A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles

Measurement ◽  
2018 ◽  
Vol 116 ◽  
pp. 402-411 ◽  
Author(s):  
Xiaoyu Li ◽  
Zhenpo Wang
2021 ◽  
Vol 9 ◽  
Author(s):  
Jia Wang ◽  
Shenglong Zhang ◽  
Xia Hu

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%.


2019 ◽  
Vol 34 (10) ◽  
pp. 9709-9718 ◽  
Author(s):  
Rui Xiong ◽  
Quanqing Yu ◽  
Weixiang Shen ◽  
Cheng Lin ◽  
Fengchun Sun

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19175-19186
Author(s):  
Jiuchun Jiang ◽  
Xinwei Cong ◽  
Shuowei Li ◽  
Caiping Zhang ◽  
Weige Zhang ◽  
...  

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 443-451 ◽  
Author(s):  
Yujie Wang ◽  
Jiaqiang Tian ◽  
Zonghai Chen ◽  
Xingtao Liu

Author(s):  
S. Shawn Lee ◽  
Tae H. Kim ◽  
S. Jack Hu ◽  
Wayne W. Cai ◽  
Jeffrey A. Abell

Automotive battery packs for electric vehicles (EV), hybrid electric vehicles (HEV), and plug-in hybrid electric vehicles (PHEV) typically consist of a large number of battery cells. These cells must be assembled together with robust mechanical and electrical joints. Joining of battery cells presents several challenges such as welding of highly conductive and dissimilar materials, multiple sheets joining, and varying material thickness combinations. In addition, different cell types and pack configurations have implications for battery joining methods. This paper provides a comprehensive review of joining technologies and processes for automotive lithium-ion battery manufacturing. It details the advantages and disadvantages of the joining technologies as related to battery manufacturing, including resistance welding, laser welding, ultrasonic welding and mechanical joining, and discusses corresponding manufacturing issues. Joining processes for electrode-to-tab, tab-to-tab (tab-to-bus bar), and module-to-module assembly are discussed with respect to cell types and pack configuration.


Author(s):  
Naifeng Gan ◽  
Zhenyu Sun ◽  
Zhaosheng Zhang ◽  
Shiqi Xu ◽  
Peng Liu ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document