Abstract
Many different prediction methods have been developed in recent years and data-driven methods are often used. The aim of this paper is to present the new method of prediction remaining useful life of components. The proposed soft computing approach bridges the fuzzy-logic and data-driven health prognostic approaches. The result of this combination is the practical method for determining the remaining useful life. Proposed method is based on a Takagi-Sugeno multiple-based framework. Compared to other data-driven methods, the proposed algorithm differs in the use of historical data in order to improve the quality of prediction and to create a flexible scheme. The entire method is used to predict remaining useful life of batteries. Finally, the validation of the proposed algorithm is made with NASA PCoE data set of Li-Ion battery. This benchmark consist run-to-failure tests.
The work was supported by the National Science Centre of Poland under Grant: UMO-2017/27/B/ST7/00620.
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The work was supported by the National Science Centre of Poland under Grant: UMO-2017/27/B/ST7/00620.
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Lipiec, B., Mrugalski, M., Witczak, M. (2023). Remaining Useful Life Prediction of the Li-Ion Batteries. In: Kowalczuk, Z. (eds) Intelligent and Safe Computer Systems in Control and Diagnostics. DPS 2022. Lecture Notes in Networks and Systems, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-16159-9_19
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