A Novel Design of SOC Prediction for an Electrical Vehicle Based on the Intelligent Algorithm

2012 ◽  
Vol 468-471 ◽  
pp. 601-606 ◽  
Author(s):  
Hao Qiu ◽  
Zheng Bao Lei ◽  
Tom Zi Ming Qi

This paper is to present a novel design to predict the State of charge (SOC) of the batteries for the Electric Vehicles (EV) using a voltage descent model which has been built based on the analysis of adaptive fuzzy neural intelligent algorithm (AFNIA) and the charge/discharge experimental data of Electric Vehicle. In this design, an improved BP neural network has also been proposed to indicate the correlation between open circuit voltage and SOC. An experiment employed a Lateral Moving and In Situ Steering EV built by Shenzhen Polytechnic. The test and simulation results showed that the intelligent methods can accurately predict the SOC of lithium batteries. The combination of fuzzy control and neural network can achieve an effective way of predicting the SOC of batteries.

2013 ◽  
Vol 724-725 ◽  
pp. 1374-1378
Author(s):  
Sheng Min Cui ◽  
Yuan Lu ◽  
Jin Ping Song ◽  
Jian Feng Wang ◽  
Wen Feng Ding

To study Zn-PANi (polyaniline) battery dynamic characteristics a vehicle power supply based on miniature electric vehicles was designed. And the power battery dynamic test cycle was determined according to the vehicle test cycle prescribed under GB using Land battery testing system. The power battery steady characteristics tests include battery voltage test, per gram capacity test, self-discharge rate test, open circuit voltage and impedance test, cycle life test and short circuit test. Battery discharge characteristics include the relationship between discharge voltage and time, DOD(depth of discharge), the relationship between open circuit voltage, impedance and SOC in different discharge currents. Rationalization proposals in using Zn-PANi batteries efficiently by analyzing battery characteristics, advantages and disadvantages as power batteries are put forward.


IEEE Micro ◽  
1995 ◽  
Vol 15 (4) ◽  
pp. 19-30 ◽  
Author(s):  
W. Pedrycz ◽  
C. Hart Poskar ◽  
P.J. Czerowski

2022 ◽  
Vol 12 (1) ◽  
pp. 461
Author(s):  
Hui Gao ◽  
Binbin Zang ◽  
Lei Sun ◽  
Liangliang Chen

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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