Journal of Power Electronics

  • 제22권5호
  • /
  • Pages.850-858
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  • 2022
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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DOI QR코드

DOI QR Code

Capacity estimation of lithium-ion batteries using convolutional neural network and impedance spectra

  • Pradyumna, T. K. (Department of Electrical Engineering, Soongsil University) ;
  • Cho, Kangcheol (Department of Electrical Engineering, Soongsil University) ;
  • Kim, Minseong (Department of Electrical Engineering, Soongsil University) ;
  • Choi, Woojin (Department of Electrical Engineering, Soongsil University)
  • 투고 : 2021.12.02
  • 심사 : 2022.02.17
  • 발행 : 2022.05.20
⟨ 이전 논문 다음 논문 ⟩

초록

Battery capacity is a parameter that has a very close association with the state of health (SoH) of a Li-ion battery. Due to the complex electrochemical mechanisms behind the degradation of battery life, the estimation of SoH encounters many difficulties. To date, experiment-based methods, model-based methods, and data-driven models have been developed to estimate capacity using data from charging curves. In the case of EVs and HEVs, due to the unpredictable charge patterns employed by users, it is not known how much charging data will be available to predict the capacity at a given point in time. This paper presents a method to accurately estimate capacity using impedance curves obtained from an electrochemical impedance spectroscopy (EIS) test and a convolutional neural network (CNN). The CNN model was trained using the impedance data of a single cell collected at different SoH values, and the trained model was verified using the impedance data of eight other cells. The maximum error in the prediction was found to be 0.57 (% capacity) and the RMS error was found to be 0.233 (% capacity). These results illustrate the accuracy and robustness of the proposed model.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2021R1A2C1011504)

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