With the research object of LiFePO 4 battery, this paper aims to correctly estimate the battery state of charge (SOC) by constructing a comprehensive SOC estimation strategy. Firstly, recursive least square (RLS) algorithm is adopted to realize online parameter identification of the equivalent battery model; and then an elaborate combination of RLS and Unscented Kalman Filter (UKF) is established, thus the battery model parameters used in UKF are actually obtained recursively by RLS; finally, SOC can be estimated by UKF. This strategy has an obvious adaptability due to the adoption of online parameter identification, so it is also called adaptive SOC estimation technique. Experimental results show that sometimes battery model parameters of different cells can be much different even though terminal voltages of these cells are very close or same when they are under resting state, and this inconsistency among LiFePO 4 batteries is captured by the RLS-UKF strategy presented in this paper; and of course battery SOC can also be correctly estimated by using the continuously updated model parameters.Keywords：LiFePO 4 battery; SOC estimation; online parameter identification; UKF IntroductionDuring recent several years, electric vehicle (EV) industry has been booming, and as the power source of the whole EV system, storage battery has drawn more and more attention.

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This paper presents an active islanding detection method based on injecting adaptive active current disturbance according to the amplitude of the voltage at the point of common coupling (PCC) detected after islanding occurred to reduce the disturbance on the inverter's output current. According to the amplitude of the voltage of PCC detected after islanding, the resistance value of the load can be obtained. Then the relationship between the minimum active disturbance current which can trigger islanding protection and the amplitude of PCC voltage is deduced. The required active disturbance current can be adaptively added to the inverter according to the amplitude of PCC voltage. As a result, the islanding can be detected after power failure without non-detection zone (NDZ). According to IEEE Std.1547.1 and IEEE Std.929-2000, the simulation results show that the proposed algorithm is correct and effective. The experiment results also show that compared to the constant and periodic disturbance methods, the disturbance on the inverter's output current is significantly reduced by using the proposed adaptive active current disturbance algorithm.

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