Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile

Kebede, Abraham Alem; Hosen, Sazzad; Kalogiannis, Theodoros; Behabtu, Henok Ayele; Jemal, Towfik; Mierlo, Joeri Van; Coosemans, Thierry; Berecibar, Maitane

doi:10.3390/en15186497

Cited by 2 publications

(3 citation statements)

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“…The model consists of an ideal voltage source (U OC ), which represents the open-circuit voltage of the battery, the internal resistance (R i ) of the battery, and two RC circuits. The time constants of these RC circuits differ by several orders of magnitude [23]. An RC circuit with a smaller time constant represents a fast response of the cell voltage, the influence of which can be perceived immediately after the onset of the current pulse.…”

Section: Lithium-ion Cell Modelmentioning

confidence: 99%

“…According to [16], the model from Figure 1 can be simplified so that only the dominant zero in the numerator of the transfer function is used, and the equivalent transfer function Ge(s) with one zero and two poles is obtained: The time constants of these RC circuits differ by several orders of magnitude [23]. An RC circuit with a smaller time constant represents a fast response of the cell voltage, the influence of which can be perceived immediately after the onset of the current pulse.…”

Section: Lithium-ion Cell Modelmentioning

confidence: 99%

“…Electrical (ECM) model of a lithium-ion cell. RC circuits represent the dynamics of polarization and diffusion between electrodes, where: R 1 -SEI layer resistance [Ω] C 1 -SEI layer capacity [F] R 2 -the battery's cell element resistance [Ω] C 2 -the internal battery's cell capacity [F]The time constants of these RC circuits differ by several orders of magnitude[23]. An RC circuit with a smaller time constant represents a fast response of the cell voltage, the influence of which can be perceived immediately after the onset of the current pulse.…”

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confidence: 99%

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A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

Radaš,

Matić,

Šunde

et al. 2024

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This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method.

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Section: Lithium-ion Cell Modelmentioning

confidence: 99%

Section: Lithium-ion Cell Modelmentioning

confidence: 99%

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confidence: 99%

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