• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.269-270
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• 2010

본 논문은 리튬 폴리머 배터리(LiPB)의 OCV(Open Circuit Voltage;개방전압)를 이용한 배터리 SOH(State Of Health;잔존수명) 추정하는 방법의 제안이다. 종래에는 배터리 수명은 제조회사에서 지정된 시간이나 충방전 횟수를 기초로 수명을 결정하였다. 하지만 배터리의 온도, 충전방법, 전류변화 및 DOD(Depth of Discharge;방전심도) 정도에 따라 배터리 수명은 유동적이다. 따라서 배터리가 노후됨에 따라 OCV가 변한다는 원리를 이용하여 임피던스 분석을 통해서 SOH, 즉 배터리 잔존수명을 추정하는 기술을 제안하였다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.47-49
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• 2019

본 논문은 노화로 인하여 감소하는 전기자동차용 배터리의 전류 용량을 실시간으로 추정하는 SOH (State-of-health) 알고리즘을 제안한다. 제안하는 알고리즘은 노화에 따른 OCV (Open circuit voltage) 곡선의 변화 경향을 분석하고, 저주파 통과 필터를 이용하여 추정된 OCV를 기반으로 전류 용량 및 SOH를 산출한다. 알고리즘을 검증하기 위하여 전기자동차용 배터리를 이용한 실험 및 시뮬레이션을 진행한다.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.379-380
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• 2020

배터리의 효율적인 관리와 안정적인 운영을 위해서는 배터리의 노화에 따른 배터리의 모니터링이 중요하다. 그 중에서도 노화에 대한 문제는 실제 어플리케이션에서 매우 중요하기 때문에 더 정확하고 안정적인 운영을 위해서는 배터리 잔존 수명을 판단하는 지표인 State of Health (SOH)가 필수적이다. 따라서 실험을 통한 UDDS의 전압 차 (Voltage Difference) 이미지를 학습데이터로 구성하여, SOH의 파라미터인 용량을 추정하는 Convolutional Neural Network(CNN) 모델을 제안한다.

• Journal of IKEEE
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• v.26 no.4
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• pp.531-536
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• 2022

In order to efficeintly manage a battery, it is important to accurately estimate and manage the SOH(State of Health) and RUL(Remaining Useful Life) of the batteries. Even if the batteries are of the same type, the characteristics such as facility capacity and voltage are different, and when the battery for the training model and the battery for prediction through the model are different, there is a limit to measuring the accuracy. In this paper, We proposed the entropy index using voltage distribution and discharge time is generalized, and four batteries are defined as a training set and a test set alternately one by one to predict the health status of batteries through linear regression analysis of machine learning. The proposed method showed a high accuracy of more than 95% using the MAPE(Mean Absolute Percentage Error).

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.56-57
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• 2017

본 논문에서는 배터리의 노화에 따른 내부 저항 및 전압변동(Differential Voltage; DV)곡선 변화를 실시간으로 추정하는 SOH (State of Health) 알고리즘을 개발한다. 개발된 알고리즘은 정확한 내부 저항 추정을 위해 동작 및 측정 환경에 따른 고주파 통과 필터의 최적 설계 방안을 제안하며 동적 전류 특성을 고려한 DV곡선의 온라인 업데이트 로직을 이용한다. 알고리즘의 정확도는 단전지 시험 결과를 기반으로 시뮬레이션을 통해 검증한다.

• 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.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.465-466
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• 2015

배터리 관리시스템(BMS;battery management system)의 중요 고려요소인 SOC(state-of-charge) 및 SOH(state-of-health)의 전기적 등가회로 모델 기반 고성능 추정의 전제 조건은 배터리 단자전압의 안정된 실험데이터 확보이다. 그러나, 예상치 않은 에러로 인해 배터리 단자전압에 노이즈 성분이 포함될 경우 SOC 및 SOH 추정알고리즘의 성능저하가 우려된다. 이를 위해, 본 논문은 이산 웨이블릿 변환(DWT;discrete wavelet transform)의 다해상도 분석(MRA;multi resolution analysis) 레벨에 따른 디노이징 최적 성능을 소개하고자 한다. 하드 임계화(hard-thresholding) 및 소프트 임계화(soft-thresholding) 기법에 따른 디노이징 성능 차이를 보이고, 각 임계화 기법 적용 시 디노이징 최적 성능을 보이는 레벨을 선택한다.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_1
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• pp.669-681
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• 2021

As the supply of xEV in Korea is rapidly increasing, the amount of waste batteries is expected to increase rapidly, but the current recycling system for waste xEV batteries is very insufficient. In order to properly utilize the xEV reusable battery module, it is essential to classify it into a type that has similar discharge characteristics to the current state of health(SOH), which is the discharge capacity of the battery. This paper proposes a system that can minimize the exchange of energy with the KEPCO system by using the charging/discharging method by circulating power between batteries in order to minimize the power consumption when charging and discharging waste batteries. In the proposed system, a function to measure parameters during the charging/discharging test of the waste battery was implemented to build a customized big date for the test waste battery. In addition, the dynamic characteristics of the proposed circuit were analyzed using PSIM, which is useful for power electronics analysis, and the validity of the proposed circuit was verified through experiments.

• 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.