• Energy Research

Emerging energy storage systems based on abundant and cost-effective materials are key to overcome the global energy and climate crisis of the 21st century.

A major driving force for the development of electrochemical storage technology with high energy density is the electrification of the mobility sector. High expectations rest on the development of beyond Li-Ion battery systems [1]. Especially, lithium-sulfur batteries (Li/S) are believed to be a promising candidate already in the near future. Despite the increasing efforts to realize a large-scale commercialization several fundamental properties of Li/S batteries are still a matter of intensive research. Recently, several groups investigated the influence of surface properties at the positive electrode, like the affinity towards polysulfides or surface roughness, on the morphology and spatial distribution of solid charge and discharge products [2]–[4]. The results indicate that a spatial control of the formation of solid Li2S is able to improve battery performance, however, additional design guidelines are needed to optimize this approach. Simulations on the continuum scale give the opportunity to interpret the measurements, improve our understanding of relevant processes, and allow for an optimization of interface, electrode and, cell designs. In our contribution we will present simulation results of a detailed model describing the nucleation, growth, and dissolution of the solid end-products S8 and Li2S in Li/S batteries. In our calculations we simulate the dynamic evolution of the corresponding particle size distributions at various positions in the cell and take into account their effect on transport properties and active surface areas. The model is integrated in our framework for the simulation of metal-sulfur (Me/S) batteries which handles the reaction and transport of dissolved sulfur species on cell level [5]. This multiscale approach allows us to track the concentration and volume fraction of sulfur species during cycling and additionally gives the opportunity for a systematic study of the polysulfide shuttle. The simulated evolution of solid volume fractions and particle size distributions are in good qualitative agreement with data of X-ray in-operando measurements recently published in the literature [6] and correlate the morphological properties of the electrode to the electrochemical characteristics. In a next step the model will be used to investigate the influence of additional positive electrode materials with different specific surface area and/or affinity towards sulfur species in order to assess their impact on battery performance. This approach provides mechanistic insights on the operation of Li/S batteries and will contribute to the understanding and, therefore, improvement of next-generation batteries

The concept of a representative elementary volume (REV) is key for connecting results of pore-scale simulations with continuum properties of microstructures. Current approaches define REVs only based on their size as the smallest volume in a heterogeneous material independent of its location and under certain aspects representing the same material at the continuum scale. However, the determination of such REVs is computationally expensive and time-consuming, as many costly simulations are often needed. Therefore, presented here is an efficient, systematic, and predictive workflow for the identification of REVs. The main differences from former studies are: (1) An REV is reinterpreted as one specificsub-volume of minimal size at a certain location that reproduces the relevant continuum properties of the full microstructure. It is therefore called a local REV (lREV) here. (2) Besides comparably cheap geometrical and statistical analyses, no further simulations are needed. The minimum size of the sub-volume is estimated using the simple statistical properties of the full microstructure. Then, the location of the REV is identified solely by evaluating the structural properties of all possible candidates in a very fast, efficient, and systematic manner using a penalty function. The feasibility and correct functioning of the workflow were successfully tested and validated by simulating diffusive transport, advection, and electrochemical properties for an lREV. It is shown that the lREVs identified using this workflow can be significantly smaller than typical REVs. This can lead to significant speed-ups for any pore-scale simulations. The workflow can be applied to any type of heterogeneous material, even though it is showcased here using a lithium-ion battery cathode.

In this paper we present a model of the discharge of a lithium–oxygen battery with aqueous electrolyte. Lithium–oxygen batteries (Li–O2) have recently received great attention due to their large theoretical specific energy. Advantages of the aqueous design include the stability of the electrolyte, the long experience with gas diffusion electrodes (GDEs), and the solubility of the reaction product lithium hydroxide. However, competitive specific energies can only be obtained if the product is allowed to precipitate. Here we present a dynamic one-dimensional model of a Li–O2 battery including a GDE and precipitation of lithium hydroxide. The model is parameterized using experimental data from the literature. We demonstrate that GDEs remove power limitations due to slow oxygen transport in solutions and that lithium hydroxide tends to precipitate on the anode side. We discuss the system architecture to engineer where nucleation and growth predominantly occurs and to optimize for discharge capacity.

Most cathode materials for lithium-ion batteries exhibit a low electronic conductivity. Hence, a significant amount of conductive graphitic additives are introduced during electrode production. The mechanical stability and electronic connection of the electrode is enhanced by a mixed phase formed by the carbon and binder materials. However, this mixed phase, the carbon binder domain (CBD), hinders the transport of lithium ions through the electrolyte pore network. Thus, reducing the performance at higher currents. In this work we combine microstructure resolved simulations with impedance measurements on symmetrical cells to identify the influence of the CBD distribution. Microstructures of NMC622 electrodes are obtained through synchrotron X-ray tomography. Resolving the CBD using tomography techniques is challenging. Therefore, three different CBD distributions are incorporated via a structure generator. We present results of microstructure resolved impedance spectroscopy and lithiation simulations, which reproduce the experimental results of impedance spectroscopy and galvanostatic lithiation measurements, thus, providing a link between the spatial CBD distribution, electrode impedance, and half-cell performance. The results demonstrate the significance of the CBD distribution and enable predictive simulations for battery design. The accumulation of CBD at contact points between particles is identified as the most likely configuration in the electrodes under consideration.

The effect of the mixing and drying process on the microstructure of ultra‐thick NCM 622 cathodes (50 mg cm−2, 8 mAh cm−2) and its implication for battery performance is investigated. It is observed that the shear force during the mixing process significantly influences the resulting microstructure with regard to binder migration during the drying process. Based on the information extracted from scanning electron microscopy–energy dispersive X‐ray spectroscopy (SEM–EDX) cross sections, the carbon binder domain (CBD) is distributed in the pore space of virtual electrodes generated by a stochastic 3D microstructure model. Simulations predict a CBD configuration that leads to optimal performance of the electrode. Furthermore, it is shown that a low drying rate has a beneficial influence toward the rate capability of the ultra‐thick cathodes. The specific energy of an ultra‐thick cathode is 18% higher compared with a cathode prepared according to the state of the art. With an improved process in a pilot scale, the advantage can be kept up to current densities of at least 3 mA cm−².

Metal-air batteries are being investigated as alternative to state-of-the-art lithium-ion batteries for mobile and stationary applications due to their higher specific energy and potentially lower cost. Modeling and simulation techniques allow a better understanding and improvement of the complex mechanisms and properties of metal-air batteries. Here we present combined modeling and experimental results on a lithium-air (Li/O2) battery with aqueous alkaline (LiOH) electrolyte using three different modeling methodologies, (i) Lattice-Boltzmann simulations on the porous electrode scale, (ii) multi-physics continuum modeling on the single-cell scale and (iii) system simulation of a Li/O2-operated electric vehicle. Lattice-Boltzmann simulations on experimentally reconstructed Ag-based gas diffusion electrodes (GDE) are performed in order to derive multi-phase transport parameters, in particular, saturation/pressure relationships and effective diffusion coefficients. The computational domain of the 1D continuum model consists of the gas-diffusion electrode as cathode, a porous separator, and a lithium metal anode. The model includes a detailed description of the multi-step electrochemistry including dissolution of O2into the liquid electrolyte, oxygen reduction to hydroxyl ions, and nucleation and growth of the solid reaction product LiOH [1]. The model is validated with experimental half-cell measurements over a wide range of conditions. The multi-physics model is integrated into a system simulation of a battery electric vehicle, including electric engine and regenerative braking. The model is used to simulate driving cycles (Fig. 1), which allow to quantify practical energy and power densities and to investigate the potential of lithium-air technology as next-generation battery for electromobility. As key result, the alkaline lithium-air battery offers the interesting capability of high-power cycling using only liquid-phase dissolved intermediates (O2 + 4e– + 2 H2O ⇄ 4 OH–) coupled to high-energy content due to solid product formation (Li+ + OH– + H2O ⇄ LiOH·H2O). This dual functionality can be exploited for the driving cycle using model-based cell design. [1] B. Horstmann, T. Danner, and W. G. Bessler, “Precipitation in aqueous lithium-oxygen batteries: a model-based analysis,” Energy & Environmental Science 6, 1299–1314 (2013).