An Enhanced Battery Aging Model Based on a Detailed Diffusing Mechanism in the SEI Layer

Abstract

The impetus for this study is the lack of a detailed knowledge on the formation mechanism of the solid electrolyte interface (SEI) layer and the diffusion mechanisms within this layer that impact the predictive abilities of the current mathematical models. Specifically, most models continue to employ a constant value of diffusion coefficient along with several lumped fitting parameters, instead of a variable formulation that is dependent on the temperature and concentration of Li-ions, to characterize Li-ion batteries. As a result, the current models fail in predicting the capacity fading accurately. In overcoming this gap, we have employed a previously proposed temperature and concentration-dependent diffusion equation to present a modified mathematical model that is capable of accurately predicting the capacity fading and SEI growth rate as a function of temperature, concentration, and time, with just two significantly simplified temperature dependent fitting parameters. Further, these parameters need to be adjusted only for new temperatures. Our enhanced model is validated with respect to the experimental data for different operational conditions, including open circuit condition with different initial state of charges (SOCs) as well as cycling with a constant current.

AI-Based Nano-Scale Material Property Prediction for Li-Ion Batteries

“…The status quo for techniques used in the discovery of new and novel materials to enhance battery technologies has progressed from expensive and time-consuming empirical trial and error methods to the more recent first principles approach of using quantum mechanics (QM) [5][6][7][8][9], Monte Carlo simulations and molecular dynamics (MD) [10][11][12][13][14]. QM calculations evaluate electron-electron interactions bby solving the complex Schr ödinger equation, thereby enabling accurate results for a wide variety of properties.…”

An Enhanced Ageing Model for Solid-State Batteries

“…Addressing this gap, this work aims at developing a model for solid-state batteries where a detailed diffusion equation is incorporated into the macro-scale model. The mathematical formulation presented in this work for ASSBs is inspired by our recent proposition for the liquid electrolyte-based LIBs [23]. More precisely, the model proposed in this work is an adaptation of the one developed by Ekström and Lindbergh [24], i.e., a macro-scale continuum mathematical model that quantifies the influence of SEI layer on the ageing of LIBs with a graphite anode material.…”

A computational framework for evaluating molecular dynamics potential parameters employing quantum mechanics

“…On the other hand, the MD approach reduces the complexity by simplifying the interactions between particles to just five main types of interactions, namely, (I) nonbonded: van der Waals, Coulombic, and polarization interactions between two particles, (II) bonded: repulsion and attraction of the bond electron pair between two atoms in a bond, (III) angle: interactions between bond pair and valence electrons in two neighboring bonds, (IV) dihedral: bond pair electron interactions in a sequential series of three bonds (four atoms), and (V) improper: interactions between bond electrons among three bonds connected to a single atom. 11–13 A simple algebraic equation is required for each interaction to estimate the system energy. For instance, nonbonded interactions between two atoms, ignoring the polarization, can be obtained by the Buckingham and Coulomb potential equation , where A , B , and C are constant coefficients called potential parameters, q i is the partial charge of the i th particle, and k is Coulomb's constant.…”