[논문]Internal Parameter Estimation of Lithium-Ion Battery Using AC Ripple With DC Offset Wave in Low and High Frequencies

Song, Kwang Seok; Park, Sung-Jun; Kang, Feel-Soon

doi:10.1109/access.2021.3082148

[해외논문] Internal Parameter Estimation of Lithium-Ion Battery Using AC Ripple With DC Offset Wave in Low and High Frequencies 원문보기

IEEE access : practical research, open solutions, v.9, 2021년, pp.76083 - 76096

Song, Kwang Seok (Chonnam National University, Gwangju, South Korea) , Park, Sung-Jun (Chonnam National University, Gwangju, South Korea) , Kang, Feel-Soon (Hanbat National University, Daejeon, South Korea)

Abstract ▼ AI-Helper

xEV batteries are not easy to accurately estimate and measure aging parameters for reuse due to continuous charging and discharging. In addition, existing AC-IR measurement methods require high-performance micro-controller units (MCUs) to handle complex operations for parametric estimation. As a cost-effective way to solve this problem, we propose AC-IR estimation techniques and implementation circuits to estimate internal battery parameters using AC ripple with DC offset wave in low and high frequencies. It estimates electrolyte resistance ( $R_{i}$) by using band-pass filter (BPF) to extract AC components of a particular frequency voltage and current and by entering very high frequencies that have the effect of shorting the equivalent capacitance by the electrical double layer ( $C_{d}$) of the Randls model. The charge transfer resistance ( $R_{d}$) and electrical double layer capacitance ( $C_{d}$) are estimated by Discrete Fourier transform (DFT), detecting effective and reactive current by inserting low frequencies. To realize the proposed approach, we propose analog power measurements and peak detection circuits, and a circuit configuration of converter supplying AC ripple with DC offset wave to estimate internal parameters at the same time to charge battery. To verify the feasibility and high-performance of the proposed AC-IR estimation method, we carry out PSIM simulations and experiments, and compare results with commercial battery parameter measuring instrument. As a result, we found that the error rate of the proposed method is lower than that of the commercial instrument as $R_{i}$ is 0.24% and $R_{d}$ is 1.18%, respectively.

참고문헌 (28)

Huo, Qian, Ma, Zhikai, Zhao, Xiaoshun, Zhang, Tao, Zhang, Yulong. Bayesian Network Based State-of-Health Estimation for Battery on Electric Vehicle Application and its Validation Through Real-World Data. IEEE access : practical research, open solutions, vol.9, 11328-11341.

상세보기
Xiong, Wei, Mo, Yimin, Yan, Cong. Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine. IEEE access : practical research, open solutions, vol.9, 1870-1881.

상세보기
Schaltz, Erik, Stroe, Daniel-Ioan, Nørregaard, Kjeld, Ingvardsen, Lasse Stenhøj, Christensen, Andreas. Incremental Capacity Analysis Applied on Electric Vehicles for Battery State-of-Health Estimation. IEEE transactions on industry applications, vol.57, no.2, 1810-1817.

상세보기
10.1109/QIR.2017.8168465
10.1109/IECON.2017.8217336
10.1109/INTLEC.1997.646071
10.1109/ICPE.2007.4692563
10.1109/EVER.2019.8813578
Proc 10th Int Conf Power Electron ECCE Asia (ICPE-ECCE Asia) Comparative analysis of NCM LIB equivalent parameter according to AC-DC impedance estimation method kwon 2019 27
Proc IEEE 8th Int Power Electron Motion Control Conf (IPEMC-ECCE Asia) A multistage current charging method for li-ion battery bank considering balance of internal consumption and charging speed liu 2016 22
High Voltage Battery Tester for EV and PHEV BT3564 Data Sheet 2019
Goud, J Saikrishna, R, Kalpana, Singh, Bhim. An Online Method of Estimating State of Health of a Li-Ion Battery. IEEE transactions on energy conversion, vol.36, no.1, 111-119.

상세보기
Electrical Measurements of Lithium-Ion Batteries-Fundamentals and Applications User’s Guide 2020
Wang, Zhenpo, Yuan, Changgui, Li, Xiaoyu. Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression. IEEE transactions on transportation electrification, vol.7, no.1, 16-25.

상세보기
Gao, Yizhao, Liu, Kailong, Zhu, Chong, Zhang, Xi, Zhang, Dong. Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model. IEEE transactions on industrial electronics : a publication of the IEEE Industrial Electronics Society, vol.69, no.3, 2684-2696.

상세보기
Ling, Liuyi, Wei, Ying. State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries Based on Dual Fractional-Order Extended Kalman Filter and Online Parameter Identification. IEEE access : practical research, open solutions, vol.9, 47588-47602.

상세보기
Ouyang, Tiancheng, Xu, Peihang, Lu, Jie, Hu, Xiaoyi, Liu, Benlong, Chen, Nan. Coestimation of State-of-Charge and State-of-Health for Power Batteries Based on Multithread Dynamic Optimization Method. IEEE transactions on industrial electronics : a publication of the IEEE Industrial Electronics Society, vol.69, no.2, 1157-1166.

상세보기
Meng, Jinhao, Cai, Lei, Stroe, Daniel-Ioan, Huang, Xinrong, Peng, Jichang, Liu, Tianqi, Teodorescu, Remus. An Automatic Weak Learner Formulation for Lithium-Ion Battery State of Health Estimation. IEEE transactions on industrial electronics : a publication of the IEEE Industrial Electronics Society, vol.69, no.3, 2659-2668.

상세보기
Cui, Shengmin, Joe, Inwhee. A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries. IEEE access : practical research, open solutions, vol.9, 27374-27388.

상세보기
Khan, Noman, Ullah, Fath U Min, Afnan, Ullah, Amin, Lee, Mi Young, Baik, Sung Wook. Batteries State of Health Estimation via Efficient Neural Networks With Multiple Channel Charging Profiles. IEEE access : practical research, open solutions, vol.9, 7797-7813.

상세보기
Xia, Zhiyong, Abu Qahouq, Jaber A.. State-of-Charge Balancing of Lithium-Ion Batteries With State-of-Health Awareness Capability. IEEE transactions on industry applications, vol.57, no.1, 673-684.

상세보기
Lievre, Aurelien, Sari, Ali, Venet, Pascal, Hijazi, Alaa, Ouattara-Brigaudet, Mathilde, Pelissier, Serge. Practical Online Estimation of Lithium-Ion Battery Apparent Series Resistance for Mild Hybrid Vehicles. IEEE transactions on vehicular technology, vol.65, no.6, 4505-4511.

상세보기
10.1109/EECCIS.2018.8692970
10.1109/IECON.2019.8926969
10.1109/IAEAC.2017.8054365
10.1109/SDPC.2018.8664941
Secondary Cells and Batteries Containing Alkaline or Other Non-Acid Electrolyte–Safety Requirements for Portable Sealed Secondary Cells and for Batteries Made From Them for Use in Portable Application—Part 2 Lithium Systems 2020
10.1109/VPPC.2015.7352959

LOADING...

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

원문 보기

AccessON 원문보기

원문 URL 링크

DOI : 10.1109/ACCESS.2021.3082148 [무료]
IEEE : 저널 > 논문 [무료]
AccessON : 저널

*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내

내보내기 메뉴

내보내기 구분

파일저장
인쇄
메일전송

구성항목

기본정보
상세정보

관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관

저장형식

Text(ASCII format)
Excel format
RefWorks Direct Export
RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley

메일정보

받는사람 (필수): @
보내는사람 (선택): @
제목
내용: KISTI 검색결과 이메일 서비스

안내

총 건의 자료가 검색되었습니다.

다운받으실 자료의 인덱스를 입력하세요. (1-10,000)

검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다.

데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요)

다운로드 파일은 UTF-8 형태로 저장됩니다.
파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오.

Text(ASCII format)
Excel format

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

AI-Helper ※ AI-Helper는 을 사용합니다.

AI-Helper

안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

연합인증