Lithium-ion batteries power electric vehicles and grid storage, but their internal electrochemical, thermal and mechanical states remain largely invisible. A new review published in the Journal of Zhejiang University–SCIENCE A examines how nondestructive sensing and failure diagnosis can transform 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 the State Key Laboratory of Chemical Engineering at Zhejiang University, addresses a critical gap: conventional battery management systems track only voltage, current and surface temperature, but these external signals are distorted by polarization, side reactions and spatial averaging. Meanwhile, advanced imaging techniques like synchrotron and magnetic resonance offer mechanistic insight but are too slow and costly for real-time use. The paper’s DOI is 10.1631/jzus.A2600100.
The review organizes nondestructive diagnostics into four families. Surface-attached sensors, such as thermocouples and fiber Bragg gratings, are low-cost but suffer from signal delays under high-rate operation. Implantable sensors, including MEMS devices and thin-film strain gauges, improve fidelity by measuring internal temperature, pressure and electrolyte chemistry, though long-term stability remains a barrier. In situ integrated designs embed sensing into current collectors, separators or packaging, while noncontact methods like magnetic-field imaging, acoustic probing and electrochemical impedance spectroscopy provide system-level insight without direct contact.
To make sense of these heterogeneous signals, the review discusses feature extraction techniques such as incremental capacity analysis and differential voltage analysis, interpreted through physics-based models like pseudo-two-dimensional models and emerging data-driven approaches including physics-informed neural networks and CNN-LSTM architectures. The authors stress feature selection, dimensionality reduction and cloud-edge collaboration to distinguish normal battery ‘breathing’ from lithium plating, gas generation and microcracking.
The authors argue that no single sensing modality can fully capture the coupled processes underlying battery degradation. The real advance, they say, comes from sensing–algorithm co-design, where surface, implanted, in situ and noncontact signals are fused with physics-informed algorithms. This shift could yield earlier thermal-runaway warnings, more accurate state-of-health and remaining-useful-life estimates, and smarter fast-charging.
In electric vehicles, the framework 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 practical deployment of advanced diagnostics in future battery systems. The work was supported by the National Natural Science Foundation of China, the Zhejiang Provincial Natural Science Foundation and the Open Research Fund of Suzhou Laboratory. Additional information is available at http://chuanlink-innovations.com.
