As one of the most appealing options for large-scale energy storage systems, the commercialization of aqueous zinc-ion batteries (AZIBs) has received considerable attention due to their cost effectiveness and inherent safety. A potential cathode material for the commercialization of AZIBs is the manganese-based cathode, but it suffers from poor cycle stability, owing to the Jahn–Teller effect, which leads to the dissolution of Mn in the electrolyte, as well as low electron/ion conductivity. In order to solve these problems, various strategies have been adopted to improve the stability of manganese-based cathode materials. Among those, the doping strategy has become popular, where the dopant is inserted into the intrinsic crystal structures of electrode materials, which would stabilize them and tune the electronic state of the redox center to realize high ion/electron transport. Herein, we summarize the ion doping strategy from the following aspects: (1) synthesis strategy of doped manganese-based oxides; (2) valence-dependent dopant ions in manganese-based oxides; (3) optimization mechanism of ion doping in zinc-manganese battery. Lastly, an in-depth understanding and future perspectives of ion doping strategy in electrode materials are provided for the commercialization of manganese-based zinc-ion batteries.

Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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