Lithium-ion batteries (LIBs) power electric transportation and grid storage, but their internal electrochemical, thermal, and mechanical states remain largely invisible. A comprehensive review published in the Journal of Zhejiang University–SCIENCE A examines how nondestructive sensing and failure diagnosis can convert hidden aging signals into actionable health information. The work, detailed with DOI 10.1631/jzus.A2600100, proposes a unified framework that links degradation mechanisms to measurable physical signatures, potentially shifting battery management from reactive protection to predictive, mechanism-informed care.
LIB aging involves tightly coupled chemical, mechanical, and thermal processes. Growth of the solid electrolyte interphase and cathode electrolyte interphase, electrolyte decomposition, particle cracking, and lithium plating progressively consume active lithium, increase impedance, and degrade structural integrity. Severe degradation can lead to internal short circuits and thermal runaway. Conventional battery management systems track only voltage, current, and surface temperature, but these external signals are distorted by polarization, side reactions, spatial averaging, and delays. Advanced imaging techniques like synchrotron and magnetic resonance offer mechanistic insight but are limited by scale, cost, and speed for real-time use.
The review organizes nondestructive diagnostics into four families. Surface-attached sensors, such as thermocouples, thermistors, resistance temperature detectors, and fiber Bragg gratings, are low-cost and easy to deploy but suffer from significant delays and measurement discrepancies under high-rate operation due to signal transmission limitations. Implantable sensors, including microelectromechanical systems, thin-film strain gauges, and optical fibers, improve signal fidelity by measuring internal temperature, pressure, strain, and electrolyte chemistry, though long-term stability and vibration fatigue remain barriers. In situ integrated designs embed sensing functions into current collectors, separators, or packaging, offering minimal intrusion and compatibility. Noncontact methods—magnetic-field imaging, acoustic and ultrasonic probing, gas analysis, and electrochemical impedance spectroscopy—provide system-level insight without direct contact.
To make full use of these heterogeneous signals, the review discusses feature extraction, multisource information fusion, and model-based interpretation. Electrochemical and thermal features can be extracted using incremental capacity analysis, differential voltage analysis, and differential thermal voltammetry, then interpreted with physics-based models like pseudo-two-dimensional and single-particle models, as well as data-driven approaches such as physics-informed neural networks, transformers, and convolutional neural network–long short-term memory architectures. The authors stress feature selection, dimensionality reduction, cloud-edge collaboration, and standardized interfaces. Together, these tools aim to distinguish normal breathing from lithium plating, gas generation, and microcracking, translating weak multiphysics signatures into mechanism-specific warnings.
The implications are significant. The authors argue the field is shifting from external, indirect observation toward direct internal perception. 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 emphasize sensing–algorithm co-design, where sensing capabilities, signal processing, and diagnostic models are considered together rather than treated as separate stages. This shift could make batteries more predictable, reliable, and durable across electric vehicles and grid storage. Practically, the framework could support earlier thermal-runaway warnings, more accurate state-of-health and remaining-useful-life 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 in future battery systems.
