Simulation-assisted electrode processing: discrete and machine learning computational approaches for the simulation of the battery manufacturing process - Université de Picardie Jules Verne Accéder directement au contenu
Chapitre D'ouvrage Année : 2023

Simulation-assisted electrode processing: discrete and machine learning computational approaches for the simulation of the battery manufacturing process

Résumé

The development of clean energy technology is one most important requirements to decrease energy-related carbon dioxide emissions. This challenge triggers the development of renewable energies to scale up in the short and medium terms because of the growth of fuel prices and fossil source depletion, extreme heatwaves, and zero-emission vehicle sales by 2035. Efforts such as electrification of transportation and electricity storage are rising steadily over the past ten years by the number of patent filings in batteries and energy accumulation. The preferred battery technology is Li-ion batteries (LIBs) due to their large capacity, high power, and cyclability. Continued innovation in LIBs is needed to satisfy the electric vehicle demands, including the development of Gigafactories to decrease production costs. To address these large-scale production efforts, it is necessary to optimize further the manufacturing parameters to fabricate LIBs. Optimization efforts across the battery production requests for improved materials, cell designs, manufacturing processes, carbon dioxide emission fingerprints, and recycling. The manufacturing optimization is developed at the prototype level rather than in Gigafactories, aiming at the final product design and quality tests by a systematic trial-and-error process. This process is time and cost-consuming because the battery manufacturing process is multistep, which includes three major steps: electrode preparation, cell assembly, and formation and testing, affected by many experimental parameters.

Domaines

Matériaux
Fichier non déposé

Dates et versions

hal-04513721 , version 1 (20-03-2024)

Identifiants

Citer

Franco Zanotto, Mohammed Alabdali, Diana Zapata Dominguez, Alejandro Franco. Simulation-assisted electrode processing: discrete and machine learning computational approaches for the simulation of the battery manufacturing process. Processing and Manufacturing of Electrodes for Lithium-Ion Batteries, Institution of Engineering and Technology, pp.299-315, 2023, 9781839536700. ⟨10.1049/PBPO227E_ch12⟩. ⟨hal-04513721⟩
13 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More