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Compared with the existing battery model parameter identification method, this study proposes a new online estimation method and which can estimate the battery open-circuit voltage in different sampling intervals with high accuracy.
The salient feature of the proposed method is that SOH of the battery is estimated automatically at the end of each discharging cycle by measuring the battery voltage and current of a battery unit, so that no complicated measurement is required.
Requirements vary somewhat for each type of battery, but a typical voltage monitoring system should meet specifications similar to those listed below.
The proposed method can track and correct the open-circuit voltage against capacity in the battery management system by comparing the capacity error with the coulomb counting and look-up table methods.
The sample entropy calculated from discharge voltage curve can serve as an indicator for assessing the condition of battery.
In this paper, a computationally feasible two-step estimation approach is proposed that only uses voltage and current measurements of the battery under consideration.
The comparison results indicate that the SPM-EKF approach can obtain a reliable battery voltage response and a more accurate SoC estimation than other approaches.
The proposed method utilizes the direct voltage measurements from the battery cells, and does not require any additional hardware or effort in modeling during fault detection.
Open circuit voltage is often used to estimate SOC of the battery, unfortunately, it is not easy to measure usually, and the hysteresis effect is a big challenge.
The comparison of measured and simulated voltage profiles indicates an excellent performance of the battery model.

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