@article{HellKim2022,
  author    = {Sebastian Matthias Hell and Chong Dae Kim},
  title     = {Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction},
  series   = {Batteries},
  volume    = {8},
  number    = {10},
  editor    = {Claudio Gerbaldi},
  publisher = {MDPI},
  issn      = {2313-0105},
  doi       = {10.3390/batteries8100192},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-20639},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}