Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi_0.86Co_0.11Al_0.03O₂-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi_0.83Co_0.11Mn_0.07O₂-based positive electrodes and batteries with the blend of Li(NiCoMn)O₂- Li(NiCoAl)O₂positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.

The current report describes the installation and the preliminary commissioning of the Material Science Powder Diffraction (MSPD) beamline at the Spanish synchrotron ALBA-CELLS. The beamline is fully dedicated to powder diffraction techniques and consists of two experimental stations positioned in series: a High Pressure/Microdiffraction station and a High Resolution/High Throughput powder diffraction station.

In recent years, the stability field of a monoclinic phase at the morphotropic phase boundary in lead zirconate titanate, $\mathrm{Pb}\left[{\mathrm{Zr}}_{1-x}{\mathrm{Ti}}_x\right]{\mathrm{O}}_3$, has been under discussion. In the present study, we investigated samples in the compositional range between $0.40⩽x⩽0.475$ and $x=0.55$ using high-resolution synchrotron x-ray diffraction in combination with transmission electron microscopy and electron paramagnetic resonance to correlate average structure and microstructural information. It is shown that the microstructure plays a crucial role in the analysis of diffraction data. The appearance of intensity in diffraction patterns formerly linked to a monoclinic phase [B. Noheda et al., Phys. Rev. B 61, 8687 (2000)] can directly be correlated to a miniaturization of the average domain structure of the material visible in the presence of nanodomains. The internal symmetry of the nanodomains is not necessarily monoclinic due to coherence effects in diffraction and is discussed with respect to martensitic theory.