• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.560-562
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• 1998

A new approach to developing battery SOC indicator for electric vehicle is discussed in this paper. One of the most difficult problems associated with the development of electric vehicle is the battery indicator which reliably informs the state of charge(SOC) of the battery to the driver. And the condition to be satisfied with SOC indicator installed on the electric vehicle is that it should be used under frequently variable load. A new method to determining SOC using neural networks(NN) is proposed to satify the condition. The training data of NN are obtained by using mathematical model of lead-acid battery, and calculating discharge currents and terminal voltages while battery discharges with constant current. The 3-layered NN with back propagation algorithm is used Simulation results show that the proposed method is appropriate as SOC indicator of the battery.

• Journal of Electrochemical Science and Technology
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• v.12 no.4
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• pp.415-423
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• 2021

For a battery module where single cells are connected in series, the single cells should each have a similar state of charge (SOC) to prevent them from being exposed to an overcharge or over-discharge during charge-discharge cycling. To detect the existence of unbalanced SOC cells in a battery module, we propose a simple measurement method using a single-frequency response of electrochemical impedance spectroscopy (EIS). For a commercially available graphite/nickel-cobalt-aluminum-oxide lithium-ion cell, the cell impedance increases significantly below SOC20%, while the impedance in the medium SOC region (SOC20%-SOC80%) remains low with only minor changes. This impedance behavior is mostly due to the elementary processes of cathode reactions in the cell. Among the impedance values (Z, Z', Z"), the imaginary component of Z" regarding cathode reactions changes heavily as a function of SOC, in particular, when the EIS measurement is performed around 0.1 Hz. Thanks to the significant difference in the time constant of cathode reactions between ≤SOC10% and ≥SOC20%, a single-frequency EIS measurement enlarges the difference in impedance between balanced and unbalanced cells in the module and facilitates an ~80% improvement in the detection signal compared to results with conventional EIS measurements.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1998.11a
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• pp.177-180
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• 1998

To manage lead-acid battery efficiently and to use it longer in UPS, the state of charge(SOC) indicator of the battery is needed. So a new approach to developing battery SOC indicator for UPS is discussed in this paper. This method to determining SOC by combining the available data of discharge characteristics of a battery with neural networks(NN) is presented. The 3-layered NN with back propagation algorithm has been used. Exprement results show that the proposed method is appropriate as SOC indicator of the battery.

• Annual Conference of KIPS
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• 2024.05a
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• pp.457-458
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• 2024

배터리의 상태 추정 알고리즘은 전기차의 성능과 안정성 관련 핵심 기술이다. SOC(state of charge) 알고리즘은 배터리의 충전 상태를 추정하는 역할을 한다. 본 논문에서는 SOC 알고리즘의 MIL(Modeling in the Loop) Test 과정에서 사용되는 파라미터의 오차와 영향에 대한 고찰을 제시한다.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1131-1140
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• 2016

A simple design for a sliding mode observer is proposed for EV lithium battery SOC estimation in this paper. The proposed observer does not have the limiting conditions of existing observers. Compared to the design of previous sliding mode observers, the new observer does not require a solving matrix equation and it does not need many observers for all of the state components. As a result, it is simple in terms of calculations and convenient for engineering applications. The new observer is suitable for both time-variant and time-invariant models of battery SOC estimation, and the robustness of the new observer is proved by Liapunov stability theorem. Battery tests are performed with simulated FUDS cycles. The proposed observer is used for the SOC estimation on both unchanging parameter and changing parameter models. The estimation results show that the new observer is robust and that the estimation precision can be improved base on a more accurate battery model.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.5
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• pp.360-368
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• 2008

A lumped parameter model of Li-ion battery in hybrid electric vehicle(HEV) is constructed and system parameters are identified by using recursive least square estimation for different C-rates, SOCs and temperatures. The system characteristics of pole and zero in the frequency domain are analyzed with the parameters obtained from different conditions. The parameterized model of a Li-ion battery indicates highly dependent of temperatures. To estimate SOC and polarization voltage, a Luenberger state observer is utilized. The P- or PI-gains of observer based on a suitable natural frequency and damping ratio is adopted for the state estimation. Satisfactory estimation accuracy of output voltage and SOC is especially obtained by a PI-gain. The feasibility of the proposed estimation method is verified through experiment under the conditions of different C-rates, SOCs and temperatures.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.853-863
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• 2020

As the battery ages, the internal resistance of the battery increases, so the loss due to the internal resistance increases at the same charging current, causing the battery temperature to rise, which further accelerates battery aging. Therefore, it is necessary to optimize the charging conditions according to the aging of the battery or the current charge amount, and to accurately estimate this, estimation of the parameters of the equivalent circuit is most important. This paper proposes a new measurement technique that can measure the internal resistance of a battery by analyzing a specific high frequency voltage and current applied to the battery. In addition, in order to test the validity of the proposed measurement technique, the current charging amount was estimated based on the measured internal resistance, and the terminal voltage of the constant current charging mode was automatically set and operated. As a result, good results were obtained regardless of the battery voltage. If this equipment is installed in the charging device, it is believed that it will be of great help in the stability management of the aging reusable battery.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.36-38
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• 2018

As the battery capacity requirement increases, battery cells are connected in a parallel configuration. However, the sharing current of each battery cell becomes unequal due to the imbalance between cell's impedance which results the mismatched states of charge (SOC). The conventional fixed-resistance balancing methods have a limitation in battery equalization performance and system efficiency. This paper proposes a battery equalization method based on dynamic resistance technique, which can improve equalization performance and reduce the loss dissipation. Based on the SOC rate of parallel connected battery cells, the switches in the equalization circuit are controlled to change the equivalent series impedance of the parallel branch, which regulates the current flow to maximize SOC utilization. To verify the method, operations of 4 parallel-connected 18650 Li-ion battery cells with 3.7V-2.6Ah individually are simulated on Matlab/Simulink. The results show that the SOCs are balanced within 1% difference with less power dissipation over the conventional method.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.204-206
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• 2019

본 논문은 이중 확장 칼만 필터를 통한 SOC (State of charge) 및 용량 추정과 배터리 모델 파라미터를 이용한 폐배터리의 최대 출력을 추정하는 방법을 연구 및 제안한다. 배터리의 단순 전압 측정을 통해 상태를 진단할 경우, 부하 조건에 따라 급격한 전압 상승 및 강하로 인해 정밀한 안전 진단 및 운용에 어려움이 따르지만, 폐배터리는 일반 배터리에 비해 전압 변동율이 크기 때문에 상태 진단에 큰 어려움이 존재한다. 따라서 본 논문에서는 폐배터리의 정밀한 안전진단을 하기 위해 SOC 영역 및 충/방전에 따른 최대 출력을 계산하여 사전에 배터리의 상태를 진단할 수 있는 알고리즘을 제안한다. 또한, 배터리의 노화도에 따른 최대 출력을 실험 및 시뮬레이션을 통해 결과를 제시하여 유효한 방식임을 검증한다.