Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method
Artikel i vetenskaplig tidskrift, 2019

This paper develops a new prediction method for the aging trajectory of lithium-ion batteries with significantly reduced experimental tests. This method is driven by data collected from two types of battery operation modes. The first type is accelerated aging tests that are performed under stress factors, such as overcharging, over-discharging and large current rates, and cover most of the battery lifespan. In the second operation mode, the same kinds of cells are aged at normal speeds to generate a partial aging profile. An accelerated aging model is developed based on the first type of data and is then migrated as a new model to describe the normal-speed aging behavior. Under the framework of Bayesian Monte Carlo algorithms, the new model is parameterized based on the second type of data and is used for prediction of the remaining battery aging trajectory. The proposed prediction method is validated on three types of commercial batteries and also compared with two benchmark algorithms. The sensitivity of results to the number of cycles is investigated for both modes. Illustrative results demonstrate that based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories (up to 500 cycles) at a root-mean-square error of less than 2.5% for all considered scenarios. When only using the first five-cycle data for model training, such a prediction error is bounded by 5% for aging trajectories of all the tested batteries.

Bayesian Monte Carlo

Model migration

Aging trajectory prediction


Lithium-ion batteries


Xiaopeng Tang

Hong Kong University of Science and Technology

Changfu Zou

Chalmers, Elektroteknik, System- och reglerteknik

Ke Yao

Hong Kong University of Science and Technology

Jingyi Lu

Hong Kong University of Science and Technology

Yongxiao Xia

Hong Kong University of Science and Technology

Furong Gao

Hong Kong University of Science and Technology

Applied Energy

0306-2619 (ISSN) 18729118 (eISSN)

Vol. 254 113591


Bioinformatik (beräkningsbiologi)


Sannolikhetsteori och statistik



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