Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery

Ecker, Madeleine; Käbitz, Stefan; Laresgoiti, I.; Sauer, Dirk Uwe

doi:10.1149/2.0541509jes

Cited by 117 publications

(146 citation statements)

References 29 publications

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“…For battery electrolytes, concentrated solution theory holds, but the Einstein relation is considered as an approximation here. As comparison with values from literature, derived using concentrated solution theory, as well as the simulation validation in 12 show, this approximation seems to be justified for the material and the currents up to 5 C considered in this work. Further technics to characterize electrolytes using concentrated solution theory are described in.…”

Section: Model-parameterizationsupporting

confidence: 70%

“…The methods are summarized and discussed for effective parameter determination. The measured values can be found in summary in the second part of this publication, 12 where they are applied and validated in a physico-chemical model. The parameters have been compared with existing literature values.…”

Section: Discussionmentioning

confidence: 99%

“…The adjustment of SOC can also lead to deviations. Due to the uncertainties in the determination of this parameter, the activation energy of solid-state diffusion has been left as a free fitting parameter in the model (see 12 ). For the measurement of the activation energy of the diffusion coefficient only one state of charge was considered.…”

Section: Model-parameterizationmentioning

confidence: 99%

“…The intensity of charge transfer depends on the concentration of the reactants. For a lithium electrode with Li ↔ Li + + e − the exchange current density depends on the lithium concentration in the electrolyte c e , leading to: [12] For an insertion electrode with Li ↔ Li + + + e − , where represents the solid lattice, the exchange current density depends on the lithium concentration in the electrolyte c e , the lithium concentration in the solid lattice c s and the concentration of unoccupied sites in the lattice (c s,max -c s ) according to:…”

Section: Model-parameterizationmentioning

confidence: 99%

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Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery

Ecker

Tran

Dechent

et al. 2015

J. Electrochem. Soc.

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228

270

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The parameterization of a physico-chemical model constitutes a critical part in model development. Conclusions about the internal state of a battery can only be drawn if a correct set of material parameters is provided for the material combination under consideration. In this work, parameters to fully parameterize a physico-chemical model for a 7.5 Ah cell produced by Kokam are determined and are compared with existing literature values. The paper presents parameter values and procedures to determine the parameters. Cells were opened under argon atmosphere and the geometrical data were measured. Hg-porosimetry was conducted to determine porosity, particle radius as well as tortuosity of the electrodes and the separator. Conductivity and diffusion constants of the electrolyte as well as the electronic conductivity of the active material were measured detecting the voltage response to a dc current. Physico-chemical models are based on fundamental equations describing migration and diffusion processes as well as intercalation kinetics. They can be used to gain understanding of internal processes of batteries and to optimize material development. Several papers have been published developing physico-chemical simulation models that are based on the work of Newman and Tiedemann 1975, 1 amongst others. 2-6However, one crucial part of physico-chemical models is the model parameterization. Especially if conclusions about the internal state of the battery are drawn, it is of utmost importance to choose the right parameters for the materials under consideration. To the knowledge of the authors no work exists where a simulation model was completely parameterized using the geometric data and the parameters of the materials of one commercial cell in total. In most works dealing with physico-chemical models, values from supplementary literature sources were used; parameters were fitted or even guessed, e.g. [2][3][4][5][6] There are publications focusing on the determination of certain parameters for certain material combinations. Park et al. 7 for example gathered diffusion constants as well as conductivities investigated for different materials used in lithium-ion batteries in literature. Several authors also determined exchange currents for graphite materials. 8-11The problem here is that these parameters are usually not measured with the purpose to parameterize a battery model. This leads to measurement setups and finally to parameters that are not applicable in battery models. The exchange current, for example, is usually not scaled with the active surface area, which makes a transfer to a material with a different surface structure impossible. Another example is the determination of the electronic conductivity of active materials. The conductivity in literature is usually not measured for a whole electrode setup, including the filler, binder and porous structure. This makes it difficult to apply these values in models.Furthermore, there are parameters that have (to the knowledge of the authors) not yet been investigated ...

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Section: Model-parameterizationsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Model-parameterizationmentioning

confidence: 99%

Section: Model-parameterizationmentioning

confidence: 99%

See 2 more Smart Citations

Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery

Ecker

Tran

Dechent

et al. 2015

J. Electrochem. Soc.

Self Cite

228

270

View full text Add to dashboard Cite

show abstract

“…The exchanging charge between the layers inside the battery characterizes the recovery/redistribution effects during relaxation phases (e.g., [8], [9]). In order to account for the so-called charge redistribution, in [10] it has proposed to use a virtual current generator ( vir I ) which wave shape and behavior has been suitably defined to match the long-term charge diffusion in the battery.…”

Section: B Two-time Constants Model (Ttc)mentioning

confidence: 99%

An adaptive model-based real-time voltage control process for Active Distribution Networks using Battery Energy Storage Systems

Bahramipanah

Torregrossa

Cherkaoui

et al. 2016

2016 Power Systems Computation Conference (PSCC)

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Analysis of Lithium‐Ion Battery State and Degradation via Physicochemical Cell and SEI Modeling

Witt

Röder²,

Krewer

2022

Batteries & Supercaps

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The quality of lithium-ion batteries is affected by the formation of the solid electrolyte interphase (SEI). For a better understanding of its effect on cell performance and aging, fast and economically scalable SEI diagnostics are indispensable. Battery models promise to extract hardly accessible interfacial and bulk properties of the SEI from electrochemical impedance spectra and discharge data. The common analysis of only one measurement, often with empirical models, impedes a precise localization of degradation-related and performance-limiting proc-esses. This work offers a solution by combining physicochemical SEI and cell modeling for the joint analysis of both measurement types. Our analysis highlights the minor importance of the SEI ionic conductivity for cell performance along with a significant improvement and notable effect of its interfacial properties along aging. Such a detailed understanding of the initial SEI and its evolution over time could enable, e. g., a knowledge-based optimization of the cell formation process.

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Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery

Cited by 117 publications

References 29 publications

Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery

Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery

An adaptive model-based real-time voltage control process for Active Distribution Networks using Battery Energy Storage Systems

Analysis of Lithium‐Ion Battery State and Degradation via Physicochemical Cell and SEI Modeling

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