Due to the inability to directly measure the internal state of batteries, there are technical challenges in battery state estimation, defect detection, and fault diagnosis. Ultrasonic technology, as a non-invasive diagnostic method, has been widely applied in the inspection of lithium-ion batteries in recent years.
Ultrasonic detection offers several distinct advantages over the aforementioned characterization methods for detecting gas defects in LIBs. Firstly, ultrasonic detection can penetrate the aluminum plastic film of batteries, allowing it to monitor tiny bubbles and defects deep inside the battery in real-time.
Defect detection within LIBs requires advanced methodologies for three-dimensional defect localization, enabling the differentiation of electrodes, separators, and aluminum-plastic films within the battery layers.
A comprehensive overview and analysis of the technical approaches, challenges, and solutions for the application of ultrasonic technology in battery state estimation is provided. The current state, main technical approaches, and challenges of ultrasonic technology in battery defect and fault diagnosis are summarized.
Ultrasonic technology, as a non-invasive detection method, shows great potential in lithium battery manufacturing and management. Fig. 17 summarizes the application scenarios of ultrasonics in LIBs. Firstly, ultrasonic technology has a broad application prospect in the state estimation and fault diagnosis of LIBs.
It would be unwise to assume ‘conventional’ lithium-ion batteries are approaching the end of their era and so we discuss current strategies to improve the current and next generation systems, where a holistic approach will be needed to unlock higher energy density while also maintaining lifetime and safety.
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DOI: 10.1002/er.6920 Corpus ID: 236272388; A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention @article{Huang2021ARO, title={A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention}, author={Lili Huang and Lishuo Liu and …
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