Dynamic Bayesian Network based Lithium-ion Battery Health Prognosis for Electric Vehicles
Artikel i vetenskaplig tidskrift, 2020

IEEE Battery prognostics and health management (PHM) are essential for lithium-ion batteries in electric vehicles. In the battery PHM, accurate estimation of the battery state of health (SOH) and prediction of the remaining useful life (RUL) are crucial to ensure safe and efficient battery operation. This paper presents a probabilistic method for the battery degradation modeling and health prognosis based on the features extracted from the charging process using the dynamic Bayesian network (DBN). First, an aggregated feature, combining the incremental capacity analysis (ICA) of constant-current (CC) charging and the time constant of constant-voltage (CV) charging, is developed to characterize the battery degradation dynamics in case some CC or CV charging information is absent. The DBN is then employed to explore the underlying correlation between the battery aging and the extracted features. The proposed model treats the degradation dynamics as a rich family of probability distributions to model real-world battery operation more accurately. Moreover, the battery SOH estimation and RUL prediction are carried out using the particle filtering (PF) inference algorithm. Experimental tests are conducted on two different battery cells and the results show that the proposed methods can provide accurate and robust battery SOH estimation and reliable RUL prediction.

lithium-ion battery

Feature extraction

Prognostics and health management

electric vehicles





dynamic Bayesian network

Bayes methods

Prognostics and health management


Guangzhong Dong

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Weiji Han

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Yujie Wang

University of Science and Technology of China

IEEE Transactions on Industrial Electronics

0278-0046 (ISSN)

Vol. In Press


Hållbar utveckling




Annan kemiteknik

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik



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