SOURCE-LINKED INTELLIGENCE

Rejuvenation and Anti-Aging Mechanisms for Vitalizing Next-Generation Electric Vehicle Batteries

CORDIS · observation · Publication date unknown

advanced in-situ diagnostics tools to closely differentiate different aging modes and predict their progression with minimized aging footprint during characterization, (2) harnessing physics-informed artificial intelligence based on reinforcement learning in discovering the most effective non-invasive rejuvenation and treatment strategies and protocols based on external stimuli, and (3) pioneering advanced anti-aging and self-repair battery charging protocols that reduce degradation while optimizing charging time. This approach integrates expertise across battery electronics, physics, control theory, Multiphysics modeling, and AI, presenting a transformative solution for EV sustainability and laying the groundwork for a new research frontier in regenerative battery science with broader implications for energy storage systems. Batteries; System Identification; Multi-physics modeling; Artificial Intelligence

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recordType

award

status

SIGNED

region

EU

value

1498201

unit

EUR