Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling

André, Dave; Meiler, M.; Steiner, K.; Walz, Hannes; Soczka‐Guth, Thomas; Sauer, Dirk Uwe

doi:10.1016/j.jpowsour.2010.07.071

Cited by 431 publications

(207 citation statements)

References 6 publications

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“…This assumption is also consistent with the low dispersion found in the parameters of the Lithium-Ion cells when dealing with the traditionally used low-frequency models [16], [17]. It is also easily perceived the expected inductive behavior of the cells on the high frequency region.…”

Section: Battery Cells High Frequency Modelsupporting

confidence: 85%

Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

Garcı́a

Saeed

Navarro-Rodríguez

et al. 2018

IEEE Trans. on Ind. Applicat.

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Abstract-This paper is focused on establishing a procedure for measuring the efficiency dependency on the switching frequency for a solid state transformer (SST), being one of the ports connected to an energy storage device (Lithium-Ion battery). Multiple contributions for measuring the efficiency/losses for different power converter structures for energy storage applications can be found in the literature. However, there are few references which consider the effects of the high frequency model of the battery in the complete system performance. This research will obtain a parametric high frequency model of the battery cells, based on a vector fitting method in frequency domain. This model will be used for the estimation of the overall system losses. It will be demonstrated that the contribution of the battery losses, as well as its behavior as a function of the switching frequency, can significantly affect the selection of the converter's switching frequency.

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Section: Battery Cells High Frequency Modelsupporting

confidence: 85%

Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

Garcı́a

Saeed

Navarro-Rodríguez

et al. 2018

IEEE Trans. on Ind. Applicat.

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“…Subsequently the impedance curves are fitted to a variety of model structures by using the nonlinear complex least square (NCLS) optimization method. The EIS technique affords a precise impedance measurement in a wide band of frequencies but only for low current values [13], [16].…”

Section: B Frequency Domain Identification Methodsmentioning

confidence: 99%

“…To achieve this goal new elements have been introduced in the frequency domain such as the constant phase element (CPE), the Zarc element, and the Warburg element. These elements do not have a Laplace transformation, thus it is not possible to have a representation of them in time domain without approximations [13].…”

Section: Equivalent Circuit Models With Zarc and Warburg Elementsmentioning

confidence: 99%

“…[13] shows also a comparison between an equivalent circuit model with RLC elements and an equivalent circuit model using Zarc and Warburg elements by applying different test profiles to the cell at different temperatures. Subsequently the measured output voltage is compared to the calculated output of both models.…”

Section: Equivalent Circuit Models With Zarc and Warburg Elementsmentioning

confidence: 99%

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Evaluation of Equivalent Circuit Diagrams and Transfer Functions for Modeling of Lithium-Ion Batteries

Rahmoun¹,

Biechl²,

Rosin

2013

Electrical, Control and Communication Engineering

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-The rapid developments in the field of electrochemistry, enabled lithium-ion batteries to achieve a very good position among all the other types of energy storage devices. Therefore they became an essential component in most of the modern portable and stationary energy storage applications, where the specific energy and the life time play an important role. In order to analyze and optimize lithium-ion batteries an accurate battery model for the dynamic behavior is required. At the beginning of this paper four different categories of electrical models for li-ion cells are presented. In the next step a comparison between equivalent circuit diagrams and fractional rational functions with the complex variable s is shown for lithium-ion battery modeling. It is described how to identify the parameters of the models in the time domain and also in frequency domain. The validation of the different models is made for high and low dynamic current profiles. In the first step the dependency of all model parameters on the temperature and on the battery age is neglected. These effects will be taken into account in the continuation of this work.

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“…Considerable research has been carried out to develop new techniques to monitor battery SOH however many are not suitable for in-operando use as they require expensive equipment such as electrochemical impedance spectroscopy (EIS) [7,8], in-situ nuclear magnetic resonance [9] or X-ray computational tomography [10,11]. Data mining techniques such as extended Kalman filters [12], neural network and linear prediction error methods [13] have also been researched for SOH estimation in a BMS.…”

Section: Introductionmentioning

confidence: 99%

Extending battery life: A low-cost practical diagnostic technique for lithium-ion batteries

Merla

Yufit

et al. 2016

Journal of Power Sources

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Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling

Cited by 431 publications

References 6 publications

Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

Evaluation of Equivalent Circuit Diagrams and Transfer Functions for Modeling of Lithium-Ion Batteries

Extending battery life: A low-cost practical diagnostic technique for lithium-ion batteries

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