• Journal of Power Electronics
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• v.15 no.4
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• pp.1105-1118
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• 2015

Battery energy storage devices (ESDs) have become more and more commonplace to maintain the stability of islanded power systems. Considering the limitation in inverter capacity and the requirement of flexibility in the ESD, the droop control was implemented in paralleled ESDs for higher capacity and autonomous operation. Under the conventional droop control, state-of-charge (SoC) errors between paralleled ESDs is inevitable in the discharging operation. Thus, some ESDs cease operation earlier than expected. This paper proposes an adaptive accelerating parameter to improve the performance of the SoC error eliminating droop controller under the constraints of a microgrid. The SoC of a battery ESD is employed in the active power droop coefficient, which could eliminate the SoC error during the discharging process. In addition, to expedite the process of SoC error elimination, an adaptive accelerating parameter is dedicated to weaken the adverse effect of the constraints due to the requirement of the system running. Moreover, the stability and feasibility of the proposed control strategy are confirmed by small-signal analysis. The effectiveness of the control scheme is validated by simulation and experiment results.

• Transactions of the Korean hydrogen and new energy society
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• v.20 no.1
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• pp.38-44
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• 2009

Recently, the research focused on fuel cell hybrid vehicles (FCHVs) is becoming an attractive solution due to environmental pollution generated by fossil fuel vehicles. The proper energy control strategy will result in extending the fuel cell lifetime, increasing of energy efficiency and an improvement of vehicle performance. Battery state of charge (SoC) is an important quantity and the estimation of the SoC is also the basis of the energy control strategy for hybrid electric vehicles. Estimating the battery's SoC is complicated by the fact that the SoC depends on many factors such as temperature, battery capacitance and internal resistance. In this paper, battery charging time estimated by SoC is studied by using the speed response and current response. Hybrid system is consist of a fuel cell unit and a battery in series connection. For experiment, speed response of vehicle and current response of battery were determined under different state of charge. As the results, the optimal battery charging time can be estimated. Current response time was faster than RPM response time at low speed and vice versa at high speed.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.178-179
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• 2016

본 논문에서는 바나듐 레독스 플로우 배터리의 동작원리를 설명하고, C-rate에 따른 특성 분석을 하였다. 전해질 양이 18mL, 22mL일 때 0.1C, 0.3C, 0.5C, 0.7C, 0.9C, 1.0C로 전류의 크기에 변화를 주어 용량을 측정한 후 비교 분석하였다. 더불어 HPPC(Hybrid pulse power characterization) 실험에서 1.0C 일 때 잔존 용량(State-of-charge, SOC)의 변화에 따른 저항을 추출하였고 분석하였다. 그 결과 바나듐 레독스 플로우 배터리의 효율 분석을 위한 파라미터 값을 확인하였다.

• 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.

• KIEE International Transactions on Electrophysics and Applications
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• v.3C no.5
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• pp.189-193
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• 2003

Initial irreversible capacity (IIC) can be defined by means of the initial intercalation Ah efficiency (IIE) and the initial irreversible specific capacity at the surface (IICs) with the linear-fit range of the intercalation so as to precisely express the irreversibility of an electrode-electrolyte system. Their relationship was IIC = Qc - Q$_{D}$ = (IIE$^{-1}$ - 1) Q$_{D}$ + IICs in the linear-fit range of IIE. Here, Qc and Qd signify charge and discharge capacity, respectively, based on a complete lithium ion battery cell. Charge indicates lithium insertion to carbon anode. Two terms of IIE and IICs depended on the types of active materials and compositions of the electrode and electrolyte but did not change with charging state. In an ideal electrode-electrolyte system, IIE and IICs would be 100%, 0 mAh/g for the electrode and mAh for the cell, respectively. These properties can be easily obtained by the Gradual Increasing of State of Charge (GISOC).OC).

• Transactions of the Korean hydrogen and new energy society
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• v.15 no.1
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• pp.1-11
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• 2004

Hybrid power systems with fuel cells and batteries have the potential to improve the operation efficiency and dynamic response. A proper load management strategy is important to better system efficiency and endurance in hybrid systems. In this paper, a fuzzy logic algorithm has been used to determine the fuel cell output power depending on the external required power and the battery state of charge(SoC). If the required power of the hybrid system is small and the SoC is small, then the greater part of the fuel cell power is used to charge the battery pack. If the required power is relatively big and the SoC is big, then fuel cell and battery are concurrently used to supply the required power. These IF-THEN operation rules are implemented by fuzzy logic for the energy management system of hybrid system. The strategy is evaluated by simulation. The results show that fuzzy logic can be effectively used to optimize the operational efficiency of hybrid system and to maintain the battery SoC properly.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.217-222
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• 2018

The deterioration of the Battery is decided by SoC and SoH. To determine Battery deterioration, SoC measurement method of Battery by Transfer Function was studied. The SoC of the Battery changes most greatly in the 1Hz band where the DC resistance is detected. but it is a narrow band to observe, therefore, the observation band was moved to 1kHz using the tone busrst signal. Welch's method was used to observe the frequency domain characteristics of the signal. As a result of the spectral estimation, it was confirmed that the response signal of the battery is 1dB difference when the SoC is 40%, 60%, and 80%, respectively. Through this, Proposed the new method of Battery SoC measurement.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1176-1185
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• 2016

This paper proposes a new output voltage control scheme based on the SoC variation of the battery energy storage system (BESS) applicable for the stand-alone DC microgrid. The proposed control scheme provides relatively lower variation of the DC grid voltage than the conventional droop method. The performance of proposed control scheme was verified through computer simulations for a typical stand-alone DC microgrid which consists of BESS, photo-voltaic (PV) panel, engine generator (EG), and DC load. A scaled hardware prototype for the stand-alone DC microgrid with DSP controller was set up in the lab, and the proposed control algorithm was installed in the DSP controller. The test results were compared with the simulation results for performance verification and actual system implementation.

• 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.