Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data
Journal article, 2023

Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot.

cycle life early prediction

quantile regression forest

interpretable machine learning.

prediction interval

Lithium-ion battery

Author

Huang Zhang

Chalmers, Electrical Engineering, Systems and control

Volvo Group

Yang Su

University Paris-Saclay

Faisal Altaf

Volvo Group

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Sebastien Gros

Norwegian University of Science and Technology (NTNU)

IEEE Transactions on Transportation Electrification

2332-7782 (eISSN)

Vol. 9 2 2669-2682

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)

Probability Theory and Statistics

DOI

10.1109/TTE.2022.3226683

More information

Latest update

7/11/2024