• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.214-219
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• 2010

This paper deals with a nonlinear fuzzy $H_{\infty}$ filter design for SOC(state of charge) estimation in Lithium polymer battery. The dynamic equation of the battery cell is modeled as a T-S fuzzy system and the filter is designed via solutions of linear matrix inequalities. In order to illustrate the performance of the designed filter, a computer simulation is performed using the experimental data with the UDDS(urban dynamometer driving schedule) current profile.

• Journal of Power Electronics
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• v.9 no.3
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• pp.472-480
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• 2009

In this paper, a charge equalization converter with parallel-connected primary windings of transformers is proposed. The proposed work effectively balances the voltage among Lithium-Ion battery cells despite each battery cell has low voltage gap compared with its state of charge (SOC). The principle of the proposed work is that the equalizing energy from all battery strings moves to the lowest voltage battery through the isolated dc/dc converter controlled by the corresponding solid state relay switch. For this research a prototype of four Lithium-Ion battery cells is optimally designed and implemented, and experimental results show that the proposed method has excellent cell balancing performance.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.129-130
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• 2018

리튬 이온 배터리가 전기 자동차 및 다양한 어플리케이션에 적용됨에 따라 배터리 관리 시스템(BMS)의 중요도가 높아지고 있다. 리튬 이온 배터리의 SOC(State of Charge) 및 단자전압 추정은 BMS에서 필수적이며 다양한 알고리즘을 통해 연구되고 있다. 본 논문에서는 비지도 학습 알고리즘인 뉴럴 네트워크의 학습을 위해 특성 파라미터(Characterstic Parmeter)를 선정하였으며, 특성 파라미터의 학습을 통해 리튬 이온배터리의 단자 전압 및 SOC를 추정하였다.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.121-122
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• 2018

리튬 인산철(LFP, $LiFePo_4$) 배터리의 경우 다른 종류의 배터리에 비해 내부 파라미터가 비선형적인 단점이 있다. 일반적인 배터리 등가회로 모델을 적용 시, 비선형성으로 인해 추정 성능이 감소한다. 배터리 등가회로 모델을 기반인 확장 칼만 필터(EKF, Extended Kalman Filter)를 통해 SOC (State of Charge) 추정 시 추정성능이 감소할 수 있다. 따라서 본 논문은 LFP 배터리의 SOC 추정 성능 향상을 위해 실시간 파라미터 관측기를 통한 배터리 등가회로 모델을 기반으로 EKF의 내부 파라미터를 분석하고 이에 따른 차등 모델을 제안한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.142-149
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• 2011

This paper presents a new state of charge(SOC) estimation of large capacity of Li-ion battery (LIB) based on the multiple model adaptive estimation(MMAE) method. We first introduce an equivalent circuit model of LIB. The relationship between the terminal voltage and the open circuit voltage(OCV) is nonlinear and may vary depending on the changes of temperature and C-rate. In this paper, such behaviors are described as a set of multiple linear time invariant impedance models. Each model is identified at a temperature and a C-rate. These model set must be obtained a priori for a given LIB. It is shown that most of impedances can be modeled by first-order and second-order transfer functions. For the real time estimation, we transform the continuous time models into difference equations. Subsequently, we construct the model banks in the manner that each bank consists of four adjacent models. When an operating point of cell temperature and current is given, the corresponding model bank is directly determined so that it is included in the interval generated by four operating points of the model bank. The MMAE of SOC at an arbitrary operating point (T $^{\circ}C$, $I_{bat}$[A]) is performed by calculating a linear combination of voltage drops, which are obtained by four models of the selected model bank. The demonstration of the proposed method is shown through simulations using DUALFOIL.

• Journal of the Korean Solar Energy Society
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• v.34 no.2
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• pp.16-23
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• 2014

This paper presents a renewable energy configuration plan of Micro grid in Gapa Island. To analyze the characteristics of Micro grid, BESS (Battery Energy Storage System), PMSG (Permanent Magnet Synchronous Generator) and SCIG (Squirrel Cage Induction Generator) are first modelled. The PMSG and SCIG will operate with basis on the real power curve. when the total power demand is larger than the total power generation, the BESS will be operated and the SOC (State Of Charge) is reduced. If the value of SOC could drop down to limited value, the system may be broken because of the voltage drop of BESS. To solve this problem, a DG (Diesel Generator) is used to charge the BESS and keep the voltage value of BESS with in a allowance limit. This paper represents simulation result when PMSG, SCIG connected to the Micro grid installed in Gapa Island. The simulation is carry out by using PSCAD/EMTDC program with actual line constant and transformer parameter in Gapa Island.

• Proceedings of the KIPE Conference
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• 2011.07a
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• pp.557-558
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• 2011

전기자동차의 축전지 관리 시스템(BMS : Battery Management System)의 잔존용량(SOC : State Of Charge)산정에는 Ah 측정법, 비중측정법, 전압측정법 등이 있다. 기존 전압 측정법의 경우 측정 전압을 프로세서에서 직접 처리하기 때문에 축전지의 미세한 전압 변화를 측정하지 못하여 잔존 용량 산정시 세밀한 계산에 어려움이 따른다. 본 논문에서는 축전지의 전압 측정 시 프로세서 전단에 전압의 부분 증폭회로를 추가하여 축전지의 미세한 전압변화를 증폭하여 측정하는 방법을 제안 하였다. 니켈수소전지를 대상으로 실험한 결과 충전 중 기존 전압측정법은 1.431V, 1.436V, 1.441V가 측정 되었을 때의 잔존 용량은 84%로 일정하였다. 같은 전압변화에서 부분증폭회로를 적용한 충전전압은 1.4297V, 1.4303V ~ 1.4352V, 1.4358V로 측정 되었으며, 그에 따른 잔존용량은 84% ~ 85%로 기존 전압 측정법 보다 약 9 ~ 10배 정도 세밀하게 측정 되었다. 제안한 방법을 통한 실험으로 제안된 방법이 기존 전압 측정법보다 세밀한 전압 측정 및 SOC산정이 가능함을 확인 하였다.

• Journal of Power Electronics
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• v.12 no.5
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• pp.778-786
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• 2012

In this paper, a novel scheme for the condition monitoring of lithium polymer batteries is proposed, based on the sigma-point Kalman filter (SPKF) theory. For this, a runtime-based battery model is derived, from which the state-of-charge (SOC) and the capacity of the battery are accurately predicted. By considering the variation of the serial ohmic resistance ($R_o$) in this model, the estimation performance is improved. Furthermore, with the SPKF, the effects of the sensing noise and disturbance can be compensated and the estimation error due to linearization of the nonlinear battery model is decreased. The effectiveness of the proposed method is verified by Matlab/Simulink simulation and experimental results. The results have shown that in the range of a SOC that is higher than 40%, the estimation error is about 1.2% in the simulation and 1.5% in the experiment. In addition, the convergence time in the SPKF algorithm can be as fast as 300 s.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.882-883
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• 2013

With the development of the society, pure electric vehicles will be surely important of the future. Electric vehicle requires various technology like motor driving, battery management, operational efficiencies and so on. Battery management is indeed the most important to enhance battery's performance and life. This paper has deeply discussed and studied on the lithium-polymer battery management system of pure electric vehicle. First of all we have analyzed the characteristic of the lithium-polymer batteries and the factors influenced on the state of charge. Then a logical SOC measuring method has been raised, which is the combination of open circuit voltage and Ah integration. The next we will introduce the design of battery management system, the battery management system performs many functions, such as inspecting the whole process, when it's running cell equalization protecting and diagnosing the battery, estimating the state of charge. The module design style including microcontroller, data aquisition module, charging control module and serial communication module. To arrive at conclusions, the battery management system which this paper has introduced is reliable and economical.

• Journal of IKEEE
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• v.24 no.2
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• pp.610-618
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• 2020

This paper presents the modeling of hybrid railway vehicles with hydrogen Fuel-Cells (FCs)/battery using a rule-based algorithm. The driving power of traction system is determined with the speed-torque curve by operation area of the electric machine and the electrical systems are modeled. The demanded power of electrical systems is set with the energy management system (EMS). The consumption of hydrogen is effectively managed with the subdivided operation region depending on the state of charge (SOC). The validity of the modeling is verified using MATLAB/Simulink.