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Title: Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method
Authors: Wei, Zhongbao
Leng, Feng
He, Zhongjie
Zhang, Wenyu
Li, Kaiyuan
Keywords: State of Health
State of Charge
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Wei, Z., Leng, F., He, Z., Zhang, W., & Li, K. (2018). Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method. Energies, 11(7), 1810-. doi:10.3390/en11071810
Series/Report no.: Energies
Abstract: The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation’s alertness and numerical stability so as to achieve an accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data–model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy.
ISSN: 1996-1073
DOI: 10.3390/en11071810
Rights: © 2018 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
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