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

Abstract

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 ...

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

“…It is investigated whether parameters obtained from coin cell setups 23 can be transferred to the original system. The simulations are compared with validation experiments performed at different temperatures.…”

“…The final parameter that is adjusted in order to obtain a good agreement between simulation and experiment is the activation energy of solid state diffusion. As discussed in, 23 uncertainties occurred in the determination of the activation energy of solid state diffusion depending on the method (i.e. EIS and GITT).…”

Multi-Scale Thermo-Electrochemical Modeling of Performance and Aging of a LiFePO4/Graphite Lithium-Ion Cell

“…The large number of required parameters poses a particular challenge to physically-based battery modeling. 72 We use the following parameterization approach: (1) Use literature values on the identical or similar cells and components for an initial set of model parameters; (2) use macroscopic experimental data, in particular electrical and thermal behavior upon charge and discharge, to re-parameterize selected performance-sensitive parameters, in particular, rate coefficients (preexponential factors the charge-transfer reactions) and thermal parameters (heat transfer coefficient); (3) use macroscopic aging data from literature (capacity as function of calendaric aging time) to parameterize the rate coefficients of the aging reaction (pre-exponential factor, activation energy); (4) test the model against macroscopic experimental data over a wide range of conditions (charge/discharge rates from 0.1 C to 10 C, temperatures, SOC). Parameter identification was performed by manually varying values (automated fitting was not possible due to high computational time of the simulation).…”

Reducing Inhomogeneous Current Density Distribution in Graphite Electrodes by Design Variation

“…[26][27][28][29][30] This modeling class was chosen for its accuracy in describing transport phenomena in the solid and liquid phase of a single electrode stack.…”

“…[26][27][28][29][30] This modeling class was chosen for its accuracy in describing transport phenomena in the solid and liquid phase of a single electrode stack. 31 As the P2D model is extensively discussed in literature, we only show the modifications to the basic model and included a short summary with all relevant parameters in the Appendix.…”