Electrolyte-assisted low-voltage decomposition of Li2C2O4 for efficient cathode pre-lithiation in lithium-ion batteries.

Lithium oxalate (Li2C2O4) is an attractive cathode pre-lithiation additive for lithium-ion batteries (LIBs), but its application is hindered by its high decomposition potential (>4.7 V). Due to the liquid-solid synergistic effect of the NaNO2 additive and the LiNi0.83Co0.07Mn0.1O2 (NCM) cathode material, the decomposition efficiency of micro-Li2C2O4 reaches 100% at a low charge cutoff voltage of 4.3 V. Our work boosts the widespread practical application of Li2C2O4 by a simple and promising electrolyte-assisted cathode pre-lithiation strategy.

Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries.

Accurate early detection of internal short circuits (ISCs) is indispensable for safe and reliable application of lithium-ion batteries (LiBs). However, the major challenge is finding a reliable standard to judge whether the battery suffers from ISCs. In this work, a deep learning approach with multi-head attention and a multi-scale hierarchical learning mechanism based on encoder-decoder architecture is developed to accurately forecast voltage and power series. By using the predicted voltage without ISCs as the standard and detecting the consistency of the collected and predicted voltage series, we develop a method to detect ISCs quickly and accurately. In this way, we achieve an average percentage accuracy of 86% on the dataset, including different batteries and the equivalent ISC resistance from 1,000 Ω to 10 Ω, indicating successful application of the ISC detection method.