A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
Beihang University · St. Francis Xavier University · +1 more institution
Abstract
Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent. Industrial big data, recording all aspects of the industrial production process, contain the key value for industrial intelligence. For industrial manufacturing, an essential and widely used electronic device is the lithium-ion battery (LIB). However, accurately predicting the remaining useful life (RUL) of LIB is urgently needed to reduce unexpected maintenance and avoid accidents. Due to insufficient amount of degradation data, the prediction accuracy of data-driven methods…
Citation impact
- FWCI
- 27.71
- Percentile
- 100%
- References
- 28
Authors
6Topics & keywords
- Computer science
- Big data
- Autoencoder
- Process (computing)
- Deep learning
- Data modeling
- Convolutional neural network
- Battery (electricity)