Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost's error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.PMID:37645330 | PMC:PMC10446031 | DOI:10.12688/openreseurope.14745.2

Source: Cell Research - August 30, 2023 Category: Cytology Authors: Burak Celen Melik Bugra Ozcelik Furkan Metin Turgut Cisel Aras Thyagesh Sivaraman Yash Kotak Christian Geisbauer Hans-Georg Schweiger Source Type: research