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Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

1
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
Quanzhou Institute of Equipment Manufacturing Haixi Institutes, Chinese Academy of Science, Quanzhou 362200, China
3
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2078; https://doi.org/10.3390/app8112078
Received: 28 September 2018 / Revised: 20 October 2018 / Accepted: 25 October 2018 / Published: 28 October 2018
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Abstract

On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods. View Full-Text
Keywords: Lithium-ion battery; remaining useful life; health indictor; long short-term memory Lithium-ion battery; remaining useful life; health indictor; long short-term memory
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Wang, C.; Lu, N.; Wang, S.; Cheng, Y.; Jiang, B. Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery. Appl. Sci. 2018, 8, 2078.

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