Opportunities and challenges in transformer neural networks for battery state estimation: Charge, health, lifetime, and safety

Abstract

Battery technology plays a crucial role across various sectors, powering devices from smartphones to electric vehicles and supporting grid-scale energy storage. To ensure their safety and efficiency, batteries must be evaluated under diverse operating conditions. Traditional modeling techniques, which often rely on first principles and atomic-level calculations, struggle with practical applications due to incomplete or noisy data. Furthermore, the complexity of battery dynamics, shaped by physical, chemical, and electrochemical interactions, presents substantial challenges for precise and efficient modeling. The Transformer model, originally designed for natural language processing, has proven effective in time-series analysis and forecasting. It adeptly handles the extensive, complex datasets produced during battery cycles, efficiently filtering out noise and identifying critical features without extensive preprocessing. This capability positions Transformers as potent tools for tackling the intricacies of battery data. This review explores the application of customized Transformers in battery state estimation, emphasizing crucial aspects such as charging, health assessment, lifetime prediction, and safety monitoring. It highlights the distinct advantages of Transformer-based models and addresses ongoing challenges and future opportunities in the field. By combining data-driven AI techniques with empirical insights from battery analysis, these pre-trained models can deliver precise diagnostics and comprehensive monitoring, enhancing performance metrics like health monitoring, anomaly detection, and early-warning systems. This integrated approach promises significant improvements in battery technology management and application.