Lithium-ion batteries (LIBs) 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 turn hidden aging signals into actionable health information, potentially revolutionizing battery safety and longevity.
The review, authored by researchers from Zhejiang University, links degradation mechanisms to measurable physical signatures and evaluates four families of sensing strategies: surface-attached, implantable, in situ integrated, and noncontact. Surface sensors like thermocouples and fiber Bragg gratings are low-cost but suffer from signal delays. Implantable sensors, such as MEMS devices and optical fibers, offer better fidelity but face stability issues. In situ integrated designs embed sensing into current collectors or separators, while noncontact methods like magnetic-field imaging and acoustic probing provide system-level insight without contact.
The importance of this work lies in its comprehensive framework that connects degradation mechanisms, internal physical signals, and state estimation. By fusing heterogeneous signals with physics-informed and data-driven algorithms—including physics-informed neural networks, transformers, and CNN-LSTM architectures—the approach aims to distinguish normal battery breathing from dangerous lithium plating, gas generation, and microcracking. This could enable earlier thermal-runaway warnings, more accurate state-of-health and remaining-useful-life estimates, and smarter fast-charging.
For electric vehicles, the implications are significant: predictive maintenance and cell-to-pack safety monitoring could become standard, reducing the risk of fires and extending battery life. In grid storage, the framework could improve fleet-level reliability, second-life assessment, and fire prevention. The review stresses that no single sensing modality can capture the complex coupled processes underlying battery degradation; the real advance comes from sensing–algorithm co-design, where sensing capabilities, signal processing, and diagnostic models are developed together.
However, barriers remain. Sensor stability, manufacturing compatibility, data standardization, bandwidth, and cost must be addressed. The authors call for modular, standardized, minimally intrusive sensing and algorithm co-design to move laboratory advances into scalable battery systems. Low-cost strategies using existing voltage, current, and temperature signals combined with cloud-edge computing could ease deployment.
The full study, with DOI 10.1631/jzus.A2600100, provides a roadmap for next-generation smart battery management systems. As the field shifts from external, indirect observation toward direct internal perception, this research could make batteries more predictable, reliable, and durable across electric vehicles and stationary storage, ultimately accelerating the transition to cleaner energy.
