Lithium-ion batteries (LIBs) are indispensable for electric transportation and grid storage, yet their internal electrochemical, thermal, and mechanical states remain largely opaque. A new review published in the Journal of Zhejiang University–SCIENCE A (DOI: 10.1631/jzus.A2600100) argues that nondestructive sensing and failure diagnosis can convert hidden aging signals into actionable health information, enabling a shift from reactive protection to predictive, mechanism-informed battery care.
The importance of this work lies in its comprehensive roadmap for overcoming a critical bottleneck: 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. As a result, early signs of degradation—such as lithium plating, gas generation, or microcracking—often go undetected until safety risks escalate. The review systematically links degradation mechanisms to measurable physical signatures and evaluates four families of sensing strategies: surface-attached sensors, implantable sensors, in situ integrated designs, and noncontact methods.
Surface-attached sensors, including thermocouples, thermistors, resistance temperature detectors, and fiber Bragg gratings, are low-cost and easy to deploy, but they suffer from significant delays and measurement discrepancies under high-rate operation because signals must travel from the battery interior to the sensing surface. Implantable sensors, such as microelectromechanical systems devices, 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 using physics-based models such as pseudo-two-dimensional and single-particle models, as well as emerging data-driven approaches like physics-informed neural networks, transformers, and convolutional neural network–long short-term memory architectures. The authors stress that no single sensing modality can fully capture the complex, coupled processes underlying battery degradation. The real advance, they argue, comes from fusing surface, implanted, in situ, and noncontact signals with physics-informed algorithms—a concept they call sensing–algorithm co-design.
The implications for industry are substantial. The proposed 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, 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 the practical deployment of advanced diagnostic technologies. As the field shifts from external, indirect observation toward direct internal perception, this roadmap offers a timely guide for making batteries more predictable, reliable, and durable across electric vehicles and grid storage. For more information, visit http://chuanlink-innovations.com.
