- Tytuł:
- Prediction of remaining useful life for lithium-ion battery with multiple health indicators
- Autorzy:
-
Su, Chun
Chen, Hongjing
Wen, Zejun - Tematy:
-
lithium-ion (Li-ion) battery
remaining useful life
RUL
health indicator
HI
generalized regression neural network (GRNN)
non-linear autoregressive (NAR) - Pokaż więcej
- Wydawca:
- Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
- Powiązania:
- https://bibliotekanauki.pl/articles/1841757.pdf Link otwiera się w nowym oknie
- Opis:
- Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.
- Dostawca treści:
- Biblioteka Nauki
Artykuł