Revolutionizing Battery Management: Harnessing 3D Acoustic Landscapes and AI for Sustainable Battery Lifetime Prediction

Batteries are vital for the green transition, enabling electrification of transportation and integration of renewable energy. Despite falling costs, uncertainties about battery lifetime challenge sustainability. Accurate lifetime prediction is key for effective management, performance, and preventing failures such as fires. Current prediction methods rely on extensive lab testing and post-mortem analyses, which are complex and energy-intensive. To address these issues, a cross-disciplinary collaboration is dedicated, bringing together experts in battery technology, statistical signal processing, and machine learning. The goal is to apply advanced data science techniques for online, non-invasive battery lifetime prediction through acoustic emission detection. This approach models the battery as a 3D acoustic landscape, tracking its changes over time. The expected outcomes are groundbreaking for battery management and will advance explainable, data-driven prediction for time series data.