The practical application of lithium–sulfur batteries (LSBs) is limited by the shuttle effect of lithium polysulfides (LiPSs), large volume expansion, and sluggish conversion kinetics of sulfur. Herein, the crystallinity regulation of NixFey alloy anchored on oxidized carbon nanotube/nitrogen‐doped graphene (NixFey@OCNT/NG) for application of a functional separator into LSBs is demonstrated. A low crystalline NixFey@OCNT/NG (LC‐NixFey@OCNT/NG) modified polypropylene separator is systematically compared with its highly crystalline counterpart (HC‐NixFey@OCNT/NG), demonstrating superior LiPS absorbability, redox mediating capability into facilitated conversion kinetics, and uniform flux of Li+ into the anode. Furthermore, theoretical calculations confirm that the LC‐NixFey alloy features high adsorption energies and low diffusion energy barriers toward LiPSs, as well as a decreased energy gap and larger electron density near Fermi level. Accordingly, the LSB cells with LC‐NixFey@OCNT/NG modified separators deliver a high specific capacity of 1379.13 mA h g−1 at 0.1 C and a low decay ratio of 0.04%/cycle over 600 cycles at 5.0 C with a high capacity of 410 mA h g−1. Even under high sulfur loading (5.37 mg cm−2) and lean electrolyte (E/S = 4.9 µL mg−1) conditions, the LSB cells with LC‐NixFey@OCNT/NG/PP deliver a high areal capacity of 4.1 mAh cm−2 at 0.2 C.

High-capacity electrode materials have been investigated to overcome the low energy density of electrochemical capacitors, but there are still issues arising from the trade-off between charge storage capacity and kinetics, efficiency, and stability. Herein, we describe multivalent sulfur redox chemistry for the high power and energy efficiency of hybrid energy storage full cells, where nitrogenincorporated nanoporous carbon/nanosulfur (N-NC/nS) and lithium manganese oxide are configured into negative and positive electrodes, respectively, using water-in-bisalt (WIBS)-soaked poly(acrylic acid) hydrogel electrolyte. As confirmed by the major contribution of surface redox capacity to the total capacity, low activation energy, high exchange current density, and fast charge transfer, the N-NC/nS achieves facile surface redox kinetics arising from the hierarchical porosity, nanoscale confinement of nS, and high ionic conductivity of WIBS hydrogels. The resulting full cells deliver capacitor-like high power density of 15.7 kW kg −1 , along with an energy density of 30.1 Wh kg −1 , 78.7% retention over 2000 cycles, and an energy efficiency of 98%.

Functional separators, which are chemically modified and coated with nanostructured materials, are considered an effective and economical approach to suppressing the shuttle effect of lithium polysulfide (LiPS) and promoting the conversion kinetics of sulfur cathodes. Herein, we report cobalt−aluminum-layered double hydroxide quantum dots (LDH-QDs) deposited with nitrogen-doped graphene (NG) as a bifunctional separator for lithium−sulfur batteries (LSBs). The mesoporous LDH-QDs/ NG hybrids possess abundant active sites of Co 2+ and hydroxide groups, which result in capturing LiPSs through strong chemical interactions and accelerating the redox kinetics of the conversion reaction, as confirmed through X-ray photoelectron spectroscopy, adsorption tests, Li 2 S nucleation tests, and electrokinetic analyses of the LiPS conversion. The resulting LDH-QDs/NG hybridcoated polypropylene (LDH-QDs/NG/PP) separator, with an average thickness of ∼17 μm, has a high ionic conductivity of 2.67 mS cm −1 . Consequently, the LSB cells with the LDH-QDs/NG/PP separator can deliver a high discharge capacity of 1227.48 mAh g −1 at 0.1C along with a low capacity decay rate of 0.041% per cycle over 1200 cycles at 1.0C.

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We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cannot be achieved using conventional methods such as electrochemical impedance spectroscopy. The battery state can be predicted based on the change in the capacity according to the state of health. For the proposed method, a statistical analysis of capacity fade considering the impedance increase according to the degree of deterioration is conducted by applying a deep neural network to diverse data from charge/discharge characteristics. Then, probabilistic predictions based on the capacity fade trends are obtained to improve the prediction accuracy of the remaining useful life using another deep neural network.