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Open AccessArticle

Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics

1
Department of Mechanical Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea
2
Korea Railroad Research Institute, Uiwang, Gyeonggi 16105, Korea
3
Department of Automotive Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea
*
Authors to whom correspondence should be addressed.
C.-J. Lee and B.-K. Kim contributed equally to this work.
Academic Editor: Sheldon Williamson
Electronics 2021, 10(7), 846; https://doi.org/10.3390/electronics10070846
Received: 16 February 2021 / Revised: 25 March 2021 / Accepted: 26 March 2021 / Published: 1 April 2021
(This article belongs to the Special Issue Advances in Control for Electric Vehicle)
We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cannot be achieved using conventional methods such as electrochemical impedance spectroscopy. The battery state can be predicted based on the change in the capacity according to the state of health. For the proposed method, a statistical analysis of capacity fade considering the impedance increase according to the degree of deterioration is conducted by applying a deep neural network to diverse data from charge/discharge characteristics. Then, probabilistic predictions based on the capacity fade trends are obtained to improve the prediction accuracy of the remaining useful life using another deep neural network. View Full-Text
Keywords: aging; battery management system; deep neural network (DNN); particle filter (PF); prognostics and health management (PHM); regression analysis; remaining useful life (RUL) aging; battery management system; deep neural network (DNN); particle filter (PF); prognostics and health management (PHM); regression analysis; remaining useful life (RUL)
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MDPI and ACS Style

Lee, C.-J.; Kim, B.-K.; Kwon, M.-K.; Nam, K.; Kang, S.-W. Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics. Electronics 2021, 10, 846. https://doi.org/10.3390/electronics10070846

AMA Style

Lee C-J, Kim B-K, Kwon M-K, Nam K, Kang S-W. Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics. Electronics. 2021; 10(7):846. https://doi.org/10.3390/electronics10070846

Chicago/Turabian Style

Lee, Chul-Jun; Kim, Bo-Kyong; Kwon, Mi-Kyeong; Nam, Kanghyun; Kang, Seok-Won. 2021. "Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics" Electronics 10, no. 7: 846. https://doi.org/10.3390/electronics10070846

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