Fast formation of anode-free Li-metal batteries by pulsed current.

Anode-free Li-metal batteries offer high energy density but are prone to dendrite formation during charging which can cause catastrophic failures. Ensuring dendrite-free smooth Li deposits during charging is therefore necessary. Suppressing dendrite growth can be achieved by pulsed current charging, especially during the formation cycle that largely determines the corrosion trajectory of a cell. As opposed to the constant-current technique, pulsed current techniques apply intermittently stopped current flows. This work investigates the electroplating of metallic Li onto a Cu foil current collector under constant-current and pulsed current formation protocols. In addition to smoother, less resistive electroplated metallic Li deposits and increased Coulombic efficiency, we show that by employing an optimized pulsed current formation protocol, the formation process is accelerated by a factor of 2 and the Coulombic efficiency was increased by 10% compared to a C/20 protocol. Finally, by employing a simple regression coupled to experimentation, we propose the pseudo-IR-drop to be used for live adjustment of pulsed current protocols i.e., individually approach each cell at all SOC during formation.

Conductivity experiments for electrolyte formulations and their automated analysis.

Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6). The mass ratio of EC to PC was varied, while keeping the mass ratio of (EC + PC) and EMC at fixed values of 3:7 and 1:1. The conducting salt concentration was also varied during the study. Conductivity data was obtained from electrochemical impedance spectroscopy (EIS) measurements at various temperatures. Based on the thus obtained temperature series, the activation energy for ionic conduction was determined during the analysis. The data is presented here in a machine-readable format and includes a Python package for analyzing temperature series of electrolyte conductivity according to the Arrhenius equation and EIS data. The data may be useful e.g. for the training of machine learning models or for reference prior to experiments.