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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.

| openaire: EC/H2020/963646/EU//HELIOS Lithium titanate oxide (LTO) batteries have been under intensive research due to their stability, safety, and rapid charging characteristics. Nevertheless, uncertainties as to LTO-batteries behavior when used as a raw material in battery recycling still exist. This study provides a grave-to-gate life cycle inventory for a hydrometallurgical battery recycling process in which Li-battery waste materials nickel manganese cobalt (NMC), LTO, and graphite were used as feed. The simulation showed that NMC cathode materials and lithium from both battery waste fractions could be recovered. In contrast, the titanium present within LTO cannot be recovered by the recycling process. Nevertheless, the life cycle assessment (LCA) of the process demonstrated clear benefits of recycling battery materials, highlighted by the decrease in global warming potential, acidification, eutrophication, and ozone depletion potential. Additionally, two routes for hazardous waste management were simulated to ascertain the environmental impacts of hazardous waste management within the recycling process. Peer reviewed

Energy and buildings 326, 114987 (2025). doi:10.1016/j.enbuild.2024.114987 Published by Elsevier Science, Amsterdam [u.a.]