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The accurate estimation of state of charge (SOC) under various conditions is critical to the research and application of batteries, especially at extreme temperatures. However, few studies have examined the SOC estimation performance of estimation algorithms for several types of batteries under such conditions. In this study, a new method was derived for SOC estimation and a series of experiments were conducted covering five types of lithium-ion batteries with three kinds of cathode materials (i.e., LiFePO4, Li(Ni0.5Co0.2Mn0.3)O2, and LiCoO2), three test temperatures, and four real driving cycles to verify the proposed method. The test temperatures for battery operation ranges from −20 to 60 °C. Then, an adaptive machine learning (ML) framework based on the deep temporal convolutional network (TCN) and Coulomb counting method was proposed, and the structure of the estimation model was designed through the Taguchi method. The accuracy and generalizability of the proposed method were evaluated by calculating the estimation errors and their standard deviations (SDs), its average errors showed a decline of at least 49.66%, and its SDs showed a decline of at least 45.88% when compared to four popular ML methods. These traditional ML methods performed poor accuracy and stability at extreme temperatures (−20 and 60 °C) when compared to 25 °C, while the proposed adaptive method exhibited stable and high performances at different temperatures. [ABSTRACT FROM AUTHOR]

The practical usage of high‐energy lithium metal batteries remains a great challenge mainly due to the formation of uneven lithium deposition and huge volume fluctuation on the anode side, which increases the potential risks of a cell short‐circuit. Herein, a nanodiamond‐Li composite anode (ND‐Li) is created using a facile thermal infusion strategy, which can mechanically confine active Li‐metal in the ND matrix and minimize the electrode volume change during electrochemical cycling with high areal capacities. The geometrically restrictive ND matrix with ultrahigh modulus can deform the uneven lithium deposition mechanically and confine the deposition to the ND matrix. Owing to the 3D rigid skeleton, low stripping/plating overpotential (≈60 mV) can be achieved for a ND‐Li anode under a high current density of 10 mA cm−2, whereas the overpotential of a bare Li foil anode is almost seven times larger than the ND‐Li matrix. When a ND‐Li anode pairs with a S cathode, a high capacity of 607.3 mAh g−1 at 1 C and stable long‐term cyclability over 500 cycles can be achieved. [ABSTRACT FROM AUTHOR]