Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

Changwen Zheng, Yunlong Ge, Ziqiang Chen, Deyang Huang, Jian Liu and Shiyao Zhou
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Changwen Zheng: State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Yunlong Ge: State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Ziqiang Chen: State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Deyang Huang: State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Jian Liu: State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Shiyao Zhou: State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2017, vol. 10, issue 11, 1-14

Abstract: The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.

Keywords: battery fault diagnosis; battery management system; parameters estimation; state of charge estimation; adaptive unscented Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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