• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.13-14
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• 2015

본 논문은 추정 정확도를 높이기 위해 비례적분 관측기를 이용한 실시간 잔존용량 추정 알고리즘을 제안한다. 시뮬레이션을 통해 제안하는 알고리즘의 타당성을 검증하였고, 초기 잔존 용량이 불명확하거나 배터리 모델 파라미터 값이 실제와 일치하지 않더라도 평균 추정오차는 0.3% 미만으로 확인되었다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.453-454
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• 2019

본 논문은 ESS(Energy Storage System)의 과충전, 과방전으로 인한 열 폭주 현상을 방지하기 위한 사전 연구로 원통형 리튬이온 단일 셀의 충/방전에 따른 열 분포를 열화상 카메라로 촬영하여 분석하였다. 실험을 통한 열 분포 이미지를 학습 데이터로 구성하여, SOC(State of Charge)를 추정하는 CNN(Convolution Neural Network) 모델을 제안한다.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.29-36
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• 2007

The purpose of this paper is the assessment of the capacity of the reinforced concrete (RC) elements of an arch bridge when they are subjected to contact and near-contact explosive charges of various amounts, and the estimation of the critical charges for these components. The bridge considered is the Tenza Viaduct, a decommissioned structure south of Naples, Italy. Its primary elements, deck, piers and arches were analyzed. The evaluation was accomplished via numerical analyses that made possible to obtain the elements dynamic response when they are exposed to blast loading conditions. To evaluate the member's capacities, failure criteria for deck, piers and arches were proposed based on concrete damage parameters. Additionally, curves relating the explosive charge to the residual capacity and to damage level of the elements were also developed. The results of this work were taken into account to investigate the progressive collapse of the global structure.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.6
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• pp.457-465
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• 2009

Lithium batteries are widely used in mobile electronic devices due to their higher voltage and energy density, lighter weight and longer life cycle compared to other secondary batteries. In particular, high demand for lithium batteries is expected for electric cars. In case of lithium batteries used in electric cars, driving distance must be calculated accurately and discharging should not be done below the level of making it impossible to crank. Therefore, accurate information about state of charge (SOC) becomes an essential element for reliable driving. In this paper, a new method of estimating the SOC of lithium polymer batteries by using AC impedance is proposed. In the proposed method, parameters are extracted by fitting a curve of impedance measured at each frequency on the equivalent impedance model and extracted parameters are used to estimate SOC. Experiments were conducted on lithium polymer batteries with similar capacities made by different manufacturers to prove the validity of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.15-23
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• 2017

This paper reports a SOC(State-of-Charge) estimation method using the extended Kalman filter(EKF) algorithm, which can allow real-time implementation and reduce the error of the model and be robust against noise, to accurately estimate and evaluate the charging/discharging state of the EV(Electric Vehicle) battery. The battery was modeled as the first order Thevenin model for the EKF algorithm and the parameters were derived through experiments. This paper proposes the changed method, which can have the SOC to 0% ~ 100% regardless of the aging of the battery by replacing the rated capacity specified in the battery with the maximum chargeable capacity. In addition, This paper proposes the EKF algorithm to estimate the non-linearity interval of the battery and simulation result based on Ah-counting shows that the proposed algorithm reduces the estimation error to less than 5% in all intervals of the SOC.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1140-1147
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• 2012

This paper proposes the power management technology for stand-alone PV system to extend installation environment and coverage. The proposed power management technology in this paper can protect battery safeness from overcharge/discharge with keeping the proper SOC(State of Charge) and extend using time of system through estimation of operating power. The proposed power management technology in this paper is applied to Infra-free Variable Message Sign. And performance of power management technology in this paper was verified using simulation scenario.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.26-34
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• 2016

Military devices and systems powered by batteries need to operate at extreme temperature and estimate the available capacity of the battery at different temperature conditions. However, accurate estimation of battery capacity is challenging due to the temperature-sensitive nature of electrochemical energy storage. In this paper, Peukert's equation with temperature factor is derived, and methods for estimating the absolute capacity of lithium-polymer battery and the state-of-charge(SOC) with respect to varying currents and temperatures are presented. The proposed estimation method is experimentally verified under three different discharge currents(0.5 A, 1 A, 3 A) and six different temperatures ranging from -30 to 45 deg. C. The results show the proposed method reduces the Peukert's estimation error by up to 30 % under or at extreme condition.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.79-81
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• 2019

전기자동차(EV)뿐만 아니라 ESS(Energy Storage System) 등의 사용량이 증가하면서 리튬이온배터리의 중요성은 점점 커지고 있다. 리튬 이온 배터리의 정확한 상태를 추정하는 것은 배터리의 안전하고 신뢰성 있는 작동을 위해 매우 중요하다. 본 논문에서는 AEKF(Adaptive Extended Kalman Filter)를 이용한 배터리 파라미터와 충전상태(SOC, State of Charge)를 추정하고, 이를 활용하여 배터리의 건강상태(SOH, State of Health)를 추정하는 간단한 알고리즘을 제시한다. AEKF에 파라미터 값을 적용하여 SOC를 추정하고, 추정된 SOC값과 전류 적산을 이용하여 SOH를 추정한다. SOC 오차에 따른 SOH 추정 값의 편차는 SOC 연산 간격을 늘리고 가중치 필터를 적용하여 최소화시킴으로써 결과의 정확성을 향상했다. 다양한 자동차의 표준 주행 패턴을 적용한 실험을 통해 제안된 방법을 이용하여 얻어진 SOH 추정 결과는 RMSE(Root Mean Square Error) 1.428% 이내임을 검증하였다.