• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.114-117
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• 2001

In the Electric Vehicle(EV) driving system, the Battery Management System(BMS) is very important and an essential equipment. Particularly, BMS monitors the State of Charge(SOC), voltage, current, and temperature of the battery modules when Electric Vehicle is in the state of motoring or charging. Major roles of BMS are like these the first, estimation of State of Charge(SOC), the second, detection of the unbalance of the voltage between battery modules, the third, control of the available limit of the voltage and temperature of batteries by monitoring the batteries status during motoring or charging. In this research, We have focused on estimating SOC of battery according to the status of Electric Vehicle and the BMS operation algorithm. The result for algorithm of SOC estimation is presented. It have been modified, compensated, and verified by means of the experiment.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.853-863
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• 2020

As the battery ages, the internal resistance of the battery increases, so the loss due to the internal resistance increases at the same charging current, causing the battery temperature to rise, which further accelerates battery aging. Therefore, it is necessary to optimize the charging conditions according to the aging of the battery or the current charge amount, and to accurately estimate this, estimation of the parameters of the equivalent circuit is most important. This paper proposes a new measurement technique that can measure the internal resistance of a battery by analyzing a specific high frequency voltage and current applied to the battery. In addition, in order to test the validity of the proposed measurement technique, the current charging amount was estimated based on the measured internal resistance, and the terminal voltage of the constant current charging mode was automatically set and operated. As a result, good results were obtained regardless of the battery voltage. If this equipment is installed in the charging device, it is believed that it will be of great help in the stability management of the aging reusable battery.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.1
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• pp.119-123
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• 2012

In traditional vehicles, a great amount of energy is dissipated by braking. In electric vehicles (EVs), however, electric motors can be controlled to operate as generators to convert kinetic and potential energy of vehicles into electrical energy and store it in batteries. In this paper, the relationship between regenerative braking factor and battery final SOC is derived and the final SOC from the relationship is compared to that from simulation. Two types of braking algorithms are introduced and applied to an EV, and the final SOC derived from simulation is compared to that derived from the relationship.

• Particle and aerosol research
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• v.16 no.1
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• pp.19-30
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• 2020

The organic carbon in the ambient particulate matter (PM) is divided into primary organic carbon (POC) and secondary organic carbon (SOC) by their formation way. To regulate PM effectively, the estimation of the amount of POC and SOC separately is one of important consideration. Since SOC cannot be measured directly, previous studies have evaluated determination of SOC by the EC tracer method. The EC tracer method is a method of estimating the SOC value from calculating the POC by determining (OC/EC)pri which is the ratio of the measured values of OC and EC from the primary combustion source. In this study, three different ways were applied to OC and EC concentrations in PM_2.5 measured at Seoul for determining (OC/EC)pri: 1) the minimum value of OC/EC ratio during the measurement period; 2) regression analysis of OC vs. EC to select the lower 5-20% OC/EC ratio; 3) determining the OC/EC ratio which has lowest correlation coefficient value (R²) between EC and SOC which is reported as minimum R squared method (MRS). Each (OC/EC)pri ratio of three ways are 0.35, 1.22, and 1.77, respectively from the 1 hourly data. We compared the (OC/EC)pri ratio from 1hourly data with 24 hourly data and revealed that (OC/EC)pri estimated from 24 hourly data had twice larger than 1hourly data due to the low time resolution of sampling. We finally confirmed that the most appropriate value of (OC/EC)pri is that calculated by a regression analysis of 1 hourly data and estimated SOC amounts at PM_2.5 of the Seoul atmosphere.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.839-844
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• 2008

A novel state of health estimation method for hybrid electric vehicle lithium battery using sliding mode observer has been presented. A simple R-C circuit method has been used for the lithium battery modeling for the reduced calculation time and system resources due to the simple matrix operations. The modeling errors of simple model are compensated by the sliding mode observer. The design methodology for state of health estimation using dual sliding mode observer has been presented in step by step. The structure of the proposed system is simple and easy to implement, but it shows robust control property against modeling errors and temperature variations. The convergence of proposed observer system has been proved by the Lyapunov inequality equation and the performance of system has been verified by the sequence of urban dynamometer driving schedule test. The test results show the proposed observer system has superior tracking performance with reduced calculation time under the real driving environments.

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.222-223
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• 2013

본 논문에서는 배터리 SOC 추정 정확도 향상을 위해 기존 EKF 추정 기법에 MMAE 방법을 접목시키는 방법을 제안한다. 노이즈의 세기에 따라 EKF 출력에 비중을 부여함으로써 배터리 사용 전 영역에서 SOC 추정 오차 저감이 가능하며, Matlab 시뮬레이션을 통하여 MMAE-EKF 알고리즘의 타당성을 검증하였다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.445-446
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• 2019

전기적 등가회로의 모델의 정확도 향상을 위하여 정확한 내부 저항과 OCV의 반영은 필수적이며, 이를 위한 OCV 실험에서 SOC 구간을 작게 작을수록 OCV의 정확도는 향상되지만 실험시간은 증가한다. 따라서 실험 시간을 고려한 적당한 SOC(5%, 10%) 구간으로 실험을 진행하며, 측정 되지 않은 영역의 내부 파라미터는 선형보간법으로 등가회로 모델에 반영한다. 이러한 문제로, 본 연구는 SOC 추정에의 주요 인자인 OCV의 추정 기법으로 뉴럴 네트워크(Neural Network)를 사용하였다. 추정 방법은 뉴럴 네트워크로 기존 OCV 실험 데이터를 학습하여 모델을 구축한다. 학습 모델의 입력값으로 용량 실험 데이터의 전압, 전류를 적용하였고 결과로 얻은 SOC-OCV 곡선을 비교 분석하였다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.198-200
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• 2019

배터리의 상태를 추정하기 위해 전압과 전류 데이터는 사용자가 센서를 통해 얻을 수 있는 정보이며, 이때 노이즈 성분이 포함된 전압 및 전류 데이터는 배터리의 상태 추정을 할 때 정확도를 크게 감소시킬 수 있다. 기존의 확장 칼만필터(EKF, Extended Kalman Filter)를 사용하여 노이즈 성분이 포함된 데이터를 통해 배터리의 상태를 추정했을 때는 노이즈의 영향으로 인해 추정 정확도가 떨어진다. 본 논문은 적응형 칼만 필터(AKF, Adaptive Kalman Filter)를 사용하여 노이즈 분산값을 업데이트 해줌으로써 SOC추정 성능을 향상시켰다. 실험 및 배터리의 모델링은 21700 NMC 고용량 배터리를 사용하였으며, 배터리의 전압에 임의의 노이즈 성분을 추가하여 배터리의 SOC를 추정 정확도를 검증 하였다.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.129-130
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• 2018

리튬 이온 배터리가 전기 자동차 및 다양한 어플리케이션에 적용됨에 따라 배터리 관리 시스템(BMS)의 중요도가 높아지고 있다. 리튬 이온 배터리의 SOC(State of Charge) 및 단자전압 추정은 BMS에서 필수적이며 다양한 알고리즘을 통해 연구되고 있다. 본 논문에서는 비지도 학습 알고리즘인 뉴럴 네트워크의 학습을 위해 특성 파라미터(Characterstic Parmeter)를 선정하였으며, 특성 파라미터의 학습을 통해 리튬 이온배터리의 단자 전압 및 SOC를 추정하였다.

• 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.