TY - JOUR
T1 - A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles
AU - Li, Xiaoyu
AU - Wang, Zhenpo
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/2
Y1 - 2018/2
N2 - This paper focuses on fault detection based on interclass correlation coefficient (ICC) method for guaranteeing safe and reliable of electric vehicles (EVs). The proposed method calculates ICC values by capturing the off-trend voltage drop and the voltages are extracted from Service and Management Center of electric vehicles. The ICC value is employed to analyze battery fault by ICC principle. The ICC value not only has advanced fault resolution by amplifying the voltage difference, but also can prolong the fault memory by setting moving windows. Moreover, a loop joints the first and last voltages is designed to locate faults in battery pack. In addition, simulation and experiment are employed to validate and analyze the voltage faults. Based on the simulation verification, the appropriate size of moving windows is set to ensuring sensitivity of fault detection method. The experiment results indicate the method can appropriately detect fault signals for EVs.
AB - This paper focuses on fault detection based on interclass correlation coefficient (ICC) method for guaranteeing safe and reliable of electric vehicles (EVs). The proposed method calculates ICC values by capturing the off-trend voltage drop and the voltages are extracted from Service and Management Center of electric vehicles. The ICC value is employed to analyze battery fault by ICC principle. The ICC value not only has advanced fault resolution by amplifying the voltage difference, but also can prolong the fault memory by setting moving windows. Moreover, a loop joints the first and last voltages is designed to locate faults in battery pack. In addition, simulation and experiment are employed to validate and analyze the voltage faults. Based on the simulation verification, the appropriate size of moving windows is set to ensuring sensitivity of fault detection method. The experiment results indicate the method can appropriately detect fault signals for EVs.
KW - Electric vehicles
KW - Fault diagnosis
KW - Interclass correlation coefficient
KW - Lithium-ion batteries
KW - Service and management center for electric
UR - http://www.scopus.com/inward/record.url?scp=85034636570&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2017.11.034
DO - 10.1016/j.measurement.2017.11.034
M3 - Article
AN - SCOPUS:85034636570
SN - 0263-2241
VL - 116
SP - 402
EP - 411
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
ER -
