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Double perovskites exhibit low toxicity, intrinsic thermodynamic stability, and small carrier effective mass. Herein, a novel doping route was adopted to incorporate Mn ions into Cs2Na1–x Ag x BiCl6 double perovskites for engineering the band gap and tailoring the energy transfer. The as-prepared Cs2Na1–x Ag x BiCl6 (0 < x < 1) exhibited excellent photoluminescence and a broad self-trapped exciton (STE) band from 500 to 900 nm, which exhibited an abnormal emission peak blue shift with increasing temperature. For Mn-doped Cs2Na1–x Ag x BiCl6, the two photoluminescence (PL) bands from d–d transition emission of Mn ions and STEs were always observed simultaneously in the PL window. The distinct energy-transfer channel from the Mn2+ ion guest to the double-perovskite host resulted in the dominant Mn2+ emission. Our results will be helpful for further understanding the nature of the photophysics of double perovskites.

Summary To achieve accurate state‐of‐charge (SoC) estimation for LiFePO4 batteries, the effects of temperature, hysteresis, and thermal evolution are elaborately modeled. Open‐circuit voltage is regarded as the sum of electromotive force and hysteresis potential (Vh), where electromotive force is constructed as the function of SoC and temperature and Vh is reproduced with a geometrical model. By simulating battery heat generation and dissipation, a thermal evolution model is established and exploited for open‐circuit voltage and parameter identification. Then, on the basis of a second‐order equivalent circuit model, 2 SoC estimation schemes are proposed: One scheme uses the recursive least square with forgetting factor algorithm and off‐line equivalent circuit model parameters derived by the differential evolution algorithm; the other scheme resorts to the adaptive extended Kalman filter (EKF) and online tuned parameters. Experiments validate the effectiveness of the hysteresis model and the thermal evolution model. In contrast to a joint EKF estimator, experimental results under different temperatures and initial states suggest that both the proposed estimators are superior to the joint EKF estimator. Benefiting from the online updated parameters, the adaptive EKF estimator behaves best for giving consistent SoC‐tracking performance under different conditions.

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