Assessment of critical materials and cell design factors for high performance lithium-sulfur batteries using machine learning
Chemical Engineering Journal, cilt.390, 2020 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 390
- Basım Tarihi: 2020
- Doi Numarası: 10.1016/j.cej.2020.124117
- Dergi Adı: Chemical Engineering Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chimica, Compendex, Food Science & Technology Abstracts, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
- Anahtar Kelimeler: Association rule mining, Data mining, Lithium-sulfur batteries, Machine learning, Materials design
- Boğaziçi Üniversitesi Adresli: Evet
Özet
In lithium-sulfur (Li-S) batteries, as a consequence of the highly complex reaction and degradation mechanisms, materials and cell design have a critical impact on the performance. Subsequently, Li-S batteries receive significant research attention. In this report, a comprehensive analysis on the effect of key factors on the battery performance, namely the peak discharge capacity and the cycle life, is conducted using machine learning. Data for 1660 cells from 353 papers in the literature are collected and analyzed via association rule mining. The analysis concludes that encapsulation material type and amount are critical for high capacities and enhanced cycle life. The use of structured carbons such as porous carbons or carbon nanotubes in encapsulated cathodes leads to superior battery performance. Likewise, electrolyte design has a key influence on the battery performance, particularly in electrolyte starved cells. The results confirm that the most promising pathway forward in Li-S batteries is materials design, predominantly design of encapsulation cathodes and electrolytes that succeed at low electrolyte-to-sulfur ratios and high sulfur loadings.