Tutorial EN12: Emerging Tools for Battery Technologies—Cost, Materials and Performance Analysis and Prediction

Monday, November 29, 2021
8:30 AM - 12:00 PM

This tutorial session reviews several emerging tools for the cost, materials and performance analysis and prediction for battery applications. The two select tutorials cover the capabilities, the major features, and examples of the use of these tools.

Stefano Passerini will discuss the use of a battery performance and cost model for estimating the performance and manufacturing cost of lithium-ion and sodium-ion batteries. Giuliana Materzanini will discuss a computational tool/database for the prediction of materials properties for battery applications.

A Cost and Resource Analysis of Lithium/Sodium-Ion Batteries 
Stefano Passerini, Helmholtz Institute Ulm, Karlsruhe Institute of Technology

In this tutorial, Passerini will review the use of a Battery Performance and Cost model to undertake a cost analysis of the materials for sodium-ion and lithium-ion cells, as well as complete batteries, and determine the effect of exchanging lithium with sodium, as well as the effect of replacing the material used for the anode current collector foil, on the cost. Moreover, he will compare the calculated production costs of exemplary sodium-ion and lithium-ion batteries and highlight the most relevant parameters for optimization. Finally, the major raw materials for lithium-ion cathodes will be examined in terms of potential supply risks because supply issues may lead to increased costs. Through the use of a scenario-based supply and demand analysis, the risks to the supply of lithium and cobalt will be assessed, and implications for battery research will be discussed.

Ab Initio
Techniques in Li-Ion Battery Materials—Addressing Ionic Diffusion 

Giuliana Materzanini, Université Catholique de Louvain

In the last two decades, Li-ion batteries have proven to offer some of the best performance among the existing electrochemical energy storage technologies, striving to firm variable renewable generation and mass-market full electrification. A Li-ion battery cell exploits the redox activities of the anode and cathode to generate electricity outside the cell while reversibly intercalating Li ions between the two electrodes through an ionic conducting electrolyte. The difference in electrochemical potential between the electrodes is the thermodynamical driving force of the cell, while its rate capability is governed by the kinetics of the ion transport in the electrodes and in the electrolyte. Ab initio techniques, relying on the knowledge of the physical laws without the need of experimental inputs, provide insight onto the materials' electronic and structural properties, and shed light onto the dynamical processes that underlie the battery performance. Calculation of intercalation potentials, prediction of electrochemical, chemical and phase stabilities, study of dissolution and diffusion phenomena in the electrolytes and electrodes, and characterization of mechanical properties are a few examples of the various applications of the state-of-the-art ab initio approaches for the modelling of battery materials. In this tutorial, we provide an overview of the ab initio methods to study ionic diffusion in battery materials. First, we discuss the simulation approaches to probe the dynamic evolution of a system of atoms via the quantum mechanical potential, namely the Born-Oppenheimer and the Car-Parrinello first-principles molecular dynamics (FPMD) techniques. Thus, the extraction of the diffusion coefficients from the sampled trajectories according to the Green-Kubo relation is described, together with the role of ionic correlations in these systems (tracer/charge diffusion coefficient), and the use of the Nernst-Einstein equation to compute the ionic conductivity from the diffusion. To conclude, we present few benchmark cases from the recent literature and we discuss intrinsic 1 limitations together with recently proposed solutions, as the use of ab initio molecular dynamics in conjunction with machine learning, to give reliable ambient temperature diffusion coefficients that can be directly compared with the experiments.

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