Comparative Analysis of Battery Cycle Life Early Prediction Using Machine Learning Pipeline
Paper in proceeding, 2023

Lithium-Ion battery system is one of the most critical but expensive components for both electric vehicles and stationary energy storage applications. In this regard, accurate and reliable early prediction of battery lifetime is important for optimizing life cycle management of batteries from cradle to grave. In particular, accurate aging diagnostics and prognostics is crucial for ensuring longevity, performance, safety, uptime, productivity, and profitability over a battery's lifetime. However, current state-of-art methods do not provide satisfactory prediction performance (lack of uncertainty quantification) using early degradation data. In the present work, to produce the best model for both battery cycle life point prediction and range prediction (i.e., confidence intervals or prediction intervals), a pipeline-based approach is proposed, in which a full 33-feature set is generated manually based on battery degradation knowledge, and then used to learn the best model among five machine learning (ML) models that have been reported in the battery lifetime prediction literature, and two quantile regression models for battery cycle life prediction. The calibration and sharpness property of battery cycle life range prediction is properly evaluated by their coverage probability and width respectively. The experimental results show that the gradient boosting regression tree model provides the best point prediction performance, while the quantile regression forest model provides the best range prediction performance with both full 33-feature set and the MIT 6-feature set.

cycle life early prediction

uncertainty quantification

Computational intelligence in control

lithium-ion battery

prediction intervals

Author

Huang Zhang

Chalmers, Electrical Engineering, Systems and control

Volvo Group

Faisal Altaf

Volvo Group

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Sebastien Gros

Norwegian University of Science and Technology (NTNU)

IFAC-PapersOnLine

24058971 (ISSN) 24058963 (eISSN)

Vol. 56 2 3757-3763
9781713872344 (ISBN)

22nd IFAC World Congress
Yokohama, Japan,

Classification and Optimal Management of 2nd life xEV Batteries

Swedish Energy Agency (45540-1), 2018-10-15 -- 2023-06-30.

Subject Categories

Bioinformatics (Computational Biology)

DOI

10.1016/j.ifacol.2023.10.1545

More information

Latest update

2/26/2024