scholarly journals Capacity Fade Diagnosis of Lithium Ion Battery Pack in Electric Vehicle Base on Fuzzy Neural Network

2014 ◽  
Vol 61 ◽  
pp. 2066-2070 ◽  
Author(s):  
Junqiu Li ◽  
Fei Tan ◽  
Chengning Zhang ◽  
Fengchun Sun
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Fuzzy Neural Network ◽  
Lithium Ion ◽  
Battery Pack ◽  
Capacity Fade ◽  
Fuzzy Neural

Newly Developed Lithium-Ion Battery Pack Technology for a Mass-Market Electric Vehicle

2013 ◽  
Author(s):  
Yukiko Kinoshita ◽  
Toshiro Hirai ◽  
Yasuharu Watanabe ◽  
Yasuo Yamazaki ◽  
Ryuichi Amagai ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Battery Pack ◽  
Mass Market

Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack

2017 ◽  
Vol 10 (2) ◽  
pp. 186 ◽  
Author(s):  
Youssef Cheddadi ◽  
Omar Diouri ◽  
Ahmed Gaga ◽  
Fatima Errahimi ◽  
Najia Es-Sbai
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Pack ◽  
Network State

scholarly journals Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 461
Author(s):  
Hui Gao ◽  
Binbin Zang ◽  
Lei Sun ◽  
Liangliang Chen
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
High Energy ◽  
Assessment Method ◽  
Fuzzy Evaluation ◽  
Electric Vehicle Charging ◽  
Fuzzy Neural

Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of EV charging. The paper presents an integrated safety assessment method for electric vehicle charging safety based on fuzzy neural network. The integrated fault model was established by analyzing the correlation between truck–pile–grid. Then the integrated evaluation index was analyzed and sorted out, and the comprehensive fuzzy evaluation method used to evaluate. Following this, the improved GA_BP neural network algorithm was used to calculate the weight. Compared with the evaluation effect before and after the improvement, the simulation results show that the GA_BP neural network has higher accuracy and smaller error than the ordinary BP neural network. Finally, the feasibility and effectiveness of the evaluation method was verified by a case study.

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