Computational understanding of Li-ion batteries

Abstract

Over the last two decades, computational methods have made tremendous advances, and today many key properties of lithium-ion batteries can be accurately predicted by first principles calculations. For this reason, computations have become a cornerstone of battery-related research by providing insight into fundamental processes that are not otherwise accessible, such as ionic diffusion mechanisms and electronic structure effects, as well as a quantitative comparison with experimental results. The aim of this review is to provide an overview of state-of-the-art ab initio approaches for the modelling of battery materials. We consider techniques for the computation of equilibrium cell voltages, 0-Kelvin and finite-temperature voltage profiles, ionic mobility and thermal and electrolyte stability. The strengths and weaknesses of different electronic structure methods, such as DFT+U and hybrid functionals, are discussed in the context of voltage and phase diagram predictions, and we review the merits of lattice models for the evaluation of finite-temperature thermodynamics and kinetics. With such a complete set of methods at hand, first principles calculations of ordered, crystalline solids, i.e., of most electrode materials and solid electrolytes, have become reliable and quantitative. However, the description of molecular materials and disordered or amorphous phases remains an important challenge. We highlight recent exciting progress in this area, especially regarding the modelling of organic electrolytes and solid-electrolyte interfaces.

Chemomechanical modeling of lithiation-induced failure in high-volume-change electrode materials for lithium ion batteries

“…These experimental data provide insights for establishing physically based models. In parallel to these experiments, models of different length scales have been established, ranging from first-principles simulations, [25][26][27][28][29][30][31][32][33][34][35][36] molecular dynamics with empirical force fields on the atomic scale, [37][38][39][40][41][42] continuum-level simulations that couple field equations dictating Li transport and mechanical equilibrium. 9,12,[43][44][45][46][47][48][49][50][51][52][53][54][55] Together, this has opened a bottom-up avenue for developing high-performance LIBs, in contrast to the top-down approach adopted by the conventional battery development.…”

High-Throughput Exploration of Evolutionary Structural Materials

“…Dies ermöglicht computergestütz-tes Materialdesign mit DFT-Modellen [7][8][9]. Heute sind Dünn-schicht-Hochdurchsatz-Techniken ein verbreitetes Versuchswerkzeug für die Entwicklung von Funktionswerkstoffen [10][11][12].…”

“…(1). Sollten sie nicht zusammenpassen, werden das Residuum des Vergleichs (6) und die Prädiktorfunktion (7) verwendet, um neue Zusammensetzungen und Behandlungsparameter (8) in der nächsten Evolutionsstufe vorzuschlagen. Um die Prädiktorfunktion zu definieren, welche mikroskopische Deskriptoren auf makroskopischen Werknanoindentation measurements was possible as well.…”

“…The predicted material properties can then be compared to the performance profile (1). If both do not match, the residual of the comparison (6) and the predictor function (7) are used to propose new compositions and treatment parameters (8) in the next evolution step. To define the predictor function which maps microscopic descriptors to macroscopic material properties, a small number of macroscopic samples (9) is required.…”

“…In contrast to structural materials, the properties of functional are mainly governed by their atomic structure alone, enabling computational materials design by DFT models [7][8][9]. Today, thin film high throughput techniques are a common experimental tool for the development of functional materials [10][11][12].…”