Artificial intelligence-driven real-world battery diagnostics

Energy and AI, Journal Year: 2024, Volume and Issue: 18, P. 100419 - 100419

Published: Aug. 29, 2024

Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances data assimilation techniques, the overwhelming volume diversity of data, coupled lack universally accepted models, underscore limitations these approaches. Recently, deep learning emerged a highly effective tool overcoming persistent issues diagnostics by adeptly managing expansive design spaces discerning intricate, multidimensional correlations. This approach resolves previously deemed insurmountable, especially lost, irregular, noisy through specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements their practical applications, including explainability computational costs associated AI-driven solutions. Emerging technologies explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine mitigate catastrophic forgetting, cloud-based digital twins open new opportunities intelligent life-cycle assessment. In this perspective, we outline opportunities, emphasizing potential innovative transform demonstrated our recent practice progress made field. includes promising achievements both industry field demonstrations modeling forecasting dynamics multiphysics multiscale systems. These systems feature inhomogeneous cascades scales, informed physical, electrochemical, observational, empirical, and/or mathematical understanding system. Through efforts, meticulous craftsmanship, elaborate implementations—and considering wealth spatio-temporal heterogeneity available data—such AI-based philosophies have great achieve better accuracy, faster training, improved generalization.

Language: Английский

Thermal Safety of Lithium-Ion Batteries: Current Status and Future Trends

Batteries, Journal Year: 2025, Volume and Issue: 11(3), P. 112 - 112

Published: March 15, 2025

Research on the thermal safety of lithium-ion batteries (LIBs) is crucial for supporting their large-scale application [...]

Language: Английский

Risk-based environmental assessment of atmospheric impact in the hydrometallurgical recycling of LFP batteries: A comparative analysis of operational scenarios

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 145306 - 145306

Published: March 1, 2025

Language: Английский