The Lithium-Ion Battery Temperature Field Prediction Model Based on CNN-Bi-LSTM-AM

This study focuses on the internal temperature field of lithium-ion batteries, aiming to address the temperature variation issues arising from complex operating conditions in new energy batteries. To cope with unpredictable temperature fluctuations and long delay times, we propose an enhanced Convolutional Bidirectional Long Short-Term Memory Neural Network (CNN-Bi-LSTM-AM) model for temperature field prediction. The model integrates CNN for spatial feature extraction, Bi-LSTM for capturing temporal characteristics, and an attention mechanism to enhance the identification of key time-series features. By simulating temperature variations through a lumped model and thermal runaway model, we generate temperature field data, which are then utilized by the deep learning model to effectively capture the complex nonlinear relationships between temperature, voltage, state of charge (SOC), insulation resistance, current, and the internal temperature field. Performance evaluation using accuracy metrics and validation under various environmental conditions demonstrates that the model improves prediction accuracy by 1.2–2.3% compared to traditional methods (e.g., ARIMA, LSTM) with only a slight increase in testing time. Comprehensive evaluations, including ablation studies, thermal runaway tests, and computational efficiency analysis, further validate the robustness and applicability of the model. Furthermore, this study contributes to the optimization of battery life and safety by enhancing the prediction accuracy of the internal temperature field, thereby reducing resource waste caused by battery performance degradation. The findings provide an innovative approach to advancing new energy battery technology, promoting its development toward greater safety, stability, and environmental sustainability, which aligns with global sustainable development goals.