• Title/Summary/Keyword: SOC(State of Charge)

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Coordinated Droop Control for Stand-alone DC Micro-grid

  • Kim, Hyun-Jun;Lee, Yoon-Seok;Kim, Jae-Hyuk;Han, Byung-Moon
    • Journal of Electrical Engineering and Technology
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    • v.9 no.3
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    • pp.1072-1079
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    • 2014
  • This paper introduces a coordinated droop control for the stand-alone DC micro-grid, which is composed of photo-voltaic generator, wind power generator, engine generator, and battery storage with SOC (state of charge) management system. The operation of stand-alone DC micro-grid with the coordinated droop control was analyzed with computer simulation. Based on simulation results, a hardware simulator was built and tested to analyze the performance of proposed system. The developed simulation model and hardware simulator can be utilized to design the actual stand-alone DC micro-grid and to analyze its performance. The coordinated droop control can improve the reliability and efficiency of the stand-alone DC micro-grid.

Performance Characteristics of Electric Powertrain Parts for Power Split Type HEV at Steady Speed (Power Split Type HEV 차량 정속주행시 전기동력부품 성능특성)

  • Kim, Chai-Won
    • Transactions of the Korean Society of Automotive Engineers
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    • v.15 no.2
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    • pp.182-186
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    • 2007
  • This paper studied performance characteristics of hybrid automotive to replace existing fossil fuel vehicles. Specially, about power split type HEV that is T-HEV's drive system when a vehicle drives at steady speed, monitored both output of each engine, motor and generator and battery SOC (state of charge) and analyzed performance characteristic of power transmission system and electricity power parts. This study shows those that acquired and analyzed information from signals between HCU and each controller of actual T- vehicle. From this study, it is confirmed that each conditions of EV and HEV drive can be a improvement with respect to the fuel efficiency of vehicles.

Operating principle and Analysis for modeling Experimental characterization of Non-aqueous lithium-air battery (비수계 리튬에어 배터리 동작원리와 모델링을 위한 특성실험 분석)

  • Jang, So-Hee;Kim, Jong-Hoon;Choi, Sang-won;Tak, Yong-sug
    • Proceedings of the KIPE Conference
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    • 2016.07a
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    • pp.375-376
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    • 2016
  • 본 논문에서는 Li-air 배터리의 동작원리를 설명하고, 모델링을 위해 Li-air 배터리의 내부와 충전 및 방전 원리를 보여주고 SOC(State Of Charge) 추정을 위한 OCV(Open-circuit Voltage) 그래프의 분석과 회로도에 대해 설명 하였다. 더불어, 전류적산법의 원리를 적용하여 SOC 추정의 기준이 되는 값을 추출하였다.

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State-of-Charge Observation of Lithium Polymer Battery using SPKF (SPKF를 이용한 리튬 폴리머 배터리(LiPB)의 충전 상태(SOC) 관측)

  • Seo, Bo-Hwan;Lee, Dong-Choon;Lee, Kyo-Beum;Kim, Jang-Mok
    • Proceedings of the KIPE Conference
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    • 2011.07a
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    • pp.228-229
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    • 2011
  • 본 논문은 SPKF(Sigma-point Kalman Filter)를 이용한 리튬 폴리머 배터리(LiPB)의 충전 상태(SOC: State of Charge) 추정 방법을 제안한다. 배터리 모델은 단순화된 테브난 등가회로 모델과 Runtime 모델이 결합되어 있고, Runtime 모델의 양단 전압을 이용하여 SOC를 추정한다. 제안된 알고리즘은 시뮬레이션을 통해 그 타당성이 검증된다.

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Li-ion Battery Charateristics for Electric Scooters (전기 스쿠터를 위한 Li-ion 배터리 특성)

  • Kim, Seunghwan;Kim, Hyosung
    • Proceedings of the KIPE Conference
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    • 2015.07a
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    • pp.71-72
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    • 2015
  • 배터리의 랜들 등가회로 모델은 기본적으로, 전달 저항 Rct, 전기 이중층 커패시턴스 Cdl, 내부저항 Ri, 그리고 개방회로전압 Voc의 4가지 파라미터로 구성 된다. 본 논문은 실험에 의해 리튬이온 배터리의 모델링을 위한 기본적 4가지 파라미터를 추출하고 운전조건에 따른 특성을 분석한다. 분석 결과를 이용하여 본 연구자에 의하여 제작된 전기 스쿠터의 SOC(State of Charge)를 추정하는 알고리즘을 제안한다.

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CNN based battery SOC estimation using thermal distribution image (CNN 기반 열 분포 영상을 이용한 배터리 SOC 추정 연구)

  • Kwon, Sanguk;Kim, Jaeho;Kim, Yongsoon;Ahn, Jeongho;Choi, Eojin;Pack, Jinu;Kim, Jonghoon
    • Proceedings of the KIPE Conference
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    • 2019.07a
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    • pp.453-454
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    • 2019
  • 본 논문은 ESS(Energy Storage System)의 과충전, 과방전으로 인한 열 폭주 현상을 방지하기 위한 사전 연구로 원통형 리튬이온 단일 셀의 충/방전에 따른 열 분포를 열화상 카메라로 촬영하여 분석하였다. 실험을 통한 열 분포 이미지를 학습 데이터로 구성하여, SOC(State of Charge)를 추정하는 CNN(Convolution Neural Network) 모델을 제안한다.

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Battery State of Charge Estimation Considering the Battery Aging (배터리의 노화 상태를 고려한 배터리 SOC 추정)

  • Lee, Seung-Ho;Park, Min-Kee
    • Journal of IKEEE
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    • v.18 no.3
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    • pp.298-304
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    • 2014
  • Proper operation of the battery powered systems depends on the accuracy of the battery SOC(State of Charge) estimation, therefore it is critical for those systems that SOC is accurately determined. The SOC of the battery is related to the battery aging and the SOC estimation methods without considering the aging of the battery are not accurate. In this paper, a new method that accurately estimate the SOC of the battery is proposed considering the aging of the battery. A mathematical model for the Battery SOC-OCV(Open Circuit Voltage) relationship is presented using Boltzmann equation and aging indicator is defined, and then the SOC is estimated combining the mathematical model and aging indicator. The proposed method takes the aging of the battery into consideration, which leads to an accurate estimation of the SOC. The simulations and experiments show the effectiveness of the proposed method for improving the accuracy of the SOC estimation.

Prediction Method of End of Charge Voltage using Battery Parameter Measurement (배터리 파라미터 측정을 이용한 충전종지전압 예측기법)

  • Kim, Ho-Yong;Wang, Yi-Pei;Park, Seong-Mi;Park, Sung-Jun;Son, Gyung-Jong
    • Journal of the Korean Society of Industry Convergence
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    • v.25 no.3
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    • pp.387-396
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    • 2022
  • Recently, e-Mobility, which is a personal mobility device such as an electric bicycle or an electric scooter, is rapidly emerging. However, since E-Mobility has various voltage systems due to the characteristics of its products, it is essential for companies that operate them to use multiple dedicated chargers. A universal charger capable of charging batteries of various voltage systems with one charger is required to reduce the cost of purchasing and managing multiple dedicated chargers. For this, information on the EOC(End of Charge) is essential. In order to know the EOC, it is necessary to detect the internal impedance of the battery. However, the internal impedance of the battery changes according to various conditions such as SOH(State Of Health), SOC(State Of Charge), and ambient temperature. By observing the change in these parameters, the state of the battery can be diagnosed and the EOC can be predicted. In this paper, we propose an algorithm to analyze the battery's internal impedance and to predict the EOC, in order to acquire information on the EOC of the battery, which is an essential requirement of a universal charger.

The SOC Management Strategy of Battery System for Propulsion in Wireless Low Floor System (무가선 저상트램 추진배터리 시스템의 SOC관리 전략)

  • Oh, Yong-Kuk;Kwak, Jae-Ho;Lee, Ho-Yong
    • Proceedings of the KSR Conference
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    • 2011.10a
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    • pp.2329-2335
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    • 2011
  • The Wireless low floor tram uses the energy more effectively than other systems with onboard battery system. But for this the SOC(state of charge) management of the battery system is required. This paper is focused on the SOC management strategy of battery system for propulsion in wireless low floor tram. For minimizing consumption energy, the SOC management strategy that maximizes the regeneration energy is studied. The SOC operating region is divided to overcome the limited life cycle pointed out as a disadvantage of battery system. And the effective energy management strategy of tram is suggested through the charge/discharge of the battery system according to tram status in catenary/catenary-free section.

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A Review of Li-ion Battery Equivalent Circuit Models

  • Zhang, Xiaoqiang;Zhang, Weiping;Lei, Geyang
    • Transactions on Electrical and Electronic Materials
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    • v.17 no.6
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    • pp.311-316
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    • 2016
  • Batteries are critical components of electric vehicles and energy storage systems. The connection of a battery to the power grid for charge and discharge greatly affects energy storage. Therefore, an accurate and easy-to-observe battery model should be established to achieve systematic design, simulation, and SOC (state of charge) estimations. In this review, several equivalent circuit models of representative significance are explained, and their respective advantages and disadvantages are compared to determine and outline their reasonable applications to Li-ion batteries. Numerous commonly used model parameter identification principles are summarized as well, and basic model verification methods are briefly introduced for the convenient use of such models.