[논문]배터리 파라미터 측정을 이용한 충전종지전압 예측기법

김호용; 왕이페이; 박성미; 박성준; 손경종

doi:10.21289/ksic.2022.25.3.387

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

한국산업융합학회 논문집 = Journal of the Korean Society of Industry Convergence, v.25 no.3, 2022년, pp.387 - 396

김호용 (전남대학교 전기공학과) , 왕이페이 (전남대학교 전기공학과) , 박성미 (한국승강기대학교 승강기공학부) , 박성준 (전남대학교 전기공학과) , 손경종 (전남대학교 전기공학과)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (15)

그림 Fig. 1 EOC Predcition algorithm
그림 Fig. 2 Simplified battery cell model
그림 Fig. 3 Thevenin ECM with 1st RC ladder
그림 Fig. 4 Changes in internal parameters due to DC
그림 Fig. 5 DCIR model of Battery
그림 Fig. 6. 2-level current pulse
그림 Fig. 7 Voltage response by 2-level current pulse
그림 Fig. 8 DCIR variation due to CC charge
그림 Fig. 9 LPF value and cumulative average value of DCIR
그림 Fig. 10 DCIR LPF value participating in numerical analysis
그림 Fig. 11 Quadratic Function Modeling with LSM
그림 Fig. 12 Charging-end-point in Charging-end-point region
그림 Fig. 13 DCIR variation at 25°C
그림 Fig. 14 Prediction of Charging-end-point
표 Table 1. Optimal quadratic function, EOC point (P_end), EOC voltage(V_oc)

참고문헌 (11)

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상세보기
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상세보기
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상세보기
Y. Song, M. Park, M. Seo and S. W. Kim, "Improved SOC estimation of lithium-ion batteries with novel SOC-OCV curve estimation method using equivalent circuit model", 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1-6, (2019).

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