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Energies 2018, 11(1), 136; doi:10.3390/en11010136

Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

1,2
,
1,2,* , 1,2,* and 1,2
1
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
2
Beijing Co-Innovation Center for Electric Vehicles Lecturer, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Received: 13 November 2017 / Revised: 2 January 2018 / Accepted: 3 January 2018 / Published: 5 January 2018
(This article belongs to the Special Issue The International Symposium on Electric Vehicles (ISEV2017))
View Full-Text   |   Download PDF [5313 KB, uploaded 5 January 2018]   |  

Abstract

The battery is a key component and the major fault source in electric vehicles (EVs). Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV) in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis. View Full-Text
Keywords: over-voltage; fault diagnosis; Li-ion batteries; entropy method over-voltage; fault diagnosis; Li-ion batteries; entropy method
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Liu, P.; Sun, Z.; Wang, Z.; Zhang, J. Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles. Energies 2018, 11, 136.

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