This work presents a novel technique which is simple yet effective in estimating electric model parameters and state-of-charge (SOC) of the LiFePO4 battery. Unlike the well-known recursive least-squares-based algorithms with single constant forgetting factor, this technique employs multiple adaptive forgetting factors to provide the capability to capture the different dynamics of model parameters. The validity of the proposed method is verified through experiments using actual driving cycles.

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MgB2 polycrystalline bulk samples with additions of 0, 5, 10 and 20% wt.% nano-sized BN powders were prepared using the reaction in-situ method. All the samples were sintered at 850°C for 1h in Ar. All the samples were characterized by X-ray diffraction, scanning electron microscopy (SEM) and magnetic measurements. The X-ray diffraction patterns show that the BN does not react with Mg and B during the heat treatment and remains as a separate phase. The synthesized materials thus contain two separate BN and MgB2 phases. In addition, the samples contain a small, almost constant amount of MgO. SEM shows that the samples contain MgB2 grains with average grain sizes of about 250 nm. Magnetic measurement results show that the critical current density and irreversibility fields decrease slightly as the BN level increases. The Tc drops slightly from 38.9 to 38.2 K and has a sharp transition with a transition width of less than 1 K. The field dependence of Jc for all the samples is also presented.