11-Feb-2026
AI "check-engine light" for electric vehicles: new deep learning model predicts battery health with 99% accuracy
Shanghai Jiao Tong University Journal CenterPeer-Reviewed Publication
With the rapid development of electric vehicles and energy storage systems (ESSs), accurate state-of-health (SOH) estimation for lithium-ion batteries has become crucial for ensuring safety and optimizing performance. However, SOH estimation under dynamic operating conditions remains challenging, as non-monotonic voltage profiles and irregular current patterns reduce the effectiveness of conventional measurement methods. This paper proposes a comprehensive approach that combines health feature extraction with a parallel deep learning architecture for robust SOH estimation. First, the method extracts four highly correlated health features (K, b, σΔQ, and σδΔQ) from dynamic measurement data collected by sensors, with correlation coefficients between these features and the actual SOH exceeding 0.95. These extracted features are then processed through a novel parallel Temporal Convolutional Networks (TCN)-Transformer hybrid architecture: the TCN captures multi-scale local temporal patterns, while the Transformer models global dependencies. An attention-gated fusion module dynamically integrates complementary feature representations from the two branches and adaptively weights different paths based on degradation features. Experimental validation on three standardized battery datasets (MIT, CALCE, Oxford) shows that the method achieves an estimation accuracy with a root mean square error (RMSE) below 1% under all operating conditions, representing an 8%–70% improvement over conventional methods. Attention weight analysis reveals correlations with aging mechanisms, providing interpretability for model decisions. The proposed method enables practical real-time battery health assessment in dynamic environments.