• 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 Electrical Engineers
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• v.66 no.2
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• pp.292-299
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• 2017

In this paper, multiple ESS(Energy Storage System) control strategy for microgrids is presented. Installation of ESS becomes mandatory when microgrids are used to supply high quality power to the loads. The one of main functions of the ESS is to maintain power balance. However ESS has limitation of its capacity and instantaneous injecting power. Power allocation method based on SOC(State Of Charge) of each ESS is proposed. P-Q control is employed as the basic control strategy for the distributed ESSs. By using the proposed method, the coefficients in the conventional P-Q control method are modified. The ESSs with higher SOC inject more active power, while those with lower SOC inject less, leading to more balanced SOC levels among the ESSs. The proposed method is demonstrated by simulation using PSCAD/EMTDC.

• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.271-272
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• 2012

전류 적산법(Coulomb counting, ampere counting)을 이용한 배터리 SOC(State-of-Charge) 추정 방법은 상용화된 IC를 사용할 수 있기에 구현이 간단하고 SOC 정의를 통해 배터리 사용 가능한 시간을 쉽게 예측할 수도 있다. 하지만 초기 SOC 오류와 누적되는 전류 정보의 오차로 인해 추정이 실패하는 단점이 존재하기 때문에 이를 해결해주는 알고리즘이 필요하다. 본 논문에서는 전류 적산법 기반의 배터리 SOC 추정 회로에 확장 칼만 필터(EKF, Extended Kalman Filter)를 접목하여 전류 적산법을 이용하였을 때 나타날 수 있는 오차 누적을 줄이는 알고리즘을 제안한다. 또한 실험을 통해 제안된 배터리 SOC 추정 회로의 성능을 확인해본다.

• 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.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.54-55
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• 2015

본 논문에서는 리튬-이온(Lithium-ion) 폐배터리 효율적인 재활용을 위한 발전된 SOC 추정방법의 필요성과 간단한 개념을 언급하고자 한다. 배터리는 노화되면 용량이 줄어들고 임피던스의 크기가 증가해 기존의 새 배터리의 SOC 추정방법으로는 정확한 추정이 어렵다. 따라서, 폐배터리를 안전하고 효율적으로 사용하기 위해서는 그에 맞는 SOC 추정방법이 필요하다. 따라서, 폐배터리의 간단한 개념을 설명하고, 동일한 배터리 등 가회로모델과 EKF 알고리즘을 적용한 새 리튬-이온 셀과 노화된 리튬-이온셀의 SOC 추정결과를 비교하고 노화에 따른 배터리 파라미터값의 변화를 분석해봄으로서 발전된 SOC 추정방법의 필요성에 대해 논의해보고자 한다.

• Journal of Ecology and Environment
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• v.45 no.1
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• pp.40-53
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• 2021

The level of soil organic carbon (SOC) fluctuates in different types of forest stands: this variation can be attributed to differences in tree species, and the variables associated with soil, climate, and topographical features. The present review evaluates the level of SOC in different types of forest stands to determine the factors responsible for the observed variation. Mixed stands have the highest amount of SOC, while coniferous (both deciduous-coniferous and evergreen-coniferous) stands have greater SOC concentrations than deciduous (broadleaved) and evergreen (broadleaved) tree stands. There was a significant negative correlation between SOC and mean annual temperature (MAT) and sand composition, in all types of forest stands. In contrast, the silt fraction has a positive correlation with SOC, in all types of tree stands. Variation in SOC under different types of forest stands in different landscapes can be due to differences in MAT, and the sand and silt fraction of soil apart from the type of forests.

• 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.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.7-14
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• 2020

A Battery Monitoring System capable of State-of-Charge(SOC) estimation using the Extended Kalman Filter(EKF) is described in this paper. In order to accurately estimate the SOC of the battery, the battery cells were modeled as the Thevenin equivalent circuit model. The Thevenin model's parameters were measured in experiments. For the Battery Monitoring System, we designed a battery monitoring device that can calculate the SOC estimation using the EKF and a monitoring server that controls multiple battery monitoring devices. We also develop a web-based dashboard for controlling and monitoring batteries. Especially the computation of the monitoring server could be reduced by calculating the battery SOC estimation at each Battery Monitoring Device.

• Environmental Engineering Research
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• v.18 no.3
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• pp.151-156
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• 2013

Several soil properties were studied from three young created mitigation wetlands (<10 years old), which were hydrologically comparable in the Piedmont region of Virginia. The properties included soil organic matter (SOM), soil organic carbon (SOC), pH, gravimetric soil moisture, and bulk density ($D_b$). No significant differences were found in the soil properties between the wetlands, except SOM and SOC. SOM and SOC indicated a slight increase with wetland age; the increase was more evident with SOC. Only about a half of SOC variability found in the wetlands was explained by SOM ($R^2$ = 0.499, p < 0.05). The majority of the ratios of SOM to SOC for these silt-loam soils ranged from 2.0 to 3.5, which was higher than the 1.724 Van Bemmelen factor, commonly applied for the conversion of SOM into SOC in estimating the carbon storage or accumulation capacity of wetlands. The results may caution the use of the conversion factor, which may lead to an overestimation of carbon sequestration potentials of newly created wetlands. SOC, but not SOM, was also correlated to $D_b$, which indicates soil compaction typical of most created wetlands that might limit vegetation growth and biomass production, eventually affecting carbon accumulation in the created wetlands.

• Journal of IKEEE
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• v.24 no.1
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• pp.333-338
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• 2020

This paper proposes a method of estimating the SOC(State of Charge) of a battery cell using a neural network algorithm. To this, we implement a battery SOC estimation simulator and derive input and output data for neural network learning through charge and discharge experiments at various temperatures. Finally, the performance of the battery SOC estimation is analyzed by comparing with the experimental value by Ah-counting using Matlab/Simulink program and confirmed that the error rate can be reduced to less than 3%.