• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.9
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• pp.96-102
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• 2015

This paper presents battery charging method using 3-phase AC-DC boost converter. General battery charging method is that charging the battery voltage to the reference voltage according to the constant current(CC) control, when it reaches the reference voltage, charging the battery fully according to the constant voltage(CV) control. However, battery chaging time is increased because of the battery impedance, constant current charging section which shoud take the large amount of charge is narrow, and constant voltage charging section which can generate insufficient charge is widen. To improve this problem, we proposes the method to reduce the charging time according to the SOC(State of Charge) estimation using battery impedance.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.317-318
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• 2013

State-of-Charge (SOC) is one of the most important indicators for the battery management system. Thus its precise estimation is crucial not only for effectively utilizing the energy but also preventing critical situations from happening to the powertrain of the vehicle. However, lead-acid battery is time-variant and highly nonlinear, and the hysteresis phenomenon causes large errors in estimating SOC. This paper proposes a novel SOC estimation technique for the lead-acid battery by using Extended Kalman Filter (EKF) considering hysteresis effect. The validity of the proposed technique is verified through the experiments.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.126-127
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• 2017

An accurate State Of Charge (SOC) estimation of battery is the most important technique for Electric Vehicles (EVs) and Energy Storage Systems (ESSs). In this paper a new integrated Unscented Kalman Filter-Particle Filter (UKF-PF) is employed to estimate the SOC of a $LiFePO_4$ battery cell and a significant improvement is obtained as compared to the other methods. The parameters of the battery is modeled by the second order Auto Regressive eXogenous (ARX) model and estimated by using Recursive Least Square (RLS) method to calculate value of each element in the model. The proposed algorithm is established by combining a parameter identification technique using RLS method with ARX model and an SOC estimation technique using UKF-PF.

• Journal of Power Electronics
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• v.18 no.1
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• pp.129-136
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• 2018

An accurate state-of-charge (SOC) estimation ensures the reliable and efficient operation of a lithium-ion battery management system. On the basis of a combined electrochemical model, this study adopts the forgetting factor least squares algorithm to identify battery parameters and eliminate the influence of test conditions. Then, it implements online SOC estimation with high accuracy and low run time by utilizing the low computational complexity of the unscented Kalman filter (UKF) and the rapid convergence of a particle filter (PF). The PF algorithm is adopted to decrease convergence time when the initial error is large; otherwise, the UKF algorithm is used to approximate the actual SOC with low computational complexity. The effect of the number of sampling particles in the PF is also evaluated. Finally, experimental results are used to verify the superiority of the combined method over other individual algorithms.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.397-398
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• 2015

본 논문은 $LiFePO_4$ 셀의 SOC(State of Charge) 추정에서 가장 중요한 역할을 하는 모델 파라미터인 OCV(Open Circuit Voltage)의 설계에 관한 것이다. $LiFePO_4$ 셀은 히스테리시스 특성 때문에 Charging/Discharging OCV값을 이은 curve인 Major loop만으로는 신뢰도 높은 SOC 추정이 어렵다. 따라서, 기존의 Major loop에 추가적으로 SOC 10% 구간별로 Minor loop을 설계해 줌으로써 배터리 모델링의 정확도를 높이고, 이를 최종적으로 EKF(Extended Kalman Filter)알고리즘을 이용하여 SOC 추정으로 해봄으로써 정확도 향상을 비교해 보고 분석해 보고자 한다.

• 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 IKEEE
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• v.23 no.4
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• pp.1328-1336
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• 2019

To maintain the safe and optimal performance of batteries, accurate estimation of state of charge (SOC) is critical. In this paper, Long short-term memory network (LSTM) based on the artificial intelligence algorithm is applied to address the problem of the conventional coulomb-counting method. Different discharge cycles are concatenated to form the dataset for training and verification. In oder to improve the quality of input data for learning, preprocessing was performed. In addition, we compared learning ability and SOC estimation performance according to the structure of LSTM model and hyperparameter setup. The trained model was verified with a UDDS profile and achieved estimated accuracy of RMSE 0.82% and MAX 2.54%.

• 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.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.2
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• pp.139-148
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• 2014

State-of-charge (SOC) is one of the significant indicators to estimate the driving range of the electric vehicle and to control the alternator of the conventional engine vehicles as well. Therefore its precise estimation is crucial not only for utilizing the energy effectively but also preventing critical situations happening to the power train and lengthening the lifetime of the battery. However, lead-acid battery is time-variant, highly nonlinear, and the hysteresis phenomenon causes large errors in estimation SOC of the battery especially under the frequent discharge/charge. This paper proposes a novel estimation technique for the SOC of the Lead-Acid battery by using a well-known Extended Kalman Filter (EKF) and an electrical equivalent circuit model of the Lead-Acid battery considering diffusion and hysteresis characteristics. The diffusion is considered by the reconstruction of the open circuit voltage decay depending on the rest time and the hysteresis effect is modeled by calculating the normalized integration of the charge throughput during the partial cycle. The validity of the proposed algorithm is verified through the experiments.

• Journal of Power Electronics
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• v.16 no.1
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• pp.238-248
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• 2016

The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.