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Energies 2015, 8(7), 6509-6527; doi:10.3390/en8076509

Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles

Collaborative Innovation Center of Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
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Author to whom correspondence should be addressed.
Academic Editor: Joeri Van Mierlo
Received: 15 April 2015 / Revised: 19 June 2015 / Accepted: 19 June 2015 / Published: 26 June 2015
(This article belongs to the Special Issue Advances in Plug-in Hybrid Vehicles and Hybrid Vehicles)
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Abstract

The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected. View Full-Text
Keywords: lithium-ion battery; fault diagnosis; faults effects analysis; extended Kalman filter lithium-ion battery; fault diagnosis; faults effects analysis; extended Kalman filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, Z.; He, H. Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles. Energies 2015, 8, 6509-6527.

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