State-of-Health Prediction of Lithium-Ion Batteries Based on Diffusion Model with Transfer Learning

An accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) is crucial to their safe and reliable. Although recently the data-driven methods have drawn great attention, owe to its efficient deep learning, it is worthwhile to continue devoting many efforts to prediction performance. In practice, fast charging mode has been widely applied in battery replenishing, which poses challenges for SOH prediction due to the diversity of charging conditions and electrochemical properties of LIBs; although, the process is stable and detectable. Furthermore, most previous data-driven prediction methods based discriminative model cannot describe the whole picture of the problem though sample data, affecting robustness of model in real-life applications. In this study, it is presented a SOH prediction model based on diffusion model, as an efficient new family of deep generative model, with time series information tackled through Bi-LSTM and the features derived from the voltage profiles in multi-stage charging process, which can identify distribution characteristics of training data accurately. The model is further refined by means of transfer learning, by adding a featured transformation from the base model for SOH prediction of different type LIBs. Two different types of LIBs datasets are used to evaluate the proposed model and the verified results revealed its better performance than those of other methods, reducing efforts required to collect data cycles of new battery types with the generality and robustness

Defects controlled hole doping and multivalley transport in SnSe single crystals

Knowledge of the electronic structure of group-IV monochalcogenides is essential for their application in high-performance thermoelectric energy harvesting. Here, using photoemission spectroscopy, the authors reveal the impact of doping, and the anisotropic nature of the band structure of SnSe