• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 전력전자학술대회
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• pp.459-460
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• 2019

The performance dynamics of battery is very sensitive to operating conditions (i.e temperature, load current, and state of charge). A model developed based on certain conditions may perform well under the similar conditions but can not accurately predict the performance for changing conditions. Thus, a generalized model is needed which can accurately emulate the battery dynamic behavior under all conditions. In addition, the components of the model should relate to the physicochemical processes that occur inside the battery. Electrochemical impedance curve shows better visible reflection of the processes inside battery as compared to voltage curve. The model trained for parameterization using neural network has better generalization than simple curve fitting. Thus, this study proposes recurrent neural network based parameterization of the Lithium ion battery model followed by impedance based identification.

• Journal of Power Electronics
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• 제14권5호
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• pp.1038-1046
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• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

• 전자공학회논문지
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• 제54권4호
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• pp.99-107
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• 2017

본 논문은 기존의 리튬 배터리(lithium battery) 등가모델의 정확도 개선을 위한 배터리 모델 계수 보정기법을 제안한다. 전기자동차 등 다양한 산업분야에 사용되는 리튬 배터리의 배터리 셀간 잔존용량(SOC, state of charge) 동일하게 유지하여 배터리 수명의 단축을 최소화하기 위해 BMS(battery management system)가 연구 개발 되었지만, 배터리 셀 전압 기반의 셀 밸런싱(cell balancing) 동작으로 내부저항 및 커패시터에 따른 SOC 변화를 따라가지 못한다. 배터리 내부저항 및 커패시터에 따른 배터리 SOC 추정을 위해 다양한 배터리 등가모델이 연구되었지만, 모든 배터리에 동일하게 적용하는 것은 한계가 있으며 특히 과도상태의 배터리 상태 추정이 어렵다. 기존의 배터리 전기적 등가모델 연구는 1종의 배터리를 대상으로 5~10% 오차율로 충 방전 동적특성을 모사하며 서로 다른 전기적 특성을 갖는 실제 배터리에 적용이 부적합하다. 따라서 본 논문에서는 모델 및 용량이 다른 실제 배터리 운용환경에 적합하며 오차율 5%이하의 동적특성 모사가 가능한 배터리 모델 계수 보정 알고리즘을 제안한다. 제안하는 배터리 모델 계수 보정법 검증을 위해 3.7 V 정격전압, 280 mAh, 1600 mAh 용량의 리튬 배터리를 사용하였으며, 리튬 배터리의 전기적 등가 모델로 2단 RC Tank 모델을 사용하였다. 또한 0.25C, 0.5C, 0.75C, 1C 4가지 C-rate를 사용하여 배터리 충 방전 실험 및 모델검증을 진행하였으며 제안하는 배터리 모델 계수 보정 알고리즘을 통해 구현한 두 종류의 배터리 모델의 배터리 충 방전 특성 및 과도상태 특성의 오차율은 최대 2.13%이다.

• Journal of Electrical Engineering and Technology
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• 제9권5호
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• pp.1729-1738
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• 2014

The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.

• 한국군사과학기술학회지
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• 제11권1호
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• pp.102-111
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• 2008

This paper describes the basic model and simulation results of thermal battery. Voltage and thermal analysis is a critical part of thermal-battery design because of the need to maintain the inner temperature above the electrolyte melting point. Traditionally, battery design has depended on an empirical approach, in which prototype batteries are outfitted with thermocouples and the design of subsequent batteries is refined accordingly. We have developed the basic model that allows the design engineer to configure or modify a battery, quickly conduct a thermal analysis, and efficiently review the results. Based on performance tests, the thermal-battery model was established and the effect of design parameters on battery performance was analyzed.

• 전력전자학회논문지
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• 제25권3호
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• pp.171-180
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• 2020

This paper presents the implementation and control methods of a battery simulator. The proposed battery simulator can emulate the dynamic characteristics of any actual battery using the second RC ladder model of the equivalent circuit. Moreover, it can emulate the variation of impedance, which is the result of the change of battery characteristics due to the aging effect. The parameters of the battery simulator can be derived from the sequence of tests of the actual battery or only from the data supplied by the battery manufacturer. Proposed methods for the battery simulator are tested by extensive experiments. Test results show that the proposed battery simulator can emulate not only the dynamic characteristics but also the aging effects of the actual battery in real time.

• 조명전기설비학회논문지
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• 제24권12호
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• pp.84-90
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• 2010

In order to level electric power of the photovoltaic and wind-turbine system and ensure fast response of the fuel-cell and micro-turbine, the energy storage is required in the microgrid system. In this paper, a simplified simulation model of the battery energy storage for charging method with IUIa is developed using PSCAD/EMTDC. The model consists of e.m.f.(electromotive force), internal resistor and overvoltage capacitor. A method for deciding parameters of the model on a case-by-case basis is proposed. The developed model can be used in the simulation of a complicated system such as a microgrid system.

• Journal of applied mathematics & informatics
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• 제39권5_6호
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• pp.805-811
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• 2021

Currently, batteries use commonly as energy sources for mobile electric devices. Due to the high density of energy, the energy storage state of a battery is very important information. To know the battery's energy storage state, it is necessary to find out the open state voltage of the battery. The open state voltage calculates with a mathematical model, but the computation of the real time state is complicated and requires many calculations. Therefore, the state observer designs to estimate in real time the battery open-circuit voltage as disturbance including model error. Using the estimated open voltage and applying it to the state estimation algorithm, we can estimate the charge. In this study, we first estimate the open-circuit voltage and design an estimation algorithm for estimating the state of battery charge. This includes errors in the system model and has a robust characteristic to noise. It is possible to increase the precision of the charge state estimation.

• 대한임베디드공학회논문지
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• 제17권2호
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• pp.85-92
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• 2022

Currently, various researches on electric vehicle battery systems have been conducted from the viewpoint of safety and performance for SoC, SoH, etc. However, it is difficult to build a precise electrical model of a battery system based on the chemical reaction and SoC prediction. Experimental measurements and predictions of the battery SoC were usually performed using dynamometers. In this paper, we construct a simulation model of an electric vehicle system using Matlab Simulink, and confirm the response characteristics based on the vehicle test driving profiles. In addition, we show that it is possible to derive the correlation between the SoC, voltage, and current of the battery according to the driving time of the electric vehicle in conjunction with the BMS model.

• Journal of Power Electronics
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• 제12권1호
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• pp.40-48
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• 2012

This paper proposes the State-of-charge (SOC) estimator of a LiPB Battery using the Extended Kalman Filter (EKF). EKF can work properly only with an accurate model. Therefore, the high accuracy electrical battery model for EKF state is discussed in this paper, which is focused on high-capacity LiPB batteries. The battery model is extracted from a single cell of LiPB 40Ah, 3.7V. The dynamic behavior of single cell battery is modeled using a bulk capacitance, two series RC networks, and a series resistance. The bulk capacitance voltage represents the Open Circuit Voltage (OCV) of battery and other components represent the transient response of battery voltage. The experimental results show the strong relationship between OCV and SOC without any dependency on the current rates. Therefore, EKF is proposed to work by estimating OCV, and then is converted it to SOC. EKF is tested with the experimental data. To increase the estimation accuracy, EKF is improved with a single dominant varying parameter of bulk capacitance which follows the SOC value. Full region of SOC test is done to verify the effectiveness of EKF algorithm. The test results show the error of estimation can be reduced up to max 5%SOC.