• 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.

• 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%.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.241-248
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• 2021

A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.1
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• pp.27-32
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• 2011

These days more electric devices are implemented in car, and more accurate estimation of SoC is required. OCV with current integration and Internal Resistance is essential method of Battery SoC Estimation. In this paper we propose OCV with current integration method and compare with Internal Resistance method. In OCV with current integration method estimation error was less than average 2%, but requires more than 5 minutes to stabilize OCV. If Stop and Running conditions are change frequently, estimation error will increase. In Internal resistance Modeling method, in high SoC state, estimation error was more than 15%, and in low SoC state, estimation error was less than 8%.

• Proceedings of the KIPE Conference
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• 2007.11a
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• pp.60-62
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• 2007

The open circuit voltage (OCV) is widely used to estimate the state of charge (SOC) in many estimation algorithms. However, the relationship between the OCV and SOC can not be exactly same for all batteries. Because the conventional OCV-SOC differs between batteries, there is a problem that the relationship of the OCV-SOC should be measured to accurately estimate the SOC. Therefore, the conventional OCV-SOC is modified to a new relationship in this paper. Thus, problems resulting from the defects of the extended Kalman filter (EKF) can be avoided by preventing the relationship from varying. In this paper, SOC and capacity of the lithium-ion battery are estimated using the dual EKF with the proposed method.

• 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.528-529
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• 2014

본 논문은 리튬-이온 배터리의 OCV 예측기법에 대해서 제안하였다. OCV는 배터리의 SOC를 추정할 때 중요한 정보이다. 하지만, 정확한 OCV를 측정하기 위해서는 최소 30분 이상의 휴지시간이 소요된다는 단점을 가지고 있다. 따라서 본 논문은 이런 단점을 해결하기 위해 OCV 예측기법에 대해서 제안하였다. 제안한 OCV 예측기법의 타당성은 배터리 모델의 OCV와 예측된 OCV를 비교하는 시뮬레이션 통해 검증하였다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.445-446
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• 2019

전기적 등가회로의 모델의 정확도 향상을 위하여 정확한 내부 저항과 OCV의 반영은 필수적이며, 이를 위한 OCV 실험에서 SOC 구간을 작게 작을수록 OCV의 정확도는 향상되지만 실험시간은 증가한다. 따라서 실험 시간을 고려한 적당한 SOC(5%, 10%) 구간으로 실험을 진행하며, 측정 되지 않은 영역의 내부 파라미터는 선형보간법으로 등가회로 모델에 반영한다. 이러한 문제로, 본 연구는 SOC 추정에의 주요 인자인 OCV의 추정 기법으로 뉴럴 네트워크(Neural Network)를 사용하였다. 추정 방법은 뉴럴 네트워크로 기존 OCV 실험 데이터를 학습하여 모델을 구축한다. 학습 모델의 입력값으로 용량 실험 데이터의 전압, 전류를 적용하였고 결과로 얻은 SOC-OCV 곡선을 비교 분석하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.77-83
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• 2014

The Estimation of State of Charge(SOC) for batteries is an important aspect of a Battery Management System(BMS). A method for estimating the SOC is proposed in order to overcome the individual disadvantages of the current integral and Open Circuit Voltage(OCV) estimation methods by combining them using Extended Kalman filter(EKF). The non-linear characteristics of the Li-Ion RC battery model used in this study is also solved through EKF. The proposed method is simulated in a Matlab environment with a Li-Ion Kokam battery (3.7V, 1,500mAh). Results showed that there is an improvement in the estimation error when using the proposed model compared to the conventional current integral method.

• Journal of the Korean Vacuum Society
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• v.16 no.3
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• pp.227-230
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• 2007

In the Li ion battery fabrication process, an aging step has treated as a miner step because there is not so much data to define the relationship between the phenomena generated in aging process and the battery performances. However, the OCV(open circuit voltage) change in the aging process is shown by the electrochemical absorption of the electrolyte component to the both electrodes(anode or cathode) and the absorbed layer to the electrode affects to form the solid electrolyte interface(SEI) layer during the first charge process. In this report, the adsorbed materials are designed deliberately and are cleared to affect to the SEI layer formation.