Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells
Sign Up to like & getrecommendations! Published in 2022 at "Advanced Science"
DOI: 10.1002/advs.202200630
Abstract: Precise lifetime predictions for lithium‐ion cells are crucial for efficient battery development and thus enable profitable electric vehicles and a sustainable transformation towards zero‐emission mobility. However, limitations remain due to the complex degradation of lithium‐ion… read more here.
Keywords: ion cells; machine learning; lithium ion; lifetime prediction ... See more keywords
Lifetime prediction and kinetic parameters of thermal decomposition of cashew gum by thermal analysis
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Thermal Analysis and Calorimetry"
DOI: 10.1007/s10973-017-6844-9
Abstract: The thermal decomposition kinetics of cashew gum, an exudate polysaccharide from A. occidentale L., has been investigated by thermogravimetric analysis in nitrogen and air atmospheres and non-isothermal conditions. The heating rates were of 5, 10,… read more here.
Keywords: cashew gum; analysis; thermal analysis; lifetime prediction ... See more keywords
High temperature corrosion evaluation and lifetime prediction of porous Fe22Cr stainless steel in air in temperature range 700–900 °C
Sign Up to like & getrecommendations! Published in 2021 at "Corrosion Science"
DOI: 10.1016/j.corsci.2021.109589
Abstract: Abstract This work describes a high temperature corrosion kinetics study of ∼30 % porous Fe22Cr alloys. The surface area of the alloy (∼0.02 m2 g−1) has been determined by tomographic microscopy. The weight gain of… read more here.
Keywords: temperature corrosion; lifetime prediction; 700 900; porous fe22cr ... See more keywords
In-phase thermomechanical fatigue lifetime prediction of nickel-based single crystal superalloys from smooth specimens to notched specimens based on coupling damage on critical plane
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Fatigue"
DOI: 10.1016/j.ijfatigue.2019.05.016
Abstract: Abstract A lifetime prediction method is proposed to predict the in-phase thermomechanical fatigue (IP TMF) lifetime of notched nickel-based single crystal superalloy by combining continuum damage theory and the critical distance theory in the present… read more here.
Keywords: nickel based; damage; lifetime prediction; based single ... See more keywords
Lifetime prediction for proton exchange membrane fuel cell under real driving cycles based on platinum particle dissolve model
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Hydrogen Energy"
DOI: 10.1016/j.ijhydene.2020.08.188
Abstract: Abstract Despite the great progress of proton exchange membrane fuel cell (PEMFC) vehicle, the durability and cost of PEMFC still remain challenges. In this paper, a lifetime prediction model of PEMFC is developed by considering… read more here.
Keywords: model; pemfc; driving cycles; lifetime prediction ... See more keywords
Lifetime prediction of aluminum solar mirrors by correlating accelerated aging and outdoor exposure experiments
Sign Up to like & getrecommendations! Published in 2018 at "Solar Energy"
DOI: 10.1016/j.solener.2018.09.006
Abstract: Abstract The reflectors used for Concentrated Solar Thermal (CST) technologies are expected to withstand harsh environmental conditions during their lifetime of more than 20 years without a significant specular reflectance loss. Accelerated aging testing is currently… read more here.
Keywords: methodology; accelerated aging; outdoor exposure; lifetime prediction ... See more keywords
Lifetime prediction of InGaZnO thin film transistor for the application of display device and BEOL-transistors
Sign Up to like & getrecommendations! Published in 2018 at "Solid-state Electronics"
DOI: 10.1016/j.sse.2018.01.003
Abstract: Abstract In this work, the lifetime prediction models of amorphous InGaZnO thin film transistors (a-IGZO TFTs) were suggested for the application of display device and BEOL (Back End Of line) transistors with embedded a-IGZO TFTs.… read more here.
Keywords: ingazno thin; application display; lifetime prediction; application ... See more keywords
Lifetime prediction for optical fibers aged in sodium chlorine aquoses solutions: Directions for deployment in seashore regions
Sign Up to like & getrecommendations! Published in 2020 at "Optical Fiber Technology"
DOI: 10.1016/j.yofte.2020.102370
Abstract: Abstract Fiber to the home networks plays a substantial role in helping operators to satisfy the increasing bandwidth demands of their subscribers. The dissemination of these networks brings new challenges, mainly in terms of the… read more here.
Keywords: aged sodium; lifetime prediction; sodium; prediction optical ... See more keywords
Modeling Outdoor Service Lifetime Prediction of PV Modules: Effects of Combined Climatic Stressors on PV Module Power Degradation
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Journal of Photovoltaics"
DOI: 10.1109/jphotov.2019.2916197
Abstract: Photovoltaic modules are exposed to a variety of climatic loads during outdoor operation. Over time, these loads trigger a number of degradation modes within the modules leading to performance loss. This paper quantifies the impact… read more here.
Keywords: lifetime prediction; combined climatic; power; degradation ... See more keywords
ADLPT: Improving 3D NAND Flash Memory Reliability by Adaptive Lifetime Prediction Techniques
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2022.3214115
Abstract: NAND flash memory has become increasingly popular in various computing systems. Although NAND flash memory offers attractive performance, it suffers limited operable programming and erasing cycles. To improve the reliability of flash-based systems, previous works… read more here.
Keywords: nand flash; flash memory; reliability; lifetime ... See more keywords
Semi-Supervised Self-Learning-Based Lifetime Prediction for Batteries
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3206776
Abstract: Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This article proposes a novel semi-supervised self-learning method for battery lifetime prediction. First, three health… read more here.
Keywords: self learning; semi supervised; supervised self; prediction ... See more keywords