Bayesian Network Based State-of-Health Estimation for Battery on Electric Vehicle Application and its Validation Through Real-World Data

State-of-health (SOH) estimation is crucial for ensuring efficient, reliable and safe operation of power battery in electric vehicle (EV) application. However, due to the complicated physicochemical reactions happened in battery cells, it is extremely difficult to accurately estimate SOH, especially...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.11328-11341
Main Authors: Huo, Qian, Ma, Zhikai, Zhao, Xiaoshun, Zhang, Tao, Zhang, Yulong
Format: Article
Language:English
Subjects:
Aging
Batteries
battery aging
Bayesian analysis
Data models
Degradation
Electric vehicle
Electric vehicles
Estimation
Markov chains
Modelling
Monte Carlo simulation
real-world data
State of charge
state-of-health estimation
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Summary:State-of-health (SOH) estimation is crucial for ensuring efficient, reliable and safe operation of power battery in electric vehicle (EV) application. However, due to the complicated physicochemical reactions happened in battery cells, it is extremely difficult to accurately estimate SOH, especially in real-world EV application scenarios. Traditional SOH estimation methods, including both model-based and data-driven ones, are deterministic, which cannot capture the stochastic property of battery aging process aroused from the inherent inconsistency during battery production. In this paper, Bayesian network (BN), which is a probabilistic graphical modeling method for indeterministic process, is used to battery degradation modeling. Its structure is derived from existing knowledge about battery aging mechanism. Two-year operational data and capacity calibration results of 16 electric taxies are collected for model training and validation. Specifically, a systematic data filling procedure is proposed to predict the missing values of variables necessary for SOH estimation. Markov Chain Monte Carlo method is adopted to generate the samples from parameterized BN for SOH estimation. Results show that the estimation result is very close to the calibrated SOH with mean absolute error below 4%. The proposed method is promising to be applied online for SOH estimation in real-world EV application.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3050557