전기전자학회논문지 (Journal of IKEEE)

  • 제24권1호
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  • Pages.333-338
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  • 2020
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  • 1226-7244(pISSN)
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  • 2288-243X(eISSN)
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)
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뉴럴 네트워크를 이용한 배터리 셀 SOC 추정

Battery Cell SOC Estimation Using Neural Network

  • Ryu, Kyung-Sang (Electric Power System Research Team, Korea Institute of Energy Research (KIER)) ;
  • Kim, Ho-Chan (Dept. of Electrical Engineering, Jeju National University)
  • 투고 : 2020.03.12
  • 심사 : 2020.03.20
  • 발행 : 2020.03.31
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초록

본 논문은 역전파 뉴럴 네트워크(Back Propagation Neural Network; BPNN) 알고리즘을 이용한 배터리 셀의 잔존용량(State Of Charge; SOC) 추정 방법을 제안한다. 이를 위해 배터리 성능평가 시뮬레이터를 구현하고 다양한 온도에서의 충방전 실험을 통해 뉴럴 네트워크 학습에 필요한 입출력 데이터를 도출한다. 최종적으로 배터리의 SOC 추정 성능은 Matlab/Simulink 프로그램을 이용하여 Ah-counting에 의한 실험치와 비교를 통해 분석하고 오차율을 3% 미만으로 줄일 수 있음을 시뮬레이션을 통해 확인한다.

This paper proposes a method of estimating the SOC(State of Charge) of a battery cell using a neural network algorithm. To this, we implement a battery SOC estimation simulator and derive input and output data for neural network learning through charge and discharge experiments at various temperatures. Finally, the performance of the battery SOC estimation is analyzed by comparing with the experimental value by Ah-counting using Matlab/Simulink program and confirmed that the error rate can be reduced to less than 3%.

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참고문헌

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