One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity**-Reference-Cited by-同舟云学术

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One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity**

Published:2022-08-23 Issue:10 Volume:5 Page:
ISSN:2566-6223
Container-title:Batteries & Supercaps
language:en
Short-container-title:Batteries & Supercaps

Author:

Rahmanian Fuzhan¹²,Vogler Monika¹²,Wölke Christian³,Yan Peng³,Winter Martin³⁴³,Cekic‐Laskovic Isidora³,Stein Helge S.¹²^ORCID

Affiliation:

1. Helmholtz Institute Ulm Applied Electrochemistry Helmholtzstr. 11 89081 Ulm Germany

2. Karlsruhe Institute of Technology Institute of Physical Chemistry Fritz-Haber-Weg 2 76131 Karlsruhe Germany

3. Helmholtz-Institute Münster (IEK-12), Forschungszentrum Jülich GmbH Corrensstraße 46 48149 Münster Germany

4. MEET Battery Research Center University of Münster Corrensstraße 46 48149 Münster Germany

Publisher

Wiley

Subject

Electrochemistry,Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/batt.202200228

Reference46 articles.

1. Temperature effect and thermal impact in lithium-ion batteries: A review

2. Designing Advanced Lithium‐Based Batteries for Low‐Temperature Conditions

3. A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures

4. Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions

5. Liquid electrolyte development for low-temperature lithium-ion batteries

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1. Self-Driving Laboratories for Chemistry and Materials Science;Chemical Reviews;2024-08-13

2. CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations;Scientific Data;2024-07-10

3. Designing workflows for materials characterization;Applied Physics Reviews;2024-03-01

4. Nonlinear Potentiodynamic Battery Charging Protocols for Fun, Education, and Application;ACS Engineering Au;2024-02-12

5. Non-aqueous battery electrolytes: high-throughput experimentation and machine learning-aided optimization of ionic conductivity;Journal of Materials Chemistry A;2024

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