Research Article
Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron
Issue:
Volume 10, Issue 1, December 2024
Pages:
1-17
Received:
5 September 2024
Accepted:
23 September 2024
Published:
10 October 2024
DOI:
10.11648/j.ijecec.20241001.11
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Abstract: Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model.
Abstract: Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, e...
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,
Sushil Paudel*
