TY - JOUR
T1 - Voltage Fault Detection and Precaution of Batteries Based on Entropy and Standard Deviation for Electric Vehicles
AU - Wang, Zhenpo
AU - Hong, Jichao
AU - Zhang, Lei
AU - Liu, Peng
N1 - Publisher Copyright:
© 2017 Published by Elsevier Ltd.
PY - 2017
Y1 - 2017
N2 - In operation process of electric vehicles, some factors, such as road conditions, driving habits, vehicle performance and charging environment always affect batteries performance, which can be characterized by batteries voltage to a certain extent. Voltage fault, such as over-voltage or under-voltage will greatly affect the cycle life, state of health and security of batteries. This paper proposed a method of voltage abnormity detection of batteries based on entropy and standard deviation for electric vehicles. The voltage fluctuation data in real time can be obtained by the operation service and management center for electric vehicles. The monitoring data was analyzed based on modified Shannon entropy, and the analysis results can predict the accurate time and the location of battery or battery pack with voltage fault in advance. The standard deviation of entropy was proposed as the security management method, and the abnormity coefficient was set to make real-time evaluation for the level of abnormal voltage. Moreover, the corresponding management strategy was formulated for accurate voltage fault precaution of batteries. This method can be used in not only electric vehicles but also in other areas in complex abnormal fluctuations environment.
AB - In operation process of electric vehicles, some factors, such as road conditions, driving habits, vehicle performance and charging environment always affect batteries performance, which can be characterized by batteries voltage to a certain extent. Voltage fault, such as over-voltage or under-voltage will greatly affect the cycle life, state of health and security of batteries. This paper proposed a method of voltage abnormity detection of batteries based on entropy and standard deviation for electric vehicles. The voltage fluctuation data in real time can be obtained by the operation service and management center for electric vehicles. The monitoring data was analyzed based on modified Shannon entropy, and the analysis results can predict the accurate time and the location of battery or battery pack with voltage fault in advance. The standard deviation of entropy was proposed as the security management method, and the abnormity coefficient was set to make real-time evaluation for the level of abnormal voltage. Moreover, the corresponding management strategy was formulated for accurate voltage fault precaution of batteries. This method can be used in not only electric vehicles but also in other areas in complex abnormal fluctuations environment.
KW - Electric vehicles
KW - Lithium-ion batteries
KW - Modified Shannon entropy
KW - Standard deviation of entropy
KW - Voltage fault
UR - http://www.scopus.com/inward/record.url?scp=85020728738&partnerID=8YFLogxK
U2 - 10.1016/j.egypro.2017.03.611
DO - 10.1016/j.egypro.2017.03.611
M3 - Conference article
AN - SCOPUS:85020728738
SN - 1876-6102
VL - 105
SP - 2163
EP - 2168
JO - Energy Procedia
JF - Energy Procedia
T2 - 8th International Conference on Applied Energy, ICAE 2016
Y2 - 8 October 2016 through 11 October 2016
ER -