A LION-optimized GAN for multi-target battery state prediction in battery management system

Area of Science:

• Battery Technology
• Machine Learning
• Data Science

Background:

• Accurate battery management relies on precise prediction of target variables.
• The inherent nonlinear and time-varying characteristics of batteries pose significant challenges to prediction accuracy.
• Existing methods struggle with the complexity of multi-target battery prediction from historical data.

Purpose of the Study:

• To introduce GAN-LION, a conditional adversarial regression framework for multi-target battery prediction.
• To evaluate the impact of the Lion optimizer against Adam and baseline models (CNN-LSTM-GRU, Autoencoder-LSTM).
• To establish an end-to-end pipeline ensuring data integrity and preventing information leakage.

Main Methods:

• Developed GAN-LION, a deterministic sequence-to-one regression framework using a generator and discriminator.
• Trained the adversarial architecture with Lion and Adam optimizers for comparative analysis.
• Implemented a unified pipeline for data preprocessing, normalization, sequence construction, and chronological splitting.

Main Results:

• GAN-LION demonstrated superior overall performance across two benchmark battery datasets.
• The model exhibited lower prediction errors, smoother convergence, and more stable gradients on a challenging dataset.
• Performance advantages were less pronounced but still competitive and stable on a simpler dataset.

Conclusions:

• Conditional adversarial regression, particularly with Lion-based optimization, offers an effective approach for battery target prediction.
• GAN-LION provides a robust and technically sound solution for complex battery management scenarios.
• The framework's stability and performance highlight its potential for advancing battery state monitoring and control.