scholarly journals Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles

Energies ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1503 ◽  
Author(s):  
Zuchang Gao ◽  
Cheng Chin ◽  
Joel Chiew ◽  
Junbo Jia ◽  
Caizhi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Real Time ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery System ◽  
Design And Implementation

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 Data-driven Fault Diagnosis of Lithium-ion Battery Over-discharge in Electric Vehicles

2021 ◽  
pp. 1-1
Author(s):  
Naifeng Gan ◽  
Zhenyu Sun ◽  
Zhaosheng Zhang ◽  
Shiqi Xu ◽  
Peng Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Data Driven

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 state of art review and future viewpoint on advance cooling techniques for Lithium–ion battery system of electric vehicles

2020 ◽  
Vol 32 ◽  
pp. 101771
Author(s):  
Amrit Kumar Thakur ◽  
Rajendran Prabakaran ◽  
M.R. Elkadeem ◽  
Swellam W. Sharshir ◽  
Müslüm Arıcı ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery System ◽  
State Of Art

Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles

2016 ◽  
Vol 184 ◽  
pp. 365-374 ◽  
Author(s):  
Zeyu Chen ◽  
Rui Xiong ◽  
Jinpeng Tian ◽  
Xiong Shang ◽  
Jiahuan Lu
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Short Circuit ◽  
Lithium Ion ◽  
Model Based ◽  
Diagnosis Approach

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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