  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.
