TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Hell, Sebastian Matthias
A1  - Kim, Chong Dae
ED  - Gerbaldi, Claudio
T1  - Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction
JF  - Batteries
N2  - 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.
KW  - lithium-ion batteries
KW  - remaining-useful-life (RUL)
KW  - gated recurrent unit neural network (GRU NN)
KW  - real-world data
KW  - Lithium-Ionen-Akkumulator
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-20639
SN  - 2313-0105
SS  - 2313-0105
U6  - https://doi.org/10.3390/batteries8100192
DO  - https://doi.org/10.3390/batteries8100192
VL  - 8
IS  - 10
SP  - 12
S1  - 12
PB  - MDPI
ER  -