A novel Sensor Fault Diagnosis method for Lithium-ion Battery System Using Hybrid System Modeling

2018 ◽  
Author(s):  
Chanzwen Zhen ◽  
Ziqiang Chen ◽  
Deyana Huanz
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Hybrid System ◽  
System Modeling ◽  
Lithium Ion ◽  
Sensor Fault ◽  
Battery System ◽  
Diagnosis Method

A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

2019 ◽  
Vol 34 (10) ◽  
pp. 9709-9718 ◽  
Author(s):  
Rui Xiong ◽  
Quanqing Yu ◽  
Weixiang Shen ◽  
Cheng Lin ◽  
Fengchun Sun
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery Pack ◽  
Sensor Fault ◽  
Diagnosis Method

scholarly journals Advanced Fault Diagnosis for Lithium-Ion Battery Systems

2020 ◽  
Author(s):  
Xiaosong Hu ◽  
Kai Zhang ◽  
Kailong Liu ◽  
Xianke Lin ◽  
Satadru Dey ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Smart Grids ◽  
Lithium Ion ◽  
Sensor Faults ◽  
Actuator Faults ◽  
Safe Operation ◽  
Battery System ◽  
Safety Issues ◽  
Fault Features

Lithium-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles and smart grids. However, various faults in a lithium-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This paper provides a comprehensive review of fault mechanisms, fault features, and fault diagnosis of various faults in LIBS, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

A multi-fault diagnosis method based on modified Sample Entropy for lithium-ion battery strings

2020 ◽  
Vol 446 ◽  
pp. 227275 ◽  
Author(s):  
Yunlong Shang ◽  
Gaopeng Lu ◽  
Yongzhe Kang ◽  
Zhongkai Zhou ◽  
Bin Duan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Sample Entropy ◽  
Diagnosis Method

scholarly journals A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jia Wang ◽  
Shenglong Zhang ◽  
Xia Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Rbf Neural Network ◽  
Lithium Ion ◽  
Rbf Neural Networks ◽  
Battery Packs ◽  
Diagnosis Method

With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

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