A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images

Sustainability of new energy vehicles from a battery recycling perspective: A bibliometric analysis

In recent years, new energy vehicles (NEVs) have taken the world by storm. A large number of NEV batteries have been scrapped, and research on NEV battery recycling is important for promoting the sustainable development of NEVs. Battery recycling is an important aspect of the sustainable development of NEVs. In this study, we conducted an in-depth analysis of the current status of research on NEV battery recycling from a new perspective using bibliometric methods and visualization software. This study shows that research targeting the recycling of NEV batteries is growing rapidly, and collaborative networks exist among researchers from different countries, institutions, and fields. The focus of research has shifted from lead-acid batteries to lithium batteries, and the supply chain and circular economy related to NEV battery recycling is an emerging research hotspot. Based on our analysis, we propose that the government should establish policies to improve the recycling networks at the collection stage and provide subsidies to attract consumers. Enterprises should develop low-cobalt and cobalt-free technologies, utilize green solvents, and develop new battery swap modes. The establishment of an information platform is conducive to the further development of collaborative networks.

Applications and Development of X-ray Inspection Techniques in Battery Cell Production

Demand for lithium-ion battery cells (LIB) for electromobility has risen sharply in recent years. In order to continue to serve this growing market, large-scale production capacities require further expansion and the overall effectiveness of processes must be increased. Effectiveness can be significantly optimized through innovative manufacturing technology and by identifying scrap early in the production chain. To enable these two approaches, it is imperative to quantify safety- and function-critical product features in critical manufacturing steps through appropriate measurement techniques. The overview in this paper on quality control in LIB production illustrates the necessity for improved inspection techniques with X-rays to realize a fast, online measurement of inner features in large-scale cell assembly with short cycle times and to visualize inner product-process interactions for the optimization in electrolyte filling. Therefore, two new inspection techniques are presented that contribute to overcoming the aforementioned challenges through the targeted use of X-rays. First, based on the results of previous experiments in which the X-ray beam directions were deliberately varied, a online coordinate measurement of anode-cathode (AC) overhang was developed using a line detector. Second, a new concept and the results of a continuous 2D visualization of the electrolyte filling process are presented, which can be used in the future to optimize this time-critical process step. By using a X-ray-permeable and portable vacuum chamber it is possible to quantify the influence of process parameters on the distribution of the electrolyte in the LIB.

Investigation on the thermo-electric-electrochemical characteristics of retired LFP batteries for echelon applications

Electric vehicles (EVs) have been developed to alleviate environmental pollution and climate change, but they leave behind a large amount of retired lithium-ion batteries (LIBs). Since the replacement of LIBs from EVs will lead to considerable waste generation, improving the echelon utilization of retired LIBs is becoming increasingly critical. In this paper, we studied the thermo-electric-electrochemical performance of retired LiFePO₄ (LFP) batteries using traditional methods, and found that the remaining capacity of retired LFP batteries has a strong correlation with their internal resistance. This result helped us to propose a rapid and elementary classification method for the calibration of the remaining capacity, and to then formulate a test protocol seeking to balance the time spent and the test cost. Besides, the cut-off voltage and charge-discharge current density have a significant impact on the calibration of the remaining capacity, especially for retired LFP batteries with low residual capacity. In the cycle life test and temperature reliability evaluation process, the results demonstrate that the retired LFP batteries have a good service life when under a lower current of charge/discharge, and the capacity reductions were 2.3%, 11.2% and 4.8% for retired LFP batteries with 80% state of health (SOH), 70% SOH and 60% SOH, respectively, after 500 cycles. Finally, considering the temperature reliability, voltage consistency and large current cycling performance of retired LFP batteries, there are still many challenges in their future echelon utilization.

A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset

BACKGROUND AND PURPOSE

COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people's health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance.

MATERIALS AND METHODS

In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used.

RESULTS

Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status.

CONCLUSIONS

A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.