Next Article in Journal
Variance Characteristics of Tropical Radiosonde Winds Using a Vector-Tensor Method
Next Article in Special Issue
Development and Test Application of an Auxiliary Power-Integrated System
Previous Article in Journal
Study on the Effect of Reciprocating Pump Pipeline System Vibration on Oil Transportation Stations
Previous Article in Special Issue
Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Energies 2018, 11(1), 136;

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

1,2, 1,2,*, 1,2,* and 1,2
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
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))
PDF [5313 KB, uploaded 5 January 2018]


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Liu, P.; Sun, Z.; Wang, Z.; Zhang, J. Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles. Energies 2018, 11, 136.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top