Abstract

In this paper, we propose a practical and accurate SOC (State of Charge) estimation system for Lithium- ion battery. The algorithm of SOC estimation uses the Extended Kalman filter, and estimates the SOC using OCV-SOC Curve, internal impedance, and the external current and voltage of a battery.12288; It is constructed on a discrete-time system model of battery model using numerical analysis method, and employs a SOC-OCV curve using simple polynomial function. Also, it provides a noise tuning method by using test discharge experiments. The new EKF technique pulls essential power of EKF and implements more accurate and stable estimation. This means that the accurate SOC estimation can be executed with a larger time step and less complex computation. Consequently, the accurate calculation does not need expensive computer. It is sufficient by an inexpensive microcomputer. Computation time for each EKF time step (1 s) was 4 ms.

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