Pseudo-2-dimensional models are routinely used to predict the lithiation curves for energy storage devices, including lithium-metal batteries. The performance of such models is as good as their parameterization, which remains a challenge especially in the presence of carbon binder domain (CBD). We propose two alternative parameterization strategies, which explicitly account for the CBD volume fraction and physical properties. The first aggregates CBD with the electrolyte-filled pore space and expresses the Bruggeman exponent in terms of a solution of microstructure-specific closure problem. The second treats CBD and active particles as a composite solid phase, whose effective properties are computed (semi-)analytically via homogenization. We show that the latter strategy used to parameterize the Doyle-Fuller-Newman model provides an attractive middle ground between the model complexity and the prediction accuracy. Our modeling results suggest that the battery discharge time decreases as either the CBD volume fraction increases or the CBD ionic diffusivity decreases, and is insensitive to the CBD ionic conductivity. The quantitative nature of these observations can be used in the optimal design of porous cathodes.

Dendritic growth of lithium (Li) metal is a leading cause of degradation and catastrophic failure of all-solid-state batteries (ASSBs) with Li anode. Insertion of a buffer layer between the Li metal and the solid electrolyte is known to ameliorate this phenomenon, yet the identification of an optimal buffer material, and the design of ASSBs that can be manufactured at scale, remains elusive and largely driven by trial-and-error experimentation. Our analysis seeks to accelerate the buffer-materials discovery by elucidating the conditions under which the buffer's presence stabilizes electrodeposition on the Li anode in ASSBs. The analysis quantifies the interfacial instability associated with dendrite formation in terms of the battery's operating conditions and the electrochemical and physical properties of the buffer material and solid electrolyte. The model predicts that, among several prospective buffer materials, Ag, Al, Sn, and antiperovskite super ionic conductor, Li3S(BF4)0.5Cl0.5, are effective in stabilizing electrodeposition and suppressing dendrite growth. Our model's predictions of the dendrite suppression abilities of different buffer materials are consistent with the published experimental findings. The model can be used to guide experimental and computational discovery of new buffer materials that match a particular electrolyte.

The ability to quantify evapotranspiration (ET) is crucial for smart agriculture and sustainable groundwater management. Efficient ET estimation strategies often rely on the vertical‐flow assumption to assimilate data from soil‐moisture sensors. While adequate in some large‐scale applications, this assumption fails when the horizontal component of the local flow velocity is not negligible due to, for example, soil heterogeneity or drip irrigation. We present novel implementations of the ensemble Kalman filter (EnKF) and the maximum likelihood estimation (MLE), which enable us to infer spatially varying ET rates and root water uptake profiles from soil‐moisture measurements. While the standard versions of EnKF and MLE update the predicted soil moisture prior to computing ET, ours treat the ET sink term in Richards' equation as an updatable observable. We test the prediction accuracy and computational efficiency of our methods in a setting representative of drip irrigation. Our strategies accurately estimate the total ET rates and root‐uptake profiles and do so up to two‐orders of magnitude faster than the standard EnKF.