Lithium-ion batteries (LIBs) power electric vehicles and grid storage, yet their internal states remain largely invisible. A new review published in the Journal of Zhejiang University–SCIENCE A (DOI: 10.1631/jzus.A2600100) argues that nondestructive sensing and intelligent diagnosis can turn hidden aging signals into actionable health information, shifting battery management from reactive protection to predictive, mechanism-informed care.
The stakes are high. LIB aging involves coupled chemical, mechanical, and thermal processes—growth of the solid electrolyte interphase, electrolyte decomposition, particle cracking, and lithium plating—that consume active lithium, increase impedance, and 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, and delays. While synchrotron and magnetic resonance imaging offer mechanistic insight, their scale, cost, and speed limit real-time use.
The review, from researchers at Zhejiang University, systematically compares four families of nondestructive diagnostics. Surface-attached sensors like thermocouples and fiber Bragg gratings are low-cost but suffer from signal delays under high-rate operation. Implantable sensors—MEMS devices, thin-film strain gauges, and optical fibers—improve fidelity by measuring internal temperature, pressure, strain, and electrolyte chemistry, though long-term stability remains a barrier. In situ integrated designs embed sensing into current collectors, separators, or packaging, offering minimal intrusion. Noncontact methods such as magnetic-field imaging, acoustic 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 and multisource fusion. Techniques like incremental capacity analysis, differential voltage analysis, and differential thermal voltammetry extract electrochemical and thermal features, which are then interpreted using physics-based models (e.g., pseudo-two-dimensional and single-particle models) and data-driven approaches such as physics-informed neural networks, transformers, and CNN-LSTM architectures. The goal is to distinguish normal breathing from lithium plating, gas generation, and microcracking, and to translate weak multiphysics signatures into mechanism-specific warnings.
The authors emphasize sensing–algorithm co-design, stating that no single modality can capture the complex degradation processes. By fusing surface, implanted, in situ, and noncontact signals with physics-informed algorithms, batteries could become more predictable, reliable, and durable. Practically, this 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 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, the review cautions that sensor stability, manufacturing compatibility, data standardization, bandwidth, and cost remain key barriers. It 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 practical deployment of advanced diagnostics in future battery systems.
