• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.824-833
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• 2012

In this paper, a coordinated state-of-charge (SOC) control strategy for the energy storage system (ESS) operating under microgrid islanded mode to stabilize the frequency and voltage was proposed. The proposed SOC control loop is made up of PI controller, which uses a SOC state of the energy storage system as an input and an auxiliary reference value of secondary control as an output. The SOC controller changes the auxiliary reference value of secondary control to charge or discharge the ESS. To verify the proposed control strategy, PSCAD/EMTDC simulation study was performed. The simulation results show that the SOC of the ESS can be regulated at the desired operating range without degrading the stabilizing control performance by proposed coordinated SOC control method.

• Journal of IKEEE
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• v.18 no.3
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• pp.298-304
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• 2014

Proper operation of the battery powered systems depends on the accuracy of the battery SOC(State of Charge) estimation, therefore it is critical for those systems that SOC is accurately determined. The SOC of the battery is related to the battery aging and the SOC estimation methods without considering the aging of the battery are not accurate. In this paper, a new method that accurately estimate the SOC of the battery is proposed considering the aging of the battery. A mathematical model for the Battery SOC-OCV(Open Circuit Voltage) relationship is presented using Boltzmann equation and aging indicator is defined, and then the SOC is estimated combining the mathematical model and aging indicator. The proposed method takes the aging of the battery into consideration, which leads to an accurate estimation of the SOC. The simulations and experiments show the effectiveness of the proposed method for improving the accuracy of the SOC estimation.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.33-34
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• 2016

This paper presents a load sharing method based on the low bandwidth communication (LBC) applied to a DC microgrid in order to balance the state of charge (SOC) of the battery units connected in parallel to the common bus. In this method, SOC of each battery unit is transferred to each other through LBC to calculate average SOC value. After that, droop coefficients of battery units are adjusted according to the difference between SOC of each unit and average SOC value of all batteries in the system. The proposed method can effectively balance the SOC of battery units in charging and discharging duration with a simple low bandwidth communication system.

• Transactions on Electrical and Electronic Materials
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• v.18 no.3
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• pp.136-140
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• 2017

The state of charge (SOC) is an important parameter in a battery-management system (BMS), and is very significant for accurately estimating the SOC of a battery. Li-ion batteries boast of excellent performance, and can only remain at their best working state by means of accurate SOC estimation that gives full play to their performances and raises their economic benefits. This paper summarizes some measures taken in SOC estimation, including the discharge experiment method, the ampere-hour integral method, the open circuit voltage method, the Kalman filter method, the neural network method, and electrochemical impedance spectroscopy (EIS. The principles of the various SOC estimation methods are introduced, and their advantages and disadvantages, as well as the working conditions adopted during these methods, are discussed and analyzed.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.4
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• pp.344-350
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• 2015

A battery management system requires accurate information on the battery state of charge (SOC) to achieve efficient energy management of electric vehicle and renewable energy systems. Although correct SOC estimation is difficult because of the changes in the electrical characteristics of the battery attributed to ambient temperature, service life, and operating point, various methods for accurate SOC estimation have been reported. On the basis of piecewise linear (PWL) modeling technique, this paper proposes a simple SOC observer for lithium-polymer batteries. For performance evaluation, the SOC estimated by the PWL SOC observer, the SOC measured by the battery-discharging experiment and the SOC estimated by the extended Kalman filter (EKF) estimator were compared through a PSIM simulation study.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.126-131
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• 2015

When a battery is discharging, the battery's current and terminal voltage must both be measured to estimate its state of charge (SOC). If the SOC can be estimated by using only the current or voltage, hardware costs will decrease. This paper proposes an SOC estimation algorithm that needs to measure only the terminal voltage while a battery is discharging. The battery's SOC can be deduced from its open circuit voltage (OCV) through the relationship between SOC and OCV. But when the battery is discharging, it is not possible to measure the OCV due to the voltage drop in the battery's internal resistance (IRdrop). The proposed algorithm calculates OCV by estimating IRdrop using a dynamic terminal voltage measurement. This paper confirms the results of applying the algorithm in a hardware environment via algorithm binarization. To evaluate the algorithm, a Simulink battery model based on actual values was used.

• The Journal of Korean Institute of Information Technology
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• v.18 no.5
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• pp.55-64
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• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.1
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• pp.9-17
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• 2011

This paper provides, through the use of Hermite Interpolation, a new polynomial for Storage of Charge(SOC) solution of the low-power-battery. It also gives a general formula which permits direct and simple computation of coefficients of the proposed polynomial. From the simulation results based on real SOC, it is shown that this new approach is more accurate and computationally efficient than previous Boltzmann's SOC. This solution provides a new insight into the development of SOC algorithm.

• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.363-370
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• 2022

In this paper, an estimation algorithm for state of charge (SOC) was applied using an equivalent circuit model (ECM) and an Extended Kalman Filter (EKF) to improve the estimation accuracy of the battery system states. In particular, an observer was designed to estimate SOC along with the aged capacity. In the case of the fresh battery, when SOC was estimated by Kalman Filter (KF), the mean absolute percentage error (MAPE) was 0.27% which was smaller than MAPE of 1.43% when the SOC was calculated by the model without the observer. In the driving mode of the vehicle, the general KF or EKF algorithm cannot be used to estimate both SOC and capacity. Considering that the battery aging does not occur in a short period of time, a strategy of periodically estimating the battery capacity during charging was proposed. In the charging mode, since the current is fixed at some intervals, a strategy for estimating the capacity along with the SOC in this situation was suggested. When the current was fixed, MAPE of SOC estimation was 0.54%, and the MAPE of capacity estimation was 2.24%. Since the current is fixed when charging, it is feasible to estimate the battery capacity and SOC simultaneously using the general EKF. This method can be used to periodically perform battery capacity correction when charging the battery. When driving, the SOC can be estimated using EKF with the corrected capacity.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1038-1046
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• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.