Battery ageing is inevitable during operation, leading not only to performance degradation but to potential safety concerns. Consequently, accurate prediction of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safety and reliability. This study proposed a novel hybrid neural network architecture that integrates a transformer module, an empirical degradation (ED) model, and a gated recurrent unit (GRU). The transformer module enhances the global representation of the feature sequence, while the ED model comprehensively considers the impact of temperature on the rate of battery capacity degradation, compensating the un-interpretability of the transformer architecture in predicting SOH. In addition, pseudo-incremental capacity curves have been obtained using charging fragments from multi-stage constant current fast charging, which solves the issue of extracting mechanism features under fast charging conditions. Experimental results demonstrate that, across a wide temperature range, the model maintains a low average RMSE between 0.43% and 0.59% for prediction horizons of 4 to 128 cycles. Specifically, the average RMSE is 0.87% at −5 °C and 0.37% between 25 °C and 55 °C. Compared to standalone data-driven models, the proposed hybrid architecture reduces prediction error by approximately 50% at 25 °C, exhibiting superior predictive performance and robustness.
Lei et al. (Mon,) studied this question.