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Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations. [ABSTRACT FROM AUTHOR]

Mesoporous silicon microspheres were fabricated via in situ magnesiothermic reduction of silicon oxide. Scanning and transmission electron microscopy images indicate that the diameter of the mesoporous silicon microspheres is approximately 500 nm. Mesopores with diameters ranging from 10 to 30 nm are homogeneously distributed in the silicon microspheres. The as-prepared mesoporous silicon microspheres show a reversible capacity of 3000 mAh g at 100 mA g discharge current density. After 60 cycles, the mesoporous silicon microspheres can still deliver a 1500 mAh g capacity at 1000 mA g discharge current density. The high reversible capacity, excellent cycling, and good rate capability of the fabricated material can be attributed to its mesoporous morphology and ultrafine particle size. [ABSTRACT FROM AUTHOR]

Abstract: Co3O4/graphene nanosheet (GNS) composites were synthesized via the in situ growth of mesoporous Co3O4 nanoparticles on graphene. The as-prepared Co3O4/GNS composites exhibited superior Li-ion battery performance and showed a large reversible capacity, excellent cycling, and good rate capability when used as an anode material in lithium ion batteries (LIBs). The uniform coating of Co3O4 around the graphene surface ensured tight electrical contact, resulting in a material with higher specific capacity and enhanced cycling performance compared with pure Co3O4 electrodes. The composites delivered a reversible capacity of 900mAhg−1 at a discharge current density of 100mAg−1 with good cycling ability. Moreover, the composites showed exceptional high-current charge–discharge performance, achieving 650mAhg−1 at a current density of 1000mAg−1. [Copyright &y& Elsevier]

Abstract: CuO nanoparticles are directly formed on graphene nanosheets through the in situ chemical decomposition of Cu(NO3)2·3H2O and are anchored tightly on the graphene surface. The lithiation-induced strain is naturally accommodated, owing to the constraint effect of the graphene matrix. Electrochemical characterization shows that CuO nanoparticles anchored on graphene sample exhibits a high capacity of about 660mAh/g at a discharge current density of 100mA/g and a good cycling ability. During the charge–discharge process, graphene nanosheets not only served as a three-dimensional conductive network for CuO nanoparticles, but also improve the detachment and agglomeration of CuO nanoparticles. This CuO/graphene nanocomposite displays superior Li-battery performance with large reversible capacity, excellent cyclic performance, and good rate capability. [Copyright &y& Elsevier]

Abstract: A MnO nanoparticle/graphene composite was prepared via in situ carbothermal reduction of Mn3O4 on the surface of graphene nanosheets. The formed MnO nanoparticles with diameters ranging from 20nm to 250nm integrated tightly with the graphene nanosheets. As anode material for lithium-ion batteries, the nanocomposite showed a high specific capacity of approximately 700mAhg−1 at 100mAg−1, excellent cyclic stability, and good rate capability. During the charge-discharge process, graphene nanosheets served as a three-dimensional conductive network for MnO nanoparticles. Furthermore, the detachment and agglomeration of MnO nanoparticles were effectively prevented due to the tight combination of MnO nanoparticles and graphene. [Copyright &y& Elsevier]

Graphene-SnS nanocomposites were prepared via a solvothermal method with different loading of SnS. The nanostructure and morphology of the samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The XRD patterns revealed that hexagonal SnS was obtained. SEM and TEM results indicated that SnS particles distributed homogeneously on graphene sheets. The electrochemical properties of the samples as active anode materials for lithium-ion batteries were examined by constant current charge-discharge cycling. The composite with weight ratio between graphene and SnS of 1:4 had the highest rate capability among all the samples and its reversible capacity after 50 cycles was 351 mAh/g, which was much higher than that of the pure SnS (23 mAh/g). With graphene as conductive matrix, homogeneous distribution of SnS nanoparticles can be ensured and volume changes of the nanoparticles during the charge and discharge processes can be accomodated effectively, which results in good electrochemical performance of the composites. [ABSTRACT FROM AUTHOR]