Tailoring Electric Double Layer by Cation Specific Adsorption for High-voltage Quasi-solid-state Lithium Metal Batteries.

The interfacial instability of high-nickel layered oxides severely plagues practical application of high-energy quasi-solid-state lithium metal batteries (LMBs). Herein, a uniform and highly oxidation-resistant polymer layer within inner Helmholtz plane is engineered by in-situ polymerizing 1-vinyl-3-ethylimidazolium (VEIM) cations preferentially adsorbed on LiNi0.83Co0.11Mn0.06O2 (NCM83) surface, inducing formation of anion-derived cathode-electrolyte interphase with fast interfacial kinetics. Meanwhile, the copolymerization of [VEIM][BF4] and vinyl ethylene carbonate (VEC) endows P(VEC-IL) copolymer with the positively-charged imidazolium moieties, providing positive electric fields to facilitate Li+ transport and desolvation process. Consequently, the Li||NCM83 cells with a cut-off voltage up to 4.5 V exhibit excellent reversible capacity of 130 mAh g-1 after 1000 cycles at 25 °C and considerable discharge capacity of 134 mAh g-1 without capacity decay within 100 cycles at -20 °C. This work provides deep understanding on tailoring electric double layer by cation specific adsorption for high-voltage quasi-solid-state LMBs.

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.