LAUSR

The battery performance decreases as the charging/discharging cycles increase. Thus, a battery management system (BMS) is essential to properly estimating the battery states. In order to enhance the performance of the BMS, an accurate estimation method for lithium-ion batteries state is proposed. The main drawback of the coulomb counting method (CCM) for estimating a state of charge (SoC) is the error of initial value. To make-up this problem, the open circuit voltage (OCV) method which includes the internal resistance of the battery has been applied to update the initial value. In this paper, an enhanced coulomb counting (ECC) method is proposed to improve the accuracy of SoC estimation. Due to the battery aging by repeated charging/discharging cycles, the charging/discharging times become reduced and it can be formulated as a function of coulombic efficiency. Using the power equation to the battery, the state of health (SoH) can be estimated according to the change in the internal resistance. In the proposed flowchart, after the completion of charging/discharging in the $k$ cycle, the internal resistance, coulombic efficiency, and capacities are calculated and those resultants will be utilized in $k+1$ cycle. The proposed methods are verified by 3 kW energy storage system and the comparative experiment results are also presented to point out its effectiveness.