Lithium-ion batteries (LIBs) are indispensable for electric transportation and grid storage, but their internal electrochemical, thermal, and mechanical states are notoriously difficult to observe. A new review published in the Journal of Zhejiang University–SCIENCE A (2026) examines how nondestructive sensing and failure diagnosis can convert hidden aging signals into actionable health information, potentially shifting battery management from reactive protection to predictive, mechanism-informed care.
The review, authored by researchers from Zhejiang University’s State Key Laboratory of Chemical Engineering, highlights that LIB aging is driven by tightly coupled chemical, mechanical, and thermal processes. Growth of the solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI), electrolyte decomposition, particle cracking, and lithium plating progressively consume active lithium, increase impedance, and degrade structural integrity, raising the risk of internal short circuits and thermal runaway. Conventional battery management systems (BMSs) track only voltage, current, and surface temperature, but these external signals are distorted by polarization, side reactions, spatial averaging, and delays. While synchrotron and magnetic resonance imaging offer mechanistic insight, their scale, cost, and speed limit real-time use. The review, available via DOI 10.1631/jzus.A2600100, systematically compares four families of nondestructive diagnostics: surface-attached sensors (thermocouples, thermistors, RTDs, fiber Bragg gratings), implantable sensors (MEMS, thin-film strain gauges, optical fibers), in situ integrated designs (embedded in current collectors, separators, or packaging), and noncontact methods (magnetic-field imaging, acoustic and ultrasonic probing, gas analysis, and electrochemical impedance spectroscopy).
To make sense of these heterogeneous signals, the review discusses feature extraction techniques such as incremental capacity analysis (ICA), differential voltage analysis (DVA), and differential thermal voltammetry (DTV), interpreted through physics-based models like pseudo-two-dimensional (P2D) and single-particle models (SPM), as well as data-driven approaches including physics-informed neural networks (PINNs), transformers, and CNN-LSTM architectures. It stresses feature selection, dimensionality reduction, cloud-edge collaboration, and standardized interfaces. The authors argue that no single sensing modality can fully capture the complex, coupled processes underlying battery degradation; the real advance comes when surface, implanted, in situ, and noncontact signals are fused with physics-informed algorithms. They emphasize sensing–algorithm co-design, where sensing capabilities, signal processing, and diagnostic models are considered together rather than treated as separate stages.
Practically, the proposed framework could support earlier thermal-runaway warnings, more accurate state-of-health (SOH) and remaining-useful-life (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. For industry, integrating sensing and diagnostic capabilities into battery design could improve manufacturing compatibility and long-term reliability while facilitating the practical deployment of advanced diagnostic technologies. As the field shifts from external, indirect observation toward direct internal perception, this roadmap could make batteries more predictable, reliable, and durable across electric vehicles and grid storage.
