Lithium-ion batteries (LIBs) are essential for electric transportation and grid storage, but their internal electrochemical, thermal, and mechanical states are notoriously difficult to observe. A comprehensive review published in the Journal of Zhejiang University–SCIENCE A (DOI: 10.1631/jzus.A2600100) examines how nondestructive sensing and failure diagnosis can turn hidden aging signals into actionable health information, potentially revolutionizing battery management systems (BMSs).
Conventional BMSs rely on external voltage, current, and surface temperature measurements, which are distorted by polarization, side reactions, spatial averaging, and delays. As a result, they often miss early signs of degradation. The review, led by researchers at Zhejiang University, systematically compares four families of nondestructive diagnostic technologies: surface-attached sensors (e.g., thermocouples, fiber Bragg gratings), implantable sensors (e.g., MEMS devices, optical fibers), in situ integrated designs (embedded in current collectors or separators), and noncontact methods (magnetic-field imaging, acoustic probing, gas analysis, and electrochemical impedance spectroscopy). Each approach offers trade-offs in cost, fidelity, and intrusiveness, but together they provide a more complete picture of battery health.
The real advance, according to the authors, comes from fusing these heterogeneous signals with physics-informed and data-driven algorithms. The review discusses feature extraction techniques such as incremental capacity analysis and differential thermal voltammetry, and interpretation using physics-based models (e.g., pseudo-two-dimensional models) and emerging machine learning architectures like physics-informed neural networks and CNN-LSTM. This sensing–algorithm co-design aims to distinguish normal battery "breathing" from lithium plating, gas generation, and microcracking, translating weak multiphysics signatures into mechanism-specific warnings.
For industry, the implications are significant. The framework could enable earlier thermal-runaway warnings, more accurate state-of-health and remaining-useful-life estimates, and smarter fast-charging. In electric vehicles, it may support 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, barriers remain: sensor stability, manufacturing compatibility, data standardization, bandwidth, and cost. The authors call for modular, standardized, minimally intrusive sensing and algorithm co-design to move laboratory advances into scalable battery systems. As the field shifts from external, indirect observation toward direct internal perception, this roadmap could make batteries more predictable, reliable, and durable across applications.
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