• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.418-419
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• 2010

본 논문에서는 스크리닝에 기반한 리튬이온 배터리 팩의 state of charge (SOC) 추정방법을 연구하였다. 전기화학적 특성이 서로 유사한 셀들을 미리 선별하는 스크리닝 방법을 통해 직렬, 병렬, 직/병렬팩이 구성될 때, 이러한 팩의 전기화학적 등가회로 모델은 단위 셀 대비 일정한 경향성을 보이는 용량, open circuit voltage (OCV) 등의 파라미터 정보를 토대로 기존 단위 셀 모델과 동일한 모델 구축이 가능하다. 이를 통하여 extended kalman filter (EKF)를 이용한 배터리 팩의 SOC 추정이 가능함을 보인다.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.150-151
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• 2014

높은 SOC(state-of-charge) 추정알고리즘의 성능을 위해서는 측정된 배터리 단자전압의 정확도가 요구된다. 그렇지만, 예기치 않은 에러로 인해 단자전압에 노이즈 성분이 추가될 경우 SOC 추정성능의 저하를 피할 수 없다. 그러므로, 본 논문에서는 이산 웨이블릿 변환(DWT;discrete wavelet transform)의 다해상도 분석(MRA;multi resolution analysis)의 디노이징(denoising)기법을 적용한 이차전지의 SOC 추정방법을 소개한다. MRA의 시간-주파수 분석을 통해 분해(decomposition)된 저주파 성분(approximation;$A_n$)과 고주파 성분(detail;$D_n$)중 노이즈에 관계된 $D_n$의 고주파 상세 계수(detail coefficient) $d_{j,k}$를 새로이 조정하고 이를 합성(synthesis)하여 디노이징을 마무리 한다. 확장 칼만필터(EKF;extended Kalman filter)의 비교 분석을 통해 제안된 방법의 타당성을 검증한다.

• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.157-158
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• 2012

본 논문은 리튬 폴리머 배터리의 수명 감소에 대한 경향성 테스트를 토대로 이중 확장 칼만 필터(Dual EKF)를 이용하여 배터리의 SOC(State-of-Charge) 및 SOH(State-of-Charge) 방법을 제안하였다. 배터리에 수명에 따른 임피던스 변화를 테스트를 수행함으로써 등가회로 모델상에서 수명에 따른 변화가 가장 큰 내부 저항을 선택함으로써 배터리의 SOH 추정을 위해 선택하였다. 배터리 모델은 4.2V, 1440mAh의 리튬폴리머 전지에서 추출되었다. 배터리는 Bulk 커패시터, 두 개의 R-C회로, 직렬 저항을 사용하여 모델링하였다. Dual EKF를 모델에 적용하기 위해 캐패시터 전압은 개방 회로 전압(OCV)을 나타내는데 사용된다. Dual EKF는 충/방전 기기인 TOSCAT-5200에 의해 얻은 실험 데이터로 테스트하였다.

• Journal of Power Electronics
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• v.17 no.2
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• pp.398-410
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• 2017

This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate ($LiFePO_4$) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.321-324
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• 2008

This paper presents the battery management system(BMS) for the optimum conditions of the lead-Acid battery in UPS. The proposed system controls the over and under currents of battery for protecting and it was applied algorithm for optimum conditions to estimate the State Of Charge(SOC) in charge or discharge mode. It approved the performance and the algorithm for the estimation of SOC, through the experiments which using the charge and discharge tester and the field tests.

• Proceedings of the KIPE Conference
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• 2014.11a
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• pp.60-61
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• 2014

Lithium-Ion batteries have become the best tradeoff between energy, power density and cost of the energy storage system in many portable high electric power applications. In order to manage the battery efficiently State of Charge (SOC) of the battery needs to be estimated accurately. In this paper a model-based approach to estimate the SOC of the Lithium-Ion battery based on the estimation of the battery impedance is proposed. The validity and feasibility of the proposed algorithm is verified by the experimental results.

• Journal of Power Electronics
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• v.12 no.1
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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.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.401-402
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• 2014

State of Charge (SOC) and State of Health (SOH) are the key issues for the application of Absorbent Glass Mat (AGM) type battery in Idle Start Stop (ISS) system which is popularly integrated in Electric Vehicles (EVs). However, battery parameters strongly depend on SOC, current rate and temperature and significantly change over the battery life cycles. In this research, a novel method for SOC, SOH estimation which combines the Auto Regressive with external input (ARX) method using for online parameters prediction and Dual Extended Kalman Filter (DEKF) algorithm considering hysteresis is proposed. The validity of the proposed algorithm is verified by the simulation and experiments.

• Journal of Power Electronics
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• v.10 no.2
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• pp.181-186
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• 2010

This paper presents a new method for the estimation of State of Charge (SOC) for NiMH batteries. Among the conventional methods to estimate SOC, Coulomb Counting is widely used, but this method is not precise due to error integration. Another method that has been proposed to estimate SOC is by using a measurement of the Open Circuit Voltage (OCV). This method is found to be a precise one for SOC estimation. In NiMH batteries, the hysteresis characteristic of OCV is very strong compared to other type of batteries. Another characteristic of NiMH battery to be considered is that the OCV of a NiMH battery under discharging mode is lower than it is under charging mode. In this paper, the OCV is modeled by a simple method based on a hyperbolic function which well known as Takacs’s model. The OCV model is then used for SOC estimation. Although the model is simple, the error is within 10%.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.75-76
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• 2016

The relationship of widely used Open circuit Voltage (OCV) versus State of Charge (SOC) is critical for any reliable SOC estimation technique. However, the hysteresis existing in all type of battery which has been come to the market leads this relationship to a complicated one, especially in Lithium Iron Phosphate (LiFePO4) battery. An accurate model for hysteresis phenomenon is essential for a reliable SOC identification. This paper aims to investigate and propose a method for hysteresis modeling. The SOC estimation is done by using Extended Kalman Filter (EKF), the parameter of the battery is modeled by Auto Regressive Exogenous (ARX) and estimated by using Recursive Least Square (RLS) filter to tract each element of the parameter of the model.