Investigation into the impact of charging rates on the stress development within silicon composite electrodes.

Silicon, renowned for its remarkable energy density, has emerged as a focal point in the pursuit of high-energy storage solutions for the next generation. Nevertheless, silicon electrodes are known to undergo significant volume expansion during the insertion of lithium ions, leading to structural deformation and the development of internal stresses, and causing a rapid decline in battery capacity and overall lifespan. To gain deeper insights into the intricacies of charge rate effects, this study employs a combination of in situ measurements and computational modeling to elucidate the cyclic performance of composite silicon electrodes. The findings derived from the established model and curvature measurement system unveil the substantial alterations in stress and deformation as a consequence of varying charge rates. Notably, the active layer experiences compressive forces that diminish as the charge rate decreases. At a charge rate of 0.2, the active layer endures a maximum stress of 89.145 MPa, providing a comprehensive explanation for the observed deterioration in cycling performance at higher charge rates. This study not only establishes a fundamental basis for subsequent stress analyses of silicon electrodes but also lays a solid foundation for further exploration of the impact of charge rates on composite silicon electrodes.

Chemical-Free Recycling of Cathode Material and Aluminum Foil from Waste Lithium-Ion Batteries by Combining Plasma and Ultrasonic Technology.

With the rapid demand for lithium-ion batteries due to the widespread application of electric vehicles, a significant amount of battery electrode pieces requiring urgent treatment are generated during battery production and disposal. The strong bonding caused by the presence of binders makes it challenging to achieve thorough separation between the cathode active materials and Al foil, posing difficulties in efficient battery material recycling. To address this issue, a plasma-ultrasonically combined physical separation method is proposed in this study. This method utilizes plasma-generated excited-state radicals assisted by ultrasonic waves to separate active materials and current collectors. The results indicate that the binders are effectively decomposed under plasma treatment at 13.56 MHz, 100 W, and 10 min in an oxygen atmosphere, resulting in a separation efficiency of 96.8 wt % for the cathode materials. Characterization results demonstrate that the morphology, crystal structure, and chemical composition of the recycled cathode active materials remain unchanged, facilitating subsequent direct restoration and hydrometallurgical recycling. Simultaneously, the Al foil is also completely recycled for subsequent reuse. Compared with traditional methods of separating cathode active materials and aluminum foil, the method proposed in this study has significant economic and environmental potential. It can promote the recycling of battery materials and the development of sustainable transportation.

Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning.

The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highlights the opportunities to diagnose lifetime abnormalities via "big data" analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost-benefit, and improved environmental friendliness.

Predicting battery impedance spectra from 10-second pulse tests under 10 Hz sampling rate.

Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model-a highly nonlinear model with clear physical meanings-with a median-filtered neural network machine learning. Over 1000 load profiles collected under different state-of-charge and state-of-health are utilized for verification, and the root-mean-squared-error of our predictions could be bounded by 1.1 mΩ and 2.1 mΩ when using dynamic profiles last for 3 min and 10 s, respectively. Our method allows using size-varying input data sampled at a rate down to 10 Hz and unlocks opportunities to detect the battery's internal electrochemical characteristics onboard via low-cost embedded sensors.