of this algorithm is further corroborated by comparing to single model-based estimators and two prevalent adaptive SoC estimators.

Keywords: Battery management system; Building energy storage system; State of charge estimation; Model switching (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.apenergy.2019.01.057

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