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The lifespan of lithium-ion batteries varies enormously from fundamental study to practical applications. This big difference has been typically ascribed to the high degree of uncertainty in unpredictable and complicated operation conditions in real-life applications. Here, we report that the pause of the charging–discharging process, which is frequently operated in practice but rarely studied in academics, is an important reason for the performance degradation of the NCM111 cathode. It is found that the pause during cycling could trigger a remarkable drop in capacity, giving rise to ∼30% more capacity decay compared with the continuously cycled sample. In situsynchrotron X-ray diffraction analysis reveals that the harmful H1–H2 phase transition, which typically appears in the initial cycle but disappears in subsequent cycles, is reactivated by the pausing process. The anisotropic lattice strains that occur during the H1–H2 transition result in mechanical fractures that terminate with an inert NiO-type rock-salt phase on the surface of particles. The present study indicates that the discontinuous usage of rechargeable batteries is also a key factor for cycle life, which might provide a distinct perspective on the performance decay in practical applications.

A critical problem in the current simultaneous localization and mapping (SLAM) research is the matching estimation of multi-feature fusion, such as low feature matching accuracy; weak robustness; and poor timeliness. Therefore, this paper focuses on using edge features for assisted localization, which makes two important contributions to multi-feature fusion in autonomous driving scenarios. Firstly, based on inheriting the three-thread architecture of ORB-SLAM2, this paper uses point features to filter and determine new keyframes. Then a feature evaluation method based on map information is proposed, aiming to efficiently introduce edge compensation features and optimize the real-time processing speed of multi-feature based SLAM systems. Secondly, to optimize the accuracy of positioning by edge compensation features, this paper starts from two aspects: the description of angles and the description of length(endpoints) to further define and optimize the reprojection error. The algorithm is tested in the KITTI, an autonomous driving scene data set. And we made a quantitative analysis of the influence of optimization results on computing time and positioning accuracy. Compared to ORB-SLAM2, this system improves localization accuracy by an average of 15.1%, while saving computation time by an average of 26.9% more than PL-SLAM. The effectiveness of the proposed method was verified through simulation in a typical dataset and comparison with the state-of-the-art(SOTA) SLAM system.

Mitigating high-voltage diffusion kinetic hindrance enables 4.6 V LiCoO2extreme fast charging capability, which is ascribed to more homogenous Li-ion de/intercalation at high voltage under high-rate current charge/discharge process.