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With their high-energy density, high-power density, long life, and low self-discharge, lithium-ion capacitors are a novel form of electrochemical energy storage devices which are extensively utilized in electric vehicles, energy storage systems, and portable electronic gadgets. Li-ion capacitor aging mechanisms and life prediction techniques, however, continue to be active research areas. This paper examines the aging process for Li-ion batteries, covering the alterations in cell composition, the effect of the electrode charge state, temperature effects, and electrolyte deterioration. Additionally, this research offers approaches for predicting the lifespan of lithium-ion batteries, including those based on physical models, machine learning, and artificial intelligence. In this work, cycle life testing techniques are also discussed, including accelerated aging experiments for lithium-ion capacitors. The paper concludes by discussing future directions for the creation of aging mechanisms and lithium-ion capacitor life prediction techniques. [ABSTRACT FROM AUTHOR]

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting. [ABSTRACT FROM AUTHOR]

The Portevin–Le Chatelier effect of Cu–2.0Be alloy was investigated using hot isothermal compression at varying strain rates (0.01–10 s−1) and temperature (903–1063 K). An Arrhenius-type constitutive equation was developed, and the average activation was determined. Both strain-rate-sensitive and temperature-sensitive serrations were identified. The stress–strain curve exhibited three types of serrations: type A at high strain rates, type B (mixed A + B) at medium strain rates, and type C at low strain rates. The serration mechanism is mainly affected by the interaction between the velocity of solute atom diffusion and movable dislocations. As the strain rate increases, the dislocations outpace the diffusion speed of the solute atoms, limiting their ability to effectively pin the dislocations, resulting in lower dislocation density and serration amplitude. Moreover, the dynamic phase transformation triggers the formation of nanoscale dispersive β phases, which impede dislocation and cause a rapid increase in the effective stress required for unpinning, leading to the formation of mixed A + B serrations at 1 s−1. [ABSTRACT FROM AUTHOR]