The accurate and rapid estimation of the state of charge (SOC) is important and difficult in lithium battery management systems. In this paper, an adaptive infinite Kalman filter (AUKF) was used to estimate the state of charge for a 18650 LiNiMnCoO₂/graphite lithium-ion battery, and its performance was systematically evaluated under large initial errors, wide temperature ranges, and different drive cycles. In addition, three other Kalman filter algorithms on the predicted SOC of LIB were compared under different work conditions, and the accuracy and convergence time of different models were compared. The results showed that the convergence time of the AUKF algorithms was one order of magnitude smaller than that of the other three methods, and the mean absolute error was only less than 50% of the other methods. The present work can be used to help other researchers select an appropriate strategy for the SOC online estimation of lithium-ion cells under different applicable conditions.

Poly(ethylene oxide) (PEO)-based solid-state polymer electrolyte (SPE) is a promising candidate for the next generation of safer lithium-metal batteries. However, the serious side reaction between PEO and lithium metal and the uneven deposition of lithium ions lead to the growth of lithium dendrites and the rapid decline of battery cycle life. Building a LiF-rich solid electrolyte interface (SEI) layer is considered to be an effective means to solve the above problems. Here, porous organic polymers (POPs) with aromatic structures and non-aromatic structures were synthesized and introduced into the PEO-based SPE as fillers to explore the effect of aromatic structures on LiF-rich SEI formation. The results show that the POPs containing aromatic groups could catalyze the decomposition of LiTFSI to form a stable LiF-rich SEI layer and inhibit the growth of lithium dendrites. The discharge capacity of the LFP/Li battery is 103 mA h g⁻¹ after 500 cycles at 1C (100 °C). It provides a promising way to improve the stability of the solid electrolyte matrix and SEI layer.