지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 500 - 503 (4page)
이용수
초록· 키워드
오류제보하기
Lithium-ion batteries are generally used in electric vehicles, mobile phones, and lap-tops. Such batteries demonstrate advantages such as environmental-friendliness, high energy density, and long life. However, if not continuously monitored, battery overcharging and over-discharging may occur. Overcharging causes fire and explosion casualties, and overdischarging causes a reduction in the battery capacity and life. In addition, the internal resistance of such batteries varies depending on the external temperature of the batteries, and as the temperature decreases, the capacity of the batteries decreases as well. In this paper, we propose a method for estimating the state of charge (SOC) using a
neural network model best suited for the external temperature of such batteries. Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle-driving simulator. The experimental data were provided as inputs to multi-layer neural network (MNN). The MNN models were trained and optimized for specific temperatures measured during the experiment, and the SOC was estimated by selecting the most suitable model for a temperature. The experimental results revealed that such an estimation of the SOC was better than that using conventional methods.
neural network model best suited for the external temperature of such batteries. Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle-driving simulator. The experimental data were provided as inputs to multi-layer neural network (MNN). The MNN models were trained and optimized for specific temperatures measured during the experiment, and the SOC was estimated by selecting the most suitable model for a temperature. The experimental results revealed that such an estimation of the SOC was better than that using conventional methods.
목차
Abstract
1. INTRODUCTION
2. VEHICLE-DRIVING SIMULATOR
3. PROPOSED SOC ESTIMATION METHOD
4. RESULTS
5. CONCLUSION
REFERENCES
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
다층 신경 회로망을 이용한 리튬배터리 Pack에서의 각 Cell의 SOC 추정
대한전자공학회 학술대회
2020 .08
-
LSTM-Based Real-Time SOC Estimation of Lithium-Ion Batteries Using a Vehicle Driving Simulator
제어로봇시스템학회 국제학술대회 논문집
2021 .10
-
Capacity estimation of lithium-ion batteries under various temperatures using two aging indicators
제어로봇시스템학회 국제학술대회 논문집
2021 .10
-
차량 시뮬레이터 및 인공신경망을 이용한 배터리의 SOC 추정
한국정보기술학회논문지
2021 .05
-
Battery State Estimation Algorithm for High-Capacity Lithium Secondary Battery for EVs Considering Temperature Change Characteristics
Journal of Electrical Engineering & Technology
2018 .09
-
이차전지 리튬이온 배터리용 시험설비를 이용한 성능 및 신뢰성 분석
전기학회논문지
2023 .12
-
리튬이온 배터리의 고온 테스트 실험
한국조명·전기설비학회 학술대회논문집
2021 .05
-
해수이차전지 온도별 SoC 추정을 위한 특성 분석 실험
한국정밀공학회 학술발표대회 논문집
2023 .05
-
Study of Battery State-of-charge Estimation with kNN Machine Learning Method
IEIE Transactions on Smart Processing & Computing
2021 .12
-
배터리 내부 온도를 고려를 위한 실험적 열 모델 기반 리튬 인산 철 배터리의 SOC 추정 알고리즘
전기학회논문지
2024 .12
-
리튬이온 배터리의 열관리가 전기자동차 주행거리에 미치는 영향
한국산학기술학회 논문지
2017 .05
-
전기자동차 모델링 및 배터리 SOC 추정
제어로봇시스템학회 논문지
2018 .01
-
전기자동차용 리튬-이온 배터리의 온도 특성을 고려한 충전 제어 기법
조명·전기설비학회논문지
2018 .04
-
리튬 배터리 등가모델의 정확도 개선을 위한 SOC 계수 보정법
전자공학회논문지
2017 .04
-
A study on artificial intelligence applied modeling for battery cooling system operation optimization
대한설비공학회 학술발표대회논문집
2022 .06
-
Cell-balancing Algorithm for Paralleled Battery Cells using State-of-Charge Comparison Rule
전력전자학회 학술대회 논문집
2018 .07
-
전기자동차용 배터리의 수명 예측을 위한 열전달 모델 개발
대한기계학회 춘추학술대회
2017 .11
-
Internal Structure Optimization to enhance the Thermal Performance of an Air-cooled Lithium-ion Battery Pack
한국기계가공학회지
2021 .12
-
리튬 이차전지의 저온 성능 개선을 위한 에너지 순환 작동 연구
전력전자학회논문지
2021 .12
-
Real-Time State of Charge estimation of Li-Ion Battery Considering The Effect of State of Health
한국통신학회 학술대회논문집
2021 .02
이 논문의 저자 정보
최근 본 자료
댓글(0)
0