A synthetic data generation method and evolutionary transformer model for degradation trajectory prediction in lithium-ion batteries

Identifying the long-term degradation of lithium-ion batteries in their early usage phase is crucial for the battery management system (BMS) to properly maintain the battery for practical use. Nevertheless, this procedure is challenging due to variations in the production and operating conditions of the battery. In recent years, it has been empirically proven that the data-driven method is a promising solution for handling the prediction of degradation. However, the lack of appropriate data remains the main obstacle that impacts the ultimate performance of the prediction. Furthermore, the prediction is also influenced by the setup of the predictor, which covers the structure of neural networks and their hyperparameters. The challenge of automating this process remains unresolved. In this study, we propose a novel degradation trajectory prediction framework. First, synthetic data is generated via a conditional generative adversarial network (CGAN), providing the characterization of the battery’s degradation at an early stage and utilizing the argument data to alleviate the issue of insufficient data. Second, an evaluation method to evaluate the quality of the synthetic data is also provided. In addition, a selection method is proposed based on the diversity mechanism to further filter out the redundancy of synthetic data. These two sub-processes aim to promote the quality of the synthetic data. Finally, the synthetic data hybrid with real values is used for the training of a transformer model, whose architecture and hyper-parameters are automatically configured via an evolutionary framework. The experimental results show that the proposed method can achieve accurate predictions compared to its rivals, and its best configuration can be automatically configured without hand-crafted efforts.