• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.118-121
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• 2023

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and asthe market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2017년도 추계학술대회
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• pp.167-168
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• 2017

This paper investigates to identify an optimal for analysis of battery behavior in system-level applications such as Battery Energy Storage Systems in Smart Grid infrastructures and Electrical vehicles. At system level applications, it is mandatory to check model for meaningful equivalency and practical ability for extension from unit cell to Battery stack. The investigation of current battery models in relation to their suitability for study and analysis of system level applications of battery helpful for identification of optimal model and it also provides an intuition and direction to develop the most suitable model, if such models are not available already.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2134-2141
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• 2014

This paper addresses on a wind power system with BESS(Battery Energy Storage System). The concerned system consists of four parts: the wind speed production model, the wind turbine model, configure capacity of the battery energy storage, battery model and control of the BESS. First of all, we produce wind speed by 4-component composite wind speed model. Secondly, the maximum available wind power is determined by analyzing the produced wind speed and the characteristic curve of wind power. Thirdly, we configure capacity of the BESS according to wind speed and characteristic curve of wind speed-power. Then, we propose a control strategy to track the power reference. Finally, some simulations have been demonstrated to visualize the feasibility of the proposed methodology.

• 전기학회논문지
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• 제58권9호
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• pp.1714-1719
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• 2009

In this paper, methodology of battery optimal designing is proposed. Fuel cell model including dynamic characteristic is developed and load model is produced by considering driving schedule. Using these models, required energy of load and supplying energy from fuel cell are analyzed by comparing simulation results. Also parameter of fuel cell model is changed variously and battery capacity is calculated in each cases. And methode of battery optimal designing is presented by regarding dynamic characteristic of fuel cell.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1525-1527
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• 2004

Modeling of the battery for 42V Power-Net system is presented. For the Battery Management System(BMS) algorithm in a Mildhybrid vehicle, accuracy in SOC estimation is crucial. The battery model is needed for the BMS algorithm as well as system computer symulation for the energy management. The battery model was composed of impedance elements and the each element of the model is estimated by the analysis of the terminal voltage. The result of the model is confirmed by experimental data.

• Journal of Electrical Engineering and Technology
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• 제7권6호
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• pp.932-939
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• 2012

This paper presents an equivalent circuit model of a LIPB (Li-Ion Polymer battery) for Hybrid Electric Vehicles (HEVs). The proposed equivalent circuit can be used to predict the charging/discharging characteristics in time domain as well as the impedance characteristic analysis in frequency domain. Based on these features, a one-cell model is established as a function of Depth of Discharge (DoD), and a 48-cell model for a battery pack was also established. It was confirmed by experiment that the proposed model predict the discharging and impedance (AC) characteristics quite accurately at different constant current levels. To check the usefulness of the proposed circuit, the model was used to simulate a motor driving circuit with an Insulated Gate Bipolar Transistor (IGBT) inverter and Brushless DC (BLDC) motor, and it is confirmed that the model can calculate the battery voltage fluctuation in time domain at different DoDs.

• 대한기계학회논문집B
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• 제38권3호
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• pp.227-234
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• 2014

전기 자동차의 연료 소모량은 배터리 성능에 의존한다. 배터리의 성능은 작동온도에 민감하기 때문에, 배터리 온도 관리는 성능과 내구성을 보장한다. 특히, 배터리 팩에서의 모듈의 온도 분포는 냉각특성에 영향을 미친다. 이 연구는 모듈 사이의 온도 분포를 확인 할 수 있는 배터리 열적 모델링에 초점을 두었다. 본 연구의 배터리 모델은 NiMH 각형 모델이며, 10개의 모듈로 구성되어졌다. 배터리 열 모델은 열 발생, 채널을 통과하는 대류 열 전달 그리고 모듈 사이의 전도 열 전달로 구성되었다. 배터리 내에서 발생되는 열발생 모델은 충/방전 동안의 전기적인 저항열에 의해 계산되어 진다. 모델은 전 하이브리드 자동차의 운전 동안 적절한 열관리의 전략을 결정한다.

• 한국항공우주학회지
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• 제49권12호
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• pp.989-996
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• 2021

배터리를 동력원으로 사용하는 무인항공기의 경우 배터리 용량이 한정적이기 때문에 임무 수행에 제약이 발생할 수 있다. 이를 최소화하기 위해 임무 지역으로 이동하는 동안 소모되는 배터리를 최소화 하는 것이 중요하다. 또한 임무 계획 단계에서 배터리 소모량 예측 모델을 이용하여 임무 수행 가능성을 사전에 판단할 수 있으며 복귀 시점 선정에 기준이 될 수 있다. 본 논문에서는 3차원 공간에서 환경 요소를 반영한 배터리 사용량 예측 모델을 제안한다. 무인항공기의 비행 기하 관계에 따라 요구 동력을 산출하고 이를 통해 배터리 사용량을 예측하였으며 기존에 제안된 배터리 사용량 예측 기법과 비교를 통해 검증한다. 또한 이를 목적함수로 하여 배터리 사용량을 최소화 하는 비행경로를 생성하고 최단 거리를 목적함수로 하였을 때의 결과와 비교하였다.

• 전력전자학회논문지
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• 제24권1호
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• pp.16-24
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• 2019

The performance of the battery management system (BMS) algorithm is important for ensuring the stability and efficient operation of battery packs. Such a performance is determined by the internal parameters of the electrical equivalent circuit model (EECM). This study proposes a performance improvement and verification of battery parameters for the BMS algorithm using electrical experiments and tools. The parameters were extracted through electrical characteristic experiments, and an EECM based on Ah counting was designed. Simulation results using the EECM were compared with actual experimental data to determine the best parameter extraction method.

• 한국융합학회논문지
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• 제11권6호
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• pp.7-14
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• 2020

본 논문에서는 확장 칼만 필터를 이용한 SOC 추정이 가능한 배터리 모니터링 시스템을 개발하였다. 배터리의 충·방전 상태를 정확하게 추정하기 위해 배터리 셀을 테브닌 모델로 모형화하고, 모델에 맞는 파라미터를 추출하였다. 이를 이용하여 확장 칼만 필터 알고리즘을 이용한 SOC 추정이 가능한 배터리 모니터링 디바이스를 제작하였으며, 다중 배터리 모니터링 디바이스 제어 및 배터리 상태 측정이 가능한 모니터링 서버를 제작하였다. 또한, 관리자가 실시간으로 상태를 확인하며 배터리 모니터링 디바이스 제어가 가능한 웹 서비스를 제작하였다. 특히, 배터리 SOC를 각각의 배터리 모니터링 디바이스에서 추정하고, 최종 결과만 모니터링 서버로 전달함으로써 서버의 계산량을 줄일 수 있다.