International Journal of Control, Automation, and Systems 2023; 21(11): 3563-3573
https://doi.org/10.1007/s12555-023-0377-8
© The International Journal of Control, Automation, and Systems
This study presents a current sensor fault-detecting method for an electric vehicle battery management system. The proposed current sensor fault detector comprises the nonlinear battery cell model, the Luenbergertype state estimator, and a disturbance observer-based current residual generator. The features of this study are summarized as follows: 1) A nonlinear state space representation of the battery cell model is derived so that the disturbance observer considering the engaged current as an external disturbance can be applied, 2) a nonlinear model-based state observer and disturbance observer are combined to deal with the state of charge estimation as well as the unknown current estimation and 3) the concept of the normalized residual is introduced for current sensor fault detection criteria. Because the proposed method can estimate the engaged current whether the current measurement is available or not, the residual between the estimated current and measured current can capture the current sensor fault. Additionally, the normalization process ensures the current sensor fault diagnosis can be realized regardless of the magnitude of the engaged current. The performance of the proposed current sensor fault algorithm was experimentally verified under several magnitudes of engaged current scenarios using a single battery cell.
Keywords Battery management system, disturbance observer, fault diagnosis, fault-tolerant system, lithium-ion battery, state of charge estimation.
International Journal of Control, Automation, and Systems 2023; 21(11): 3563-3573
Published online November 1, 2023 https://doi.org/10.1007/s12555-023-0377-8
Copyright © The International Journal of Control, Automation, and Systems.
Wooyong Kim, Kunwoo Na, and Kyunghwan Choi*
Gwangju Institute of Science and Technology
This study presents a current sensor fault-detecting method for an electric vehicle battery management system. The proposed current sensor fault detector comprises the nonlinear battery cell model, the Luenbergertype state estimator, and a disturbance observer-based current residual generator. The features of this study are summarized as follows: 1) A nonlinear state space representation of the battery cell model is derived so that the disturbance observer considering the engaged current as an external disturbance can be applied, 2) a nonlinear model-based state observer and disturbance observer are combined to deal with the state of charge estimation as well as the unknown current estimation and 3) the concept of the normalized residual is introduced for current sensor fault detection criteria. Because the proposed method can estimate the engaged current whether the current measurement is available or not, the residual between the estimated current and measured current can capture the current sensor fault. Additionally, the normalization process ensures the current sensor fault diagnosis can be realized regardless of the magnitude of the engaged current. The performance of the proposed current sensor fault algorithm was experimentally verified under several magnitudes of engaged current scenarios using a single battery cell.
Keywords: Battery management system, disturbance observer, fault diagnosis, fault-tolerant system, lithium-ion battery, state of charge estimation.
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