A Data-Model Interactive Remaining Useful Life Prediction Approach of Lithium-Ion Batteries Based on PF-BiGRU-TSAM
Harbin Institute of Technology · National Taipei University of Technology · +2 more institutions
Abstract
Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for energy supply systems. In conventional data-driven RUL prediction approaches, the battery's degradation mechanism is difficult into incorporate in the RUL prediction. Furthermore, there are notable limitations in reflecting the significance of different time instances, and the uncertainty in the degradation process. Consequently, a novel data-model interactive RUL prediction approach based on particle filter-temporal attention mechanism-bidirectional gated recurrent unit (PF-BiGRU-TSAM) is proposed. Specifically, BiGRU-TSAM is trained offline through historical data, which assigns corresponding significance to battery…
Citation impact
- FWCI
- 19.26
- Percentile
- 100%
- References
- 35
Authors
7Topics & keywords
- Battery (electricity)
- Degradation (telecommunications)
- Computer science
- Particle filter
- Process (computing)
- Battery capacity
- Data modeling
- Reliability engineering