Journal of the Microelectronics and Packaging Society (마이크로전자및패키징학회지)

The Korean Microelectronics and Packaging Society (한국마이크로전자및패키징학회)
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State of Health and State of Charge Estimation of Li-ion Battery for Construction Equipment based on Dual Extended Kalman Filter

이중확장칼만필터(DEKF)를 기반한 건설장비용 리튬이온전지의 State of Charge(SOC) 및 State of Health(SOH) 추정

  • Hong-Ryun Jung (School of Chemical Engineering, Chonnam National University) ;
  • Jun Ho Kim (School of Chemical Engineering, Chonnam National University) ;
  • Seung Woo Kim (School of Chemical Engineering, Chonnam National University) ;
  • Jong Hoon Kim (Department of Electrical Engineering, Chungnam National University) ;
  • Eun Jin Kang (Department of Electrical Engineering, Chungnam National University) ;
  • Jeong Woo Yun (School of Chemical Engineering, Chonnam National University)
  • 정홍련 (전남대학교 화학공학과) ;
  • 김준호 (전남대학교 화학공학과) ;
  • 김승우 (전남대학교 화학공학과) ;
  • 김종훈 (충남대학교 전기공학과) ;
  • 강은진 (충남대학교 전기공학과) ;
  • 윤정우 (전남대학교 화학공학과)
  • Received : 2023.12.07
  • Accepted : 2024.01.10
  • Published : 2024.03.30
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Abstract

Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.

전기자동차와 신재생에너지에 관한 관심이 높아지면서 건설장비 산업분야에서도 리튬이온 배터리를 접목하려는 요구가 높아지고 있다. 건설중장비는 건설 현장의 다양한 작업으로 인해 전류 용량의 감소가 급속히 진행되기 때문에 SOC(State of Charge) 및 SOH(State of Health) 같은 배터리의 상태를 더욱 정확하게 추정할 필요가 있다. 본 논문에서는 SOC와 SOH를 동시에 추정이 가능한 적응제어 기법 기반 이중확장칼만필터(Dual Extended Kalman Filter, DEKF) 알고리즘을 이용하여 실제 측정데이터와의 오차를 비교하였다. 배터리 충전 상태 예측을 위해 배터리 셀을 완전 충전 후 0.2C-rate조건에서 SOC 5% 간격으로 OCV를 측정하였고, 배터리의 열화를 판단할 수 있는 건전성 지표 확보를 위해 다양한 C-rate(0.2, 0.3, 0.5, 1.0, 1.5C rate) 조건에서 50 Cycle 동안 노화 실험을 수행하였다. DEKF를 이용한 SOC 및 SOH 추정 오차는 C-rate이 커질수록 커지는 경향을 보였으며 특히 SOC 추정결과, 0.2, 0.5 및 1C-rate에서 6%이하로 나타남을 확인하였다. 또한 SOH 추정 결과는 0.2 와 0.3C-rate에서 각각 최대오차 1.0% 및 1.3% 이내로 좋은 성능을 보이는 것으로 확인하였다. 다만, C-rate가 0.5C-rate에서 1.5C-rate으로 증가함에 따라 추정오차도 1.5%에서 2%로 다소 증가하는 것을 확인할 수 있었으나, 모든 C-rate 조건에서 DEKF를 사용한 SOH의 추정 성능은 약 2% 이내인 것으로 나타났다.

Keywords

Acknowledgement

이 연구는 2022년도 산업통상자원부 및 산업기술평가관리원(KEIT) 연구비 지원에 의한 연구임('과제번호: 00155172')

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