• 전기전자학회논문지
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• 제18권3호
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• pp.298-304
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• 2014

배터리를 사용하고 있는 시스템에서 배터리의 잔존 용량에 대한 정보는 매우 중요하며, 따라서 정확한 SOC(State of Charge)의 추정이 필요하다. 배터리는 노화됨에 따라 전체 사용 가능 용량이 줄어들고 성능이 떨어지는데 이러한 노화의 영향을 고려하지 않는 배터리의 SOC 추정 방법은 추정의 정확도가 떨어지는 단점이 있다. 따라서 본 논문에서는 배터리의 노화 상태를 고려하여 배터리의 SOC를 추정하는 새로운 방법을 제안한다. 제안한 방법에서는 배터리의 전압-SOC 특성 곡선을 Boltzmann 방정식을 사용하여 모델링하고 노화 지표를 정의하며, 노화 지표를 Boltzmann 방정식 모델과 결합하여 SOC를 추정한다. 따라서 제안한 방법은 배터리의 노화 상태를 SOC 추정에 반영하여 노화된 배터리에 대한 정확한 SOC 추정이 가능하다. 또한 새 배터리와 1년 사용한 배터리에 대한 실험과 시뮬레이션을 통하여 제안한 방법의 유효성을 확인한다.

• 전력전자학회논문지
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• 제20권4호
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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.

• 제어로봇시스템학회논문지
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• 제14권9호
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• pp.904-908
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• 2008

Every autonomous system like a robot needs a power source known as a battery. And proper management of the battery is very important for proper operation. To know State of Charge(SOC) of a battery is the very core of proper battery management. In this paper, the SOC estimation problem is tackled based on the well known Extended Kalman Filter(EKF). Combined the existing battery model is used and then EKF is employed to estimate the SOC. SOC table is constructed by extensive experiment under various conditions and used as a true SOC. To verify the estimation result, extensive experiment is performed with various loads. The comparison result shows the battery estimation problem can be well solved with the technique proposed in this paper. The result of this paper can be used to develop related autonomous system.

• 전력전자학회논문지
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• 제17권3호
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• pp.222-229
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• 2012

The SOC estimation method based on Kalman Filter(KF) requires the accurate battery model to express the electrical characteristics of the battery. However, the performance of KF SOC estimator can hardly be improved because of the nonlinear characteristic of the battery. This paper proposes the new KF SOC estimator of Lithium-Polymer Battery(LiPB), which considers the variation of parameters based on the hysteresis effect, the magnitude of SOC, the charging/discharging mode and the on/off load conditions. The proposed SOC estimation method is verified with the PSIM simulation combined the experimental data of the LiPB.

• 한국융합학회논문지
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• 제11권6호
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• pp.7-14
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• 2020

본 논문에서는 확장 칼만 필터를 이용한 SOC 추정이 가능한 배터리 모니터링 시스템을 개발하였다. 배터리의 충·방전 상태를 정확하게 추정하기 위해 배터리 셀을 테브닌 모델로 모형화하고, 모델에 맞는 파라미터를 추출하였다. 이를 이용하여 확장 칼만 필터 알고리즘을 이용한 SOC 추정이 가능한 배터리 모니터링 디바이스를 제작하였으며, 다중 배터리 모니터링 디바이스 제어 및 배터리 상태 측정이 가능한 모니터링 서버를 제작하였다. 또한, 관리자가 실시간으로 상태를 확인하며 배터리 모니터링 디바이스 제어가 가능한 웹 서비스를 제작하였다. 특히, 배터리 SOC를 각각의 배터리 모니터링 디바이스에서 추정하고, 최종 결과만 모니터링 서버로 전달함으로써 서버의 계산량을 줄일 수 있다.

• IEIE Transactions on Smart Processing and Computing
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• 제4권2호
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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.

• 전력전자학회논문지
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• 제23권2호
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• pp.106-111
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• 2018

In this work, we propose techniques for estimating the SOC of Li-air battery. First, we describe and explain the operation principle of the Li-air battery. Energy density of the Li-air battery was compared with that of the Li-ion battery. The capacity and impedance value of the fully discharged voltage is analyzed, and the OCV value for SOC estimation is measured through the electrochemical characterization of the Li-air battery. Estimation value is obtained by SOC modeling through extended Kaman filter and is compared with the measurement value from the Coulomb counting method. Moreover, the performance of SOC estimation circuit is evaluated.

• Journal of Electrochemical Science and Technology
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• 제13권3호
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• pp.323-338
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• 2022

Heat generation and temperature of a battery is usually presented by an equation of current. This means that we need to adopt time domain calculation to obtain thermal characteristics of the battery. To avoid the complicated calculations using time domain, 'state of charge (SOC)' can be used as an independent variable. A SOC based calculation method is elucidated through the comparison between the calculated results and experimental results together. Experiments are carried for rapid resistive discharge of a large-capacitive lithium secondary battery to evaluate variations of cell potential, current and temperature. Calculations are performed based on open-circuit cell potential (SOC,T), internal resistance (SOC,T) and entropy (SOC) with specific heat capacity.

• Journal of Power Electronics
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• 제12권1호
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• pp.40-48
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• 2012

This paper proposes the State-of-charge (SOC) estimator of a LiPB Battery using the Extended Kalman Filter (EKF). EKF can work properly only with an accurate model. Therefore, the high accuracy electrical battery model for EKF state is discussed in this paper, which is focused on high-capacity LiPB batteries. The battery model is extracted from a single cell of LiPB 40Ah, 3.7V. The dynamic behavior of single cell battery is modeled using a bulk capacitance, two series RC networks, and a series resistance. The bulk capacitance voltage represents the Open Circuit Voltage (OCV) of battery and other components represent the transient response of battery voltage. The experimental results show the strong relationship between OCV and SOC without any dependency on the current rates. Therefore, EKF is proposed to work by estimating OCV, and then is converted it to SOC. EKF is tested with the experimental data. To increase the estimation accuracy, EKF is improved with a single dominant varying parameter of bulk capacitance which follows the SOC value. Full region of SOC test is done to verify the effectiveness of EKF algorithm. The test results show the error of estimation can be reduced up to max 5%SOC.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 1998년도 학술발표회논문집
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• pp.177-180
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• 1998

To manage lead-acid battery efficiently and to use it longer in UPS, the state of charge(SOC) indicator of the battery is needed. So a new approach to developing battery SOC indicator for UPS is discussed in this paper. This method to determining SOC by combining the available data of discharge characteristics of a battery with neural networks(NN) is presented. The 3-layered NN with back propagation algorithm has been used. Exprement results show that the proposed method is appropriate as SOC indicator of the battery.