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. Energies2018, 11, 1810.
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.
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. Energies2018, 11, 1810.
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.
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, the online model identification is scrutinized to achieve high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation alertness and numerical stability, so as to achieve 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 LIB. Simulation and experimental studies are performed to evaluate the performance of the proposed data-model fusion method. Results suggest that the proposed method can 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 accuracy and high stability.
Subject Areas
state of charge; state of health; model identification; estimation; lithium-ion battery
Copyright:
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.
Zhongbao Wei