Enhancing lithium-ion battery lifespan early prediction using a multi-branch vision transformer model

Accurately predicting the lifespan of lithium-ion batteries is crucial for effective battery management systems, particularly for ensuring the safe operation and proactive maintenance of electric vehicles. However, existing methods encounter challenges due to the early weak capacity aging trend in batteries. This study a novel approach using a multibranch vision transformer model to address early stage lifespan prediction issues. The proposed model leverages a multi-input data structure by considering various battery parameters during the charging and discharging phases. By employing distinct branch structures for different inputs, the model can separately extract features from individual input variables. Each vision transformer network was meticulously designed to extract high-dimensional global hidden features from the inputs and integrated to predict the battery lifespan. Comparative analyses against advanced baseline models and existing methods consistently demonstrate the superior performance and robustness of the proposed model. Compared with traditional vision transformer, the proposed model demonstrated a notable reduction in root mean square errors of 13.17 % and 6.32 % on the two public datasets, respectively, indicating the efficacy and reliability of our approach for accurately predicting the lifespan of LIBs during the early stages of capacity decline.