DocumentCode :
630292
Title :
High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network
Author :
Fengwu Zhou ; Lujun Wang ; Huiping Lin ; Zhengyu Lv
Author_Institution :
Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
fYear :
2013
fDate :
3-6 June 2013
Firstpage :
513
Lastpage :
517
Abstract :
The state of charge online estimation of EV/HEV lithium battery with high accuracy is very important, Since it can be used to prolong the battery lifetime and improve its performances. Traditional SOC estimation algorithms have show their drawbacks apparently, so the Adaptive Wavelet Neural Network(AWNN) based SOC estimation model is presented. By using adaptive algorithm to train the model, the accurate online SOC estimation is implemented. The simulation and experiment results are given and show that the proposed algorithm is an effective and feasible method to estimate the SOC of the lithium battery with fastest convergence speed and most high accuracy.
Keywords :
battery powered vehicles; hybrid electric vehicles; learning (artificial intelligence); neural nets; power engineering computing; secondary cells; wavelet transforms; AWNN; EV lithium batteries; HEV lithium batteries; adaptive algorithm; adaptive wavelet neural network; convergence speed; high accuracy state-of-charge online estimation; hybrid electric vehicle; online SOC estimation algorithms; prolong battery lifetime; Batteries; Adaptive Wavelet Neural Network(AWNN); EV/HEV; convergence speed; state of charge(SOC);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
ECCE Asia Downunder (ECCE Asia), 2013 IEEE
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-0483-9
Type :
conf
DOI :
10.1109/ECCE-Asia.2013.6579145
Filename :
6579145
Link To Document :
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