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Energies 2017, 10(11), 1810; doi:10.3390/en10111810

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

State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
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Received: 24 October 2017 / Revised: 3 November 2017 / Accepted: 4 November 2017 / Published: 9 November 2017
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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. View Full-Text
Keywords: battery fault diagnosis; battery management system; parameters estimation; state of charge estimation; adaptive unscented Kalman filter battery fault diagnosis; battery management system; parameters estimation; state of charge estimation; adaptive unscented Kalman filter
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Zheng, C.; Ge, Y.; Chen, Z.; Huang, D.; Liu, J.; Zhou, S. Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter. Energies 2017, 10, 1810.

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