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

Advanced lithium-ion batteries (LIBs) are critical to electric vehicles and grid storage, yet their internal electrochemical, thermal, and mechanical states remain difficult to observe. A new review from researchers at Zhejiang University, published in the Journal of Zhejiang University–SCIENCE A, addresses this challenge by examining how nondestructive sensing and failure diagnosis can transform hidden aging signals into actionable health information. The paper, with DOI 10.1631/jzus.A2600100, links degradation mechanisms to measurable physical signatures and evaluates four families of diagnostic technologies: surface-attached, implantable, in situ integrated, and noncontact sensing. It also assesses multisource feature fusion, physics-informed and data-driven algorithms, and smart battery management concepts. The authors argue that moving from reactive protection to predictive, mechanism-informed care could improve safety, reliability, and lifetime across electric vehicles and stationary storage.

Lithium-ion battery aging is driven by tightly coupled chemical, mechanical, and thermal processes, including solid electrolyte interphase (SEI) growth, cathode electrolyte interphase (CEI) formation, electrolyte decomposition, particle cracking, and lithium plating. Conventional battery management systems (BMSs) mainly track voltage, current, and surface temperature, but these external signals are distorted by polarization, side reactions, spatial averaging, and delays. Synchrotron and magnetic resonance imaging offer mechanistic insight, yet their scale, cost, and speed limit real-time use. The review systematically compares surface-attached sensors (thermocouples, thermistors, RTDs, fiber Bragg gratings), implantable sensors (MEMS, thin-film strain gauges, optical fibers), in situ integrated designs, and noncontact methods (magnetic-field imaging, acoustic and ultrasonic probing, gas analysis, electrochemical impedance spectroscopy). It then examines algorithms for state-of-health (SOH) estimation, remaining-useful-life (RUL) prediction, and early failure warning, proposing a unified framework that connects degradation mechanisms, internal physical signals, and state estimation to guide next-generation smart BMS design.

The authors emphasize that no single sensing modality can fully capture the complex, coupled processes underlying battery degradation and safety failure; the real advance comes when surface, implanted, in situ, and noncontact signals are fused with physics-informed algorithms. They stress sensing–algorithm co-design, in which sensing capabilities, signal processing, and diagnostic models are considered together rather than treated as separate stages. Practically, the framework could support earlier thermal-runaway warnings, more accurate SOH and RUL estimates, and smarter fast-charging. In electric vehicles, it may enable predictive maintenance and cell-to-pack safety monitoring; in grid storage, it could improve fleet-level reliability, second-life assessment, and fire prevention. Low-cost strategies using existing voltage, current, and temperature signals, combined with cloud-edge computing, could ease deployment. However, sensor stability, manufacturing compatibility, data standardization, bandwidth, and cost remain key barriers. The review calls for modular, standardized, minimally intrusive sensing and algorithm co-design to move laboratory advances into scalable battery systems.