The structural integrity and internal consistency of lithium-ion batteries are pivotal for their durability and safety. Conventional detection and evaluation methods often rely on expensive instrumentation and manual expertise, which hinder scalability and efficiency. This study presents an intelligent ultrasound-based approach for the automated identification of battery regions and defects, facilitating precise assessment of internal consistency and common defect types. Initially, sample cells-comprising normal cells and those with three typical defects-are prepared, and their ultrasound signals are analyzed. Multidimensional ultrasound features are then extracted, and a random forest-based model is developed for the automated classification of battery regions and defects. Experimental validation demonstrates that: (1) the proposed method can automatically classify eight battery regions, such as the infiltration area, bubble area, and tape area, with an overall accuracy exceeding 97.2 %; (2) the method accurately identifies three typical defects-aluminum foil insertion, electrode folding, and negative electrode scraping-with recognition rates of 90.7 %, 94.7 %, and 96 %, respectively, while providing three-dimensional defect localization. This study introduces a novel monitoring approach for battery production and application, thereby enhancing battery safety.

Nondestructive testing and quantitative estimation of the electrolyte content in seal-packed lithium-ion batteries (LIBs) have remained a major technological challenge. This study developed a non-contact, multi-channel ultrasonic testing solution for nondestructive, in situ testing and quantitative estimation of the electrolyte content in LIBs. First, sample batteries with different electrolyte contents were prepared. Then, the electrolyte contents in different local areas of the sample batteries were calibrated based on the corresponding mass ratios of characteristic elements obtained using energy-dispersive spectroscopy and scanning electron microscopy; thereby, the mapping relationship between the ultrasonic propagation properties and the electrolyte content of the batteries was established. Finally, a method for segmental quantitative electrolyte content estimation with thickness-based corrections was proposed and verified experimentally. The experimental results showed that the magnitude of through-transmission ultrasonic signals and the electrolyte content of the cells were strongly correlated. The error of the electrolyte content estimation method was within 3% for cells with adequate electrolyte content, and the error of electrolyte content was smaller than 6% even for batteries with a low electrolyte content.

Lithium (Li)-ion battery is an important energy storage for electronic production and electric vehicles. Battery aging is accompanied by a state change in the active material. The method of active material status evaluation in a nondestructive way has become a major topic in battery research. In this study, a battery in situ testing with multiple noncontact ultrasonic excitation signal methodology is proposed, and for the first time to use acoustic energy to analysis signal transmittance and reflectance. Based on a 1/20C charging and discharging experiment of commercial NCM111 pouch battery, the deformation, density, wave speed, acoustic impedance, and other parameters of NCM111/graphite material under different Li stoichiometry are estimated. Acoustic property of active material has been used as a medium to explain the mechanism of ultrasonic signal changes. The experiment result shows that acoustic energy is highly correlated with the calculated acoustic impedance of the active material, and there is no accurate correspondence with battery voltage and capacity. Ultrasonic is an effective method to study the status of Li battery.

The wetting process plays an important role in battery production efficiency and battery quality, including available energy density, cycling life, power, and battery consistency. A convenient and efficient method for characterizing electrolyte filling, which becomes more crucial for lithium-ion batteries (LIBs) with a large format or super energy density, is desperately needed in the battery industry. Herein, we propose the operando monitoring of the open circuit voltage (OCV) during the electrolyte filling process. It is found that battery OCV drops dramatically to -0.80 V at the beginning of filling (within 300 ms), and then characteristically recovers to 0.10 V with the wetting process, involving valuable information about the electrolyte filling process. Insights of the correlation between electrolyte wetting process and battery OCV are further stimulated using an equivalent circuit model. The recovery rate of OCV can be a critical indicator to quantify the electrolyte wetting process, verifying by batteries used separators with different wettability. This study provides a practical and effective tool to ensure the high-quality electrolyte infiltration process of LIBs.

With the demand for high-energy-density power sources for electric vehicles, large-format lithium-ion batteries are widely applied, considering their advantages in reducing the weight of inactive materials. However, large-format cells suffer from internal inhomogeneities, which become the bottleneck limiting their performance. Here, the inhomogeneous degradation in a large-format pouch cell is comprehensively investigated using a series of (non-)destructive techniques. Spatial-resolved deformation detection and ultrasonic diagnostics are utilized to study the evolution of inhomogeneity inside the large-format battery. Localized deformation and deposits are found to firstly appear at the tab-near regions and then propagate into central regions, which is identified by characterization tests to be induced by lithium plating. The inhomogeneous degradation mechanism inside the battery is summarized as an initiation-propagation process. During the process, lithium deposit is initiated by a local high current density, resulting in the local separator pore closure. Pore closure in the separator in return creates a high current density and overpotential in the adjacent area, leading to a continuous propagation of the lithium deposition area. Finally, an effective indicator—peak height in the differential voltage curve is proposed to detect the inhomogeneity in the battery, and thus offers guidance to the design and management of large-format cells.

Lithium-ion batteries are widely used in electric vehicles and energy storage systems. Sudden fire accident is one of the most serious issue, which is mainly caused by unpredicted internal short circuit. Metal particle defect is a key factor in internal short circuit it will not show an obvious abnormal change in battery external characteristic just like mechanical and thermal abuse. So, a non-destructive testing of battery internal metal defect is very necessary. This study is first time to scan and analyze different types of defects inside a battery by using ultrasonic technology, and it shows the detection capability boundary of this methodology. A non-contact ultrasonic scanning system with multi-channel was built to scan the battery sample with aluminum foil, copper foil and copper powder defects. The position and shape of those defects were clearly shown by using tomography methodology. It was found that the acoustic properties difference between metal defects and battery active materials has a strong influence on detection sensitivity. Compared with aluminum foil, copper foil and copper powder are easier to be detected and change the ultrasonic signal greatly, they will produce an obvious shadowing artifacts and speed displacement phenomena in tomography images. Ultrasonic tomography technology is an effective method for non-destructive testing of lithium-ion batteries.

E-mobility, especially electric cars, has been scaling up rapidly because of technological advances in lithium-ion batteries (LIBs). However, LIBs degrade significantly with service life cycles. With the current increase in the adoption of electric vehicles (EVs), a large volume of retired LIB packs, which can no longer provide satisfactory performance to power an EV, will soon appear. Various end-of-life (EOL) options are under development, such as recycling and recovery. Recently, stakeholders have become more confident that giving the retired batteries a second life by reusing them in less-demanding applications, such as stationary energy storage, may create new value pools in the energy and transportation sectors. In this perspective, we evaluate the feasibility of second-life battery applications, from economic and technological perspectives, based on the latest industrial reports and technical publications.

Lithium plating leads to severe capacity fading and possible safety problems in lithium-ion batteries (LiB). Therefore, it is necessary to provide an in-situ detection methodology of lithium plating evolution during battery cycling. Qualitative analysis of the Distribution of Relaxation Times (DRT), based on Electrochemical Impedance Spectroscopy (EIS) deconvolution, shows a different degradation mechanism between lithium plating and normal aging batteries. A Support Vector Machine (SVM) detection algorithm based on the DRT is proposed. It only relies on the measured capacity and EIS data during battery aging, which is also easy to process and non-destructive to battery sample. The method has a significant advantage in battery safety management and echelon selection of retired batteries.

Layered Ni-rich LiNixMnyCo1-x-yO2 (NMC) materials are the most promising cathode materials for Li-ion batteries due to their favorable energy densities. However, the low thermal stability typically caused by detrimental oxygen release leads to significant safety concerns. Determining the pathways of oxygen evolution reaction is essential, as the ideal safety countermeasure is to break the reaction chain of thermal runaway. In this study, we demonstrate that two endogenous pathways of oxygen involved in strong exothermic reactions lead the NMC811|graphite pouch cell to an uncontrollable state, and we quantify the individual contribution of the pathways to thermal runaway. Approximately 41% of thermal-induced oxygen reacts aggressively with ethylene carbonate (EC) at the cathode/electrolyte interface with 16% heat generation, accelerating the self-heating rate and thereby further triggering thermal runaway. The residual oxygen that survives the reaction with carbonate spreads to the lithiated anode with major heat generation (65%), bringing the battery to the maximum destructive temperature during thermal runaway. By confirming the significant roles of EC and anode, a deeper understanding on battery fire was achieved. The revealed mechanism can help guide studies on stopping the two reaction pathways, allowing for the safer use of high-energy lithium-ion batteries in the future.

Electrochemical impedance spectroscopy (EIS) allows detailed investigations of polarization processes and is widely used to study the kinetics of electrode reaction in lithium-ion batteries.The distribution of relaxation times (DRT) calculated from the EIS offers a model-free approach for a deeper understanding of various electrochemical processes.A joint estimation method is proposed to identify the differential capacity caused by diffusion processes and the DRT for all polarization processes simultaneously.The differential capacity from EIS and the incremental capacity from incremental capacity analysis (ICA) have an equivalence verified by mathematical derivation.Different types of lithium-ion batteries are tested by the EIS and the ICA methods to verify the equivalence.The differential capacity extends the application of the ICA method.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.

Over the past decade, major progress in diagnosis of battery degradation has had a substantial effect on the development of electric vehicles. However, despite recent advances, most studies suffer from fatal flaws in how the data are processed caused by discrete sampling levels and associated noise, requiring smoothing algorithms that are not reliable or reproducible. We report the realization of an accurate and reproducible approach, as “Level Evaluation ANalysis” or LEAN method, to diagnose the battery degradation based on counting the number of points at each sampling level, of which the accuracy and reproducibility is proven by mathematical arguments. Its reliability is verified to be consistent with previously published data from four laboratories around the world. The simple code, exact fitting, consistent outcome, computational availability and reliability make the LEAN method promising for vehicular application in both the big data analysis on the cloud and the online battery monitoring, supporting the intelligent management of power sources for autonomous vehicles.