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

Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 443-451 ◽  
Author(s):  
Yujie Wang ◽  
Jiaqiang Tian ◽  
Zonghai Chen ◽  
Xingtao Liu
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery Pack ◽  
Model Based

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%.

scholarly journals A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19175-19186
Author(s):  
Jiuchun Jiang ◽  
Xinwei Cong ◽  
Shuowei Li ◽  
Caiping Zhang ◽  
Weige Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Diagnosis Method
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