• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.1
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• pp.130-136
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• 2009

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 frequency domain are analyzed with the parameters obtained from different conditions. The parameterized model of Li-ion battery indicates highly dependant of temperatures. The system pole and internal resistance changes 6.6 and 18 times at $-20^{\circ}C$, comparing with those at $25^{\circ}C$, respectively. These results will be utilized on constructing model-based state observer or an on-line identification and an adaptation of the model parameters in battery management systems for hybrid electric vehicle applications.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.4
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• pp.286-293
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• 2019

This study compares and analyzes online update techniques, which estimate the parameters of battery equivalent circuit models in real time. Online update techniques, which are based on extended Kalman filter and recursive least square methods, are constructed by considering the dynamic characteristics of batteries. The performance of the online update techniques is verified by simulation and experiments. Each online update technique is compared and analyzed in terms of complexity and accuracy to propose a suitable guide for selecting algorithms on various types of battery applications.

• Journal of Power Electronics
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• v.16 no.5
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• pp.1981-1989
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• 2016

Battery diagnosis is vital to battery-based applications because it ensures system reliability by avoiding battery failure. This paper presents a novel intelligent battery charger with an online diagnosis function to circumvent interruptions in system operation. The charger operates in normal charging and diagnosing modes. The diagnosis function is performed with the impedance spectroscopy technique, which is achieved by injecting a sinusoidal voltage excitation signal to the battery terminals without the need for additional hardware. The impedance spectrum of the battery is calculated based on voltage excitation and current response with the aid of an embedded digital lock in amplifier in a digital signal processor. The measured impedance data are utilized in the application of the complex nonlinear least squares method to extract the battery parameters of the equivalent circuit. These parameters are then compared with the reference values to reach a diagnosis. A prototype of the proposed charger is applied to four valve-regulated lead-acid batteries to measure AC impedance. The results are discussed.

• 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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• 2018.07a
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• pp.476-477
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• 2018

최근 휴대폰 등 DC 가전기기의 증대와 에너지 시간 이동이 가능한 저장장치를 겸비한 스마트그리드 출현으로 에너지를 저장하는 장치의 수요가 증가하고 있다. 본 논문에서는 충전시간이 많이 소요되는 정전압 충전 방식을 제거하고 정전류 충전방식으로만 배터리 충전을 완료할 수 있는 충전기법을 제안한다. 이를 위해 정전류 충전에 필요한 배터리 파라미터를 실시간으로 추정하여 배터리 전압을 추정할 수 있는 간략화된 알고리즘을 제안한다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.59-61
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• 2019

고용량 어플리케이션의 높은 신뢰성을 만족하기 위해 배터리 열화에 영향을 미치는 다양한 변수가 고려되어야 하며, 24S1P의 배터리팩을 사용하여 전기적 노화를 수행하였다. 주성분 분석을 통해 열화에 상관성이 있는 변수인 용량, 내부 저항, 셀간 전압 편차, 최대 온도, 만방에서의 최소 전압 등을 설명하는 새로운 열화의 변수를 추출하였다. 열화 변수를 사용하여 설계한 SOH 추정 기법을 비교 및 검증한다.

• Journal of Power Electronics
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• v.16 no.2
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• pp.643-653
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• 2016

The conventional methods used to evaluate battery state-of-charge (SOC) cannot accommodate the chemistry nonlinearities, measurement inaccuracies and parameter perturbations involved in estimation systems. In this paper, an impedance-based equivalent circuit model has been constructed with respect to a LiFePO₄ battery by approximating the electrochemical impedance spectrum (EIS) with RC circuits. The efficiencies of approximating the EIS with RC networks in different series-parallel forms are first discussed. Additionally, the typical hysteresis characteristic is modeled through an empirical approach. Subsequently, a methodology incorporating an H-infinity observer designated for open-circuit voltage (OCV) observation and a hysteresis model developed for OCV-SOC mapping is proposed. Thereafter, evaluation experiments under FUDS and UDDS test cycles are undertaken with varying temperatures and different current-sense bias. Experimental comparisons, in comparison with the EKF based method, indicate that the proposed SOC estimator is more effective and robust. Moreover, test results on a group of Li-ion batteries, from different manufacturers and of different chemistries, show that the proposed method has high generalization capability for all the three types of Li-ion batteries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.847-849
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• 2009

Usually, after the decisions of the performance and range in the commercial vehicle, it is designed the ratings of the electric and mechanical elements for the vehicles. In this paper, the given performance and driving conditions, which are the maximum velocity, mileage, total weight of the normal gasoline scooter, battery type and size, and so on, are analysed for the design of the electric scooter. The maximum rotational speed and needed torque values of the electric motor which is substituted for the gasoline engine are calculated. These values can help to calculate the rating of the electric motor. In the calculation to obtain the torque and speed values, battery discharge and the running resistances are considered. We can decide the electric motor current value from the torque and speed values. The electric motor current values, which are significant parameter to decide the motor type and dimensions and characteristics of the electric motor, are decided by numerical simulation by the above conditions.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.345-346
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• 2014

본 논문에서는 배터리팩의 안전한 충방전 관리를 위해 배터리팩의 전류 제한 지표인 state-of-power (SOP)를 구하는 알고리즘을 제안한다. 직렬 연결된 배터리 팩의 SOP를 구하기 위해서는 각 셀의 배터리 파라미터 추정 과정이 필수적이다. 이를 구현하기 위해 듀얼 확장 칼만 필터 (DEKF)를 사용하였으며 효율적인 운용을 위해 DEKF의 사용량을 줄이는 방안을 제시한다. 실험을 통해 배터리 파라미터 추정 결과를 확인하였다.

• 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.