Economic and Environmental Feasibility of Second-Life Lithium-Ion Batteries as Fast-Charging Energy Storage

2020 ◽  
Vol 54 (11) ◽  
pp. 6878-6887 ◽  
Author(s):  
Dipti Kamath ◽  
Renata Arsenault ◽  
Hyung Chul Kim ◽  
Annick Anctil
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Second Life ◽  
Lithium Ion ◽  
Fast Charging ◽  
Environmental Feasibility ◽  
Charging Energy

Design of Niobium tungsten oxide/C micro-structured electrode for fast charging lithium-ion batteries

2021 ◽  
Author(s):  
Weiwei Liu ◽  
Meng Xu ◽  
Menghua Zhu
Keyword(s):  
Energy Storage ◽  
Tungsten Oxide ◽  
Lithium Ion Batteries ◽  
Electronic Transport ◽  
Electrode Material ◽  
Lithium Ion ◽  
Maximum Energy ◽  
High Rate ◽  
Charging Time ◽  
Fast Charging

The maximum energy storage in minimum charging time is increasingly important to evaluate the performance of lithium ion batteries (LIBs). High rate electrode material requires high ion and electronic transport...

Unusual Stability of Acetonitrile-Based Superconcentrated Electrolytes for Fast-Charging Lithium-Ion Batteries

2014 ◽  
Vol 136 (13) ◽  
pp. 5039-5046 ◽  
Author(s):  
Yuki Yamada ◽  
Keizo Furukawa ◽  
Keitaro Sodeyama ◽  
Keisuke Kikuchi ◽  
Makoto Yaegashi ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Fast Charging

scholarly journals Impact of Electrode and Cell Design on Fast Charging Capabilities of Cylindrical Lithium-Ion Batteries

2020 ◽  
Vol 167 (13) ◽  
pp. 130505
Author(s):  
J. Sturm ◽  
A. Frank ◽  
A. Rheinfeld ◽  
S. V. Erhard ◽  
A. Jossen
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Cell Design ◽  
Fast Charging

scholarly journals Accuracy improvement of SOC estimation in lithium-ion batteries by ANFIS vs ANN modeling of nonlinear cell characteristics

2021 ◽  
Author(s):  
Mohammad Hassan Amir Jamlouie
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Intelligent System ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Training Data ◽  
Accurate Estimation ◽  
Consumption Pattern ◽  
Load Prediction

Over the last century, the energy storage industry has continued to evolve and adapt to changing energy requirements. To run an efficient energy storage system two points must be considered. Firstly, precise load forecasting to determine energy consumption pattern. Secondly, is the correct estimation of state of charge (SOC). In this project there is a model introduced to predict the load consumption based on ANN implemented by MATLAB. The Designed intelligent system introduced for load prediction according to the hypothetical training data related to two years daily based load consumption of a residential area. For another obstacle which is accurate estimation of SOC, two separate models are provided based on ANN and ANFIS for Lithium-ion batteries as an energy storage system. There are several researches in this regard but in this project the author makes an effort to introduce the most efficient based on the MSE of each performance and as a result the method by ANN is found more accurate.

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