scholarly journals A Fast Multi-Switched Inductor Balancing System Based on a Fuzzy Logic Controller for Lithium-Ion Battery Packs in Electric Vehicles

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 1034 ◽  
Author(s):  
Xiudong Cui ◽  
Weixiang Shen ◽  
Yunlei Zhang ◽  
Cungang Hu
Keyword(s):  
Fuzzy Logic ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Fuzzy Logic Controller ◽  
Lithium Ion ◽  
Battery Packs

Joining Technologies for Automotive Lithium-Ion Battery Manufacturing: A Review

2010 ◽  
Author(s):  
S. Shawn Lee ◽  
Tae H. Kim ◽  
S. Jack Hu ◽  
Wayne W. Cai ◽  
Jeffrey A. Abell
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Dissimilar Materials ◽  
Lithium Ion ◽  
Cell Types ◽  
Advantages And Disadvantages ◽  
Battery Packs ◽  
Hybrid Electric ◽  
Battery Cells

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.

Evaluating Real-Life Performance of Lithium-Ion Battery Packs in Electric Vehicles

2019 ◽  
Vol 41 (32) ◽  
pp. 1-11
Author(s):  
Verena Klass ◽  
Maårten Behm ◽  
Göran Lindbergh
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Real Life ◽  
Lithium Ion ◽  
Battery Packs

scholarly journals State of Charge Estimation Techniques for Lithium-ION Batteries Used in Electric Vehicle Applications

2021 ◽  
Vol 9 (8) ◽  
pp. 2750-2760
Author(s):  
Nikhil P
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
High Energy ◽  
State Of Charge ◽  
High Energy Density ◽  
Estimation Methods ◽  
Estimation Techniques ◽  
Soc Estimation ◽  
Battery Packs

Abstract: Lithium-ion battery packs constitute an important part of Electric vehicles. The usage of Lithium-ion based chemistries as the source of energy has various advantages like high efficiency, high energy density, high specific energy, longevity among others. However, the management of lithium-ion battery packs require a Battery Management System (BMS). The BMS deals with functions like safety, prevention of abusive usage of battery pack, overcharging & over-discharging protection, cell balancing and others. One of the prominent features of the BMS is the estimation of State of charge (SOC). SOC is like a fuel gauge in automobile, it indicates how much more the battery can be used before charging it again. SOC is also required for other functions of BMS like State of Health (SOH) tracking, Range calculation, power & energy availability calculations. However, there is no means of measuring it directly (at least not on-board a vehicle) or estimating it easily. Various techniques should be used to estimate SOC indirectly. This paper starts from classical techniques that have existed since long time and reviews some of the modern & developing methods for SOC estimation. It contains a brief review about most of these SOC estimation methods, thus highlighting the methodology, advantages & disadvantages of each of these techniques. A brief review of other developing SOC estimation techniques is also provided. Keywords: State of Charge, SOC, Lithium-ion battery packs, Electric vehicles, Kalman Filter.

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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