Universal Battery Charging Protocol Generation via Generative Machine Learning Models

Battery charging protocols play a pivotal role in the efficiency, safety, and longevity of battery systems. Existing protocol generation methods face three key limitations, including limited protocol diversity, inflexible across application scenarios, and lack of a compact unified representation that enables systematic analysis. To address these challenges, this article proposes a variational autoencoder (VAE)-based universal battery charging protocol (UBCP) generation framework that learns a unified, low-dimensional, latent representation capable of expressing charging protocols with comprehensive patterns. A randomized dataset is used to train the encoder, ensuring exposure to diverse patterns necessary for learning such a universal representation. A merge-based data augmentation strategy is introduced for the decoder to fuse distinct base protocols, improving reconstruction accuracy while preserving the ability to generate novel protocol patterns. The framework is evaluated in protocol reconstruction, fast-charging, and power-constrained charging scenarios. Experimental results show that the model achieves high-fidelity reconstruction and efficiently customizes protocols to meet given constraints. Compared with optimization-based baselines, the UBCP framework attains comparable or superior performance while requiring only a simple search and constraint check, highlighting its flexibility and computational efficiency.