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Prediction of Remaining Useful Life (RUL) of Lithium ion (Li-ion) Batteries

Shukla, Rashmikant T. (2020) Prediction of Remaining Useful Life (RUL) of Lithium ion (Li-ion) Batteries. Masters thesis, Dublin, National College of Ireland.

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In recent time Li-ion battery gained popularity because of their high charge density, portability and longer life span. It compliments the human quest for green energy. As many of the green energy applications like Electrical Vehicle, Wind Energy and Solar Energy use Li-ion battery as their energy storage device. So, a better and intelligent Remaining Useful Life (RUL) prediction model will improve the reliability of these systems. This research states that autoencoder can be used to learn time-based battery parameter and their dimension reduction. Features from an autoencoder are fed into another neural network which predicts RUL of battery. NASA battery degradation data set is used for this analysis and target data are extracted based on the geometric features. The model evaluation is based on R-square and Mean Squared Error.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 25 Jan 2021 15:10
Last Modified: 25 Jan 2021 15:10

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