<p>Predicting the Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring the reliability and safety of devices in applications like electric vehicles, mobile devices, and renewable energy storage systems. Current RUL prediction methods perform well during certain phases of battery degradation. However, their overall performance remains limited due to complex data patterns and changing statistical properties over time. Models relying on local features or standard Transformer attention often fail to capture the non-stationary nature of degradation data. To address these challenges, we introduce AutoDS, a framework designed to forecast reconstructed degradation series using De-stationary attention. AutoDS incorporates a specialized attention map within the Transformer architecture to better capture temporal variations in non-stationary degradation data. An auto-correlation module refines degradation series at the sub-series level, removing redundancies while retaining key patterns. The framework then uses a De-stationary Transformer to model global non-stationary patterns. It restores the original attention map using statistical metrics from normalization, preserving both global relevance and non-stationarity in predicted and historical data. AutoDS employs an end-to-end design to ensure accurate and efficient forecasting. Experimental results show that AutoDS consistently outperforms leading RUL prediction and time series forecasting methods across two datasets.</p>