Short-Term Capacity Estimation and Long-Term Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Data-Driven Method
Publication: Journal of Energy Engineering
Volume 148, Issue 6
Abstract
In this work, the characteristic data of the peaks of the incremental capacity (IC) curves, the constant-current (CC) charging time, and their neighborhoods were determined during the CC charging phases of a battery. These data were transformed into an aging characteristic series and input into a long short-term memory (LSTM) recurrent neural network to achieve an accurate short-term capacity estimate. A method was then developed to predict the long-term remaining useful life (RUL). Specifically, a double exponential empirical model (DEEM) was employed to describe the fade trend of the battery capacity. The DEEM model parameters were initialized based on the offline nonlinear least squares (NLS) method. The particle filter (PF) algorithm was then used to update the DEEM model parameters and predict the RUL based on the short-term capacity estimated by the LSTM network. The experimental results revealed that the proposed method could effectively overcome the phenomenon of lithium-ion battery capacity regeneration and inconsistency. In addition, this method could realize the RUL prediction of the continuous prediction start point (SP). Under the same prediction SP setting, the proposed method outperformed other prediction methods.
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Data Availability Statement
All data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Fei Xia and Jiajun Chen contributed equally to this work. This work was supported by National Natural Science Foundation of China (No. 71690234), National Key R&D Program of China (No. 2017YFE0100900), and Shanghai Committee of Science and Technology Innovation Program (No. 19DZ1206800).
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Published In
Journal of Energy Engineering
Volume 148 • Issue 6 • December 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jan 3, 2022
Accepted: Jul 21, 2022
Published online: Sep 29, 2022
Published in print: Dec 1, 2022
Discussion open until: Feb 28, 2023
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