Online State-of-Health Estimation of VRLA Batteries Using State of Charge

Shahriari, Mehrnoosh; Farrokhi, Mohammad

doi:10.1109/tie.2012.2186771

Cited by 171 publications

(65 citation statements)

References 46 publications

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“…Notice that the nonlinearity is the inherent (1) The H ∞ method is employed to design a nonlinear observer with dynamic gain for the nonlinear second-order RC model, which is a powerful tool to restrict the effect of the non-Gaussian model and measurement noises on state estimation [32]. This implies that the proposed H ∞ -based nonlinear observer can achieve faster convergence and better robustness than the classic EKF [3,8,9]. In addition, for the existing H ∞ observer-based SOC estimation methods, the observer's gain in the work [21] is constant and difficult to calculate to adapt the nonlinear battery model.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

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This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H ∞ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF).

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Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

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“…Unlike these traditional BPNNs, [132] does not take into account the equivalent inner resistance of the battery and makes a single improvement to the weight adjustment algorithm of its BPNN that can significantly reduce the SoC estimation error. In [133] a simple radial basis ANN is used just to identify the ECM parameters and then, using SoC as one of the state-space variables and employing EKF, SoC is estimated. In [133] the inputs of the ANN are SoC and the current and voltage measured, and the output is the OCV.…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

“…In [133] a simple radial basis ANN is used just to identify the ECM parameters and then, using SoC as one of the state-space variables and employing EKF, SoC is estimated. In [133] the inputs of the ANN are SoC and the current and voltage measured, and the output is the OCV. A similar strategy is used in [43,[134][135][136], where, after modeling the battery system by an ANN model and a state space model as presented in Section 3, SoC is calculated using a dual EKF or other adaptive filter-based estimator.…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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“…In general, the battery cell ages due to its internal resistances and higher terminal voltages, and this degrades the usable battery capacity [24] even though all cells in a new battery pack show the same characteristics and aging initially. Moreover, the difference between SOC of cells accelerates the aging process of battery while resulting in the different maximum capacity of cell.…”

Section: Determination Of Optimal Reference Socmentioning

confidence: 99%

Optimal SOC Reference Based Active Cell Balancing on a Common Energy Bus of Battery

Bae

Park²,

Lee

2017

Journal of Electrical Engineering and Technology

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-This paper presents a study on the state-of-charge (SOC) reference based active cell balancing in real-time. The optimal references of SOC are determined by using the proposed active cell balancing system with the bidirectional DC/DC converters via the dual active bridge (DAB) type. Then, the energies between cells can be balanced by the power flow control of DAB based bidirectional DC/DC converters. That is, it provides the effective management of battery by transferring energy from the strong cell to the weak one until the cell voltages are equalized to the same level and therefore improving the additional charging capacity of battery. In particular, the cell aging of battery and power loss caused from energy transfer are considered. The performances of proposed active cell balancing system are evaluated by an electromagnetic transient program (EMTP) simulation. Then, the experimental prototype is implemented in hardware to verify the usefulness of proposed system.

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Online State-of-Health Estimation of VRLA Batteries Using State of Charge

Cited by 171 publications

References 46 publications

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Optimal SOC Reference Based Active Cell Balancing on a Common Energy Bus of Battery

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