Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries
This addresses battery management challenges across industries like household appliances and large-scale applications, but it appears incremental as it builds on existing prediction methods with specific improvements.
The paper tackles the problem of early prediction of remaining useful life (RUL) for lithium-ion batteries by proposing a two-stage framework that determines the first prediction cycle and predicts degradation patterns, resulting in outperformance over conventional methods in terms of RUL prediction.
Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications. Accurate RUL prediction improves the reliability and maintainability of battery technology. However, existing methods have limitations, including assumptions of data from the same sensors or distribution, foreknowledge of the end of life (EOL), and neglect to determine the first prediction cycle (FPC) to identify the start of the unhealthy stage. This paper proposes a novel method for RUL prediction of Lithium-ion batteries. The proposed framework comprises two stages: determining the FPC using a neural network-based model to divide the degradation data into distinct health states and predicting the degradation pattern after the FPC to estimate the remaining useful life as a percentage. Experimental results demonstrate that the proposed method outperforms conventional approaches in terms of RUL prediction. Furthermore, the proposed method shows promise for real-world scenarios, providing improved accuracy and applicability for battery management.