Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method
Paper in proceeding, 2019

Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.

Aging trajectory prediction

Model migration


Bayesian Monte Carlo

Lithium-ion batteries


Xiaopeng Tang

Hong Kong University of Science and Technology

Ke Yao

Hong Kong University of Science and Technology

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

Boyang Liu

Hong Kong University of Science and Technology

Furong Gao

Hong Kong University of Science and Technology

Energy Procedia

18766102 (ISSN)

Vol. 158 2019 2456-2461

10th International Conference on Applied Energy, ICAE 2018
Hong Kong, China,

Subject Categories

Vehicle Engineering

Other Civil Engineering

Probability Theory and Statistics



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