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Energies 2018, 11(7), 1810; https://doi.org/10.3390/en11071810

Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

Energy Research Institute @ NTU, Nanyang Technological University, Singapore 637141, Singapore
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Received: 21 June 2018 / Revised: 8 July 2018 / Accepted: 9 July 2018 / Published: 11 July 2018
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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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. View Full-Text
Keywords: state of charge; state of health; model identification; estimation; lithium-ion battery state of charge; state of health; model identification; estimation; lithium-ion battery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wei, Z.; Leng, F.; He, Z.; Zhang, W.; Li, K. Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method. Energies 2018, 11, 1810.

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