Pathway decisions for reuse and recycling of retired lithium-ion batteries considering economic and environmental functions.

Reuse and recycling of retired electric vehicle (EV) batteries offer a sustainable waste management approach but face decision-making challenges. Based on the process-based life cycle assessment method, we present a strategy to optimize pathways of retired battery treatments economically and environmentally. The strategy is applied to various reuse scenarios with capacity configurations, including energy storage systems, communication base stations, and low-speed vehicles. Hydrometallurgical, pyrometallurgical, and direct recycling considering battery residual values are evaluated at the end-of-life stage. For the optimized pathway, lithium iron phosphate (LFP) batteries improve profits by 58% and reduce emissions by 18% compared to hydrometallurgical recycling without reuse. Lithium nickel manganese cobalt oxide (NMC) batteries boost profit by 19% and reduce emissions by 18%. Despite NMC batteries exhibiting higher immediate recycling returns, LFP batteries provide superior long-term benefits through reuse before recycling. Our strategy features an accessible evaluation framework for pinpointing optimal pathways of retired EV batteries.

Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning.

Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.

Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation.

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.

[Anaysis on acupoint selection rule of acupuncture for trigeminal neuralgia].

The characteristics and rules of acupoint selection of acupuncture for trigeminal neuralgia were analyzed. By searching CNKI, VIP, WF, literature regarding acupuncture for trigeminal neuralgia from 1980 to 2013 was collected to establish an acupuncture prescription database. The data mining technology was applied to analyze the characteristics and rules of the acupoint selection. As a result, a total of 180 papers were included, involving 148 acupoints. It was found that the acupoints that had high frequency of selection included Hegu (LI 4), Xiaguan (ST 7), Fengchi (GB 20) and trigger points. The acupoints selected were distributed in 14 meridians, in which yangming meridian of hand-foot had a frequency of 41. 58%. The special acupoints including crossing points, yuan-primary points and five-shu points were widely used, accounting for 65. 9%. As for the branch of trigeminal nerve, the top-3 selected acupoints were Yangbai (GB 14), Yuyao (EX-HN 4), Cuanzhu (BL 2) in the first branch, Sibai (ST 2), Quanlian (SI 18), Yingxiang (LI 20) in the second branch, Jiache (ST 6), Xiaguan (ST 7), Dicang (ST 4) in the third branch. In conclusion, it is believed that the clinical treatment of trigeminal neural gia focuses on local acupoints in combination with nerve distribution-based acupoints and distal acupoints, also the special acupoints are emphasized.