An Improved SegRNN-Based Hierarchical Warning Method for Power Battery Thermal Runaway

Early detection of thermal runaway in electric vehicle batteries remains challenging, and warning lead times during real-world operation are often limited. This paper proposes a hierarchical thermal runaway warning method based on an improved SegRNN model. A cross-variable self-attention module and a one-dimensional convolutional channel fusion layer are integrated into the SegRNN encoder to improve voltage prediction accuracy. One year of operational data from 23 electric vehicles of the same model, including 17 normal vehicles and 6 vehicles with confirmed thermal runaway events, is used for validation. Pearson correlation analysis is applied to select five input variables for charging and discharging conditions separately. A three-level warning strategy is developed based on voltage prediction residuals, with thresholds of 0.05, 0.12, and 0.24 V. On the test set, the proposed model achieves MAE and RMSE values of 0.48624 × 10−3 and 0.64961 × 10−3 during charging, and 3.76869 × 10−3 and 8.2487 × 10−3 during discharging, respectively. Retrospective validation on the six thermal runaway vehicles shows average warning lead times of 87.3 days for Level 1 and 2.3 days for Level 3. These results demonstrate the potential of the proposed method for early thermal runaway risk identification under real-world operating conditions.