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

Burak Celen; Melik Bugra Ozcelik; Furkan Metin Turgut; Cisel Aras; Thyagesh Sivaraman; Yash Kotak; Christian Geisbauer; Hans-Georg Schweiger

doi:10.12688/openreseurope.14745.2

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

Open Res Eur. 2023 Feb 22:2:96. doi: 10.12688/openreseurope.14745.2. eCollection 2022.

Authors

Burak Celen¹, Melik Bugra Ozcelik², Furkan Metin Turgut², Cisel Aras², Thyagesh Sivaraman³, Yash Kotak⁴, Christian Geisbauer⁴, Hans-Georg Schweiger⁴

Affiliations

¹ Bogazici University, Istanbul, Turkey.
² AVL Research and Engineering Turkey, Istanbul, Turkey.
³ AVL List GMBH, Graz, 8020, Austria.
⁴ Technische Hochschule Ingolstadt, Ingolstadt, 85049, Germany.

Abstract

Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. 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.

Keywords: XGBoost; artificial neural network; calendar ageing; lithium-ion batteries; machine learning.

Grants and funding

This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No H2020-EU.3.4, 875568 (CObalt-free Batteries for FutuRe Automotive Applications [COBRA]) and by EFRE-supported EU research network project SENSE BAY (grant number EU-1802-0005). This report reflects only the author’s view. The European Commission and the Innovation and Networks Executive Agency (INEA) are not responsible for any use that may be made of the information it contains.