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New Review: Nondestructive Sensing Could Predict Battery Aging and Prevent Failures

New review in Journal of Zhejiang University–SCIENCE A explores nondestructive sensing and AI for lithium-ion battery aging, safety, and predictive health management.

This research signals a paradigm shift in battery management: instead of reacting to failures, future systems could predict and prevent them. By fusing diverse sensing modalities with physics-informed AI, the framework promises earlier thermal-runaway warnings, more accurate state-of-health and remaining-useful-life estimates, and smarter fast-charging. For electric vehicle owners, this means longer-lasting batteries and reduced risk of catastrophic fires. For grid operators, it translates to more reliable energy storage, better second-life assessments, and enhanced fire prevention. As the world accelerates toward electrification, such mechanism-informed care is essential for building trust in battery technology and ensuring safety at scale. The call for standardized, low-cost sensing and cloud-edge collaboration highlights a practical path to deployment, making this a pivotal step toward truly smart batteries.