  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.
