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Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction

  • Remaining-useful-life (RUL) prediction of Li-ion batteries is used to provide an early indication of the expected lifetime of the battery, thereby reducing the risk of failure and increasing safety. In this paper, a detailed method is presented to make long-term predictions for the RUL based on a combination of gated recurrent unit neural network (GRU NN) and soft-sensing method. Firstly, an indirect health indicator (HI) was extracted from the charging processes using a soft-sensing method that can accurately describe power degradation instead of capacity. Then, a GRU NN with a sliding window was applied to learn the long-term performance development. The method also uses a dropout and early stopping method to prevent overfitting. To build the models and validate the effectiveness of the proposed method, a real-world NASA battery data set with various battery measurements was used. The results show that the method can produce a long-term and accurate RUL prediction at each position of the degradation progression based on several historical battery data sets.

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Author:Sebastian Matthias Hell, Chong Dae Kim
Parent Title (English):Batteries
Editor:Claudio Gerbaldi
Document Type:Article
Date of first Publication:2022/10/18
Date of Publication (online):2022/11/07
Tag:gated recurrent unit neural network (GRU NN); lithium-ion batteries; real-world data; remaining-useful-life (RUL)
Page Number:12
Institutes:Anlagen, Energie- und Maschinensysteme (F09) / Fakultät 09 / Institut für Produktentwicklung und Konstruktionstechnik
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
Open Access:Open Access
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International