In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

通过引入End of Life (EOL)和Equivalent Cycle Life (ECL)作为生成AI模型的条件，该研究开发了一种经过改进的条件变分自编码器（RCVAE），通过将数据预处理成准视频格式，实现了电化学数据的综合合成，可以生成特定的电化学数据用于EOL和ECL的超定监督条件。此方法为用户提供了综合的电化学数据集，开创了锂电池数据人工合成的新研究领域，并可以基于详细的合成数据计算各种电池状态指标，为锂电池性能预测提供了新的视角和可能性。

利用生成式人工智能生成全面的锂电池充电数据