Results 221 to 230 of about 31,093 (304)

Optimizing charge discharge cycles using QPPONet-enabled hybrid learning framework for energy management and safety in electric vehicles. [PDF]

Sci Rep
Sujan Kumar MV, Khekare G.
europepmc   +1 more source

In Situ Evolution of Artificial Interphase Enabling Stable, High‐Performance Lithium Metal Batteries

Advanced Energy Materials, EarlyView.
An evolutionary artificial interphase is presented for stable lithium metal batteries. A Mg3N2‐based layer spontaneously evolves into a hybrid interphase enriched with superionic Li3N and lithiophilic Mg‐Li alloy during cycling. This dynamic, self‐reinforcing mechanism effectively suppresses dendrite growth and enables exceptional long‐term cycling ...
Sangcheol Lee, Hyunsu Han, Dong Ki Kim, Michael J. Lee, Min‐Sik Park   +4 more
wiley   +1 more source

Stochastic optimization framework for microgrid energy management integrating electric vehicles, renewable sources, and storage. [PDF]

Sci Rep
Ali ZM, Mostafa MH.
europepmc   +1 more source

High‐Performance Elastomeric Lithium Metal Anodes Enabled by a Lithiophilic Monolayer‐Assembled Nano‐Crumpled Micro‐Concave Architecture

Advanced Energy Materials, EarlyView.
We report an elastomeric lithium metal anode integrating hierarchical nano–micro architectures with uniformly assembled lithiophilic amine functionalities. This design enables synergistic stress and interfacial regulation, effectively suppresses dendritic growth, and ensures stable cycling, highlighting elastomeric anodes as a promising platform for ...
Sungha Choi, Donghyeon Nam, Geon Choi, Seonho Lee, Seungeun Paik, Yongkwon Song, Seungyeop Choi, Jaejin Lim, Woojae Chang, Jeong Gon Son, Daegun Kim, Giwon Lee, Sungjun Park, Yong Min Lee, Seoin Back, Yongmin Ko, Jinhan Cho   +16 more
wiley   +1 more source

Uncertainty aware hybrid learning framework for fast and safe charging of lithium-ion batteries using multi-fidelity observers. [PDF]

Sci Rep
Parimala CHH, Varma PS, Kumar RS, Singh AR, Mohapatra B, Addis HK.   +5 more
europepmc   +1 more source

Donor–Acceptor Covalent Organic Framework Enables Ambipolar Charge Storage in Aluminum‐Ion Energy Storage

Advanced Energy Materials, EarlyView.
A donor–acceptor covalent organic framework is designed as an ambipolar cathode for aluminum‐ion energy storage. The crystalline, microporous architecture enables intrinsic charge transport without conductive additives. Multi‐electron redox activity at donor and acceptor sites supports high capacity, excellent stability, and efficient reversible ...
Cataldo Valentini, Verónica Montes‐García, Wojciech Kukułka, Saira Sarwar, Michał Bielejewski, Mateusz Wlazło, Rossella Greco, Viktorija Pankratova, Ignacio Pérez‐Juste, Dawid Pakulski, Paolo Samorì, Artur Ciesielski   +11 more
wiley   +1 more source

Zero-emissions vehicle adoption and satellite-measured NO₂ air pollution in California, USA, from 2019 to 2023: a longitudinal observational study. [PDF]

Lancet Planet Health
Eckel SP, Chen F, Silva SJ, Goldberg DL, Johnston J, Palinkas LA, Campos A, Franco W, Garcia E.   +8 more
europepmc   +1 more source

Solid–liquid equilibria in the LiOH–ethanol–water system: Solubility measurements and thermodynamic modeling

AIChE Journal, EarlyView.
Abstract The demand for LiOH is driven by the growth of the electric vehicle industry. Evaporative crystallization of LiOH·H2O is energy intensive, whereas ethanol‐based antisolvent crystallization has emerged as a more sustainable alternative. From a process design perspective, the crystallization yield depends on the ethanol dosage, and thermodynamic
Xiaoqi Xu, Chentao He, Janet A. W. Elliott, Liuyin Xia   +3 more
wiley   +1 more source

EV charging station selection and routing flask application with ACO and NSGA-II including photovoltaic energy constraints. [PDF]

Sci Rep
Belaid M, Beid SE, Habib S, Gulzar MM, Aldahmashi J, Emara A, Doubabi S.   +6 more
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source