DocumentCode :
568108
Title :
Prediction of battery-SOC of pure electric vehicle
Author :
Wang, Cheng
Author_Institution :
Fac. of Transp. Eng., Huaiyin Inst. of Technol., Huai´´an, China
fYear :
2012
fDate :
14-17 July 2012
Firstpage :
466
Lastpage :
469
Abstract :
The accurate value of battery SOC is one of the prerequisites of a pure electric vehicle energy management to achieve optimal control. However, the battery is a highly nonlinear system and its charge-discharge process is difficult to establish accurate mathematical model. Taking into account nonlinear characteristics of BP neural network, and it has parallel structure and ability to learn, so it is suitable for online estimation of battery SOC value. Train BP neural network through Matlab programming, and predict the performance of the battery based on the built neural network model to get the battery SOC prediction. Simulation results show that the the established BP neural network has good adaptability, and is able to predict the mapping relationship between the battery voltage and SOC. This method is access to battery SOC prediction quickly and easily, the maximum error is less than 1% and the results meet the accuracy requirements.
Keywords :
backpropagation; battery powered vehicles; energy management systems; mathematical analysis; neurocontrollers; nonlinear control systems; optimal control; prediction theory; system-on-chip; BP neural network training; battery SOC value online estimation; battery-SOC prediction; charge-discharge process; mathematical model; nonlinear system; optimal control; parallel structure; pure electric vehicle energy management; Batteries; Biological neural networks; Mathematical model; Neurons; Predictive models; System-on-a-chip; BP Neural Network; Pure Electric Vehicle; SOC;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science & Education (ICCSE), 2012 7th International Conference on
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4673-0241-8
Type :
conf
DOI :
10.1109/ICCSE.2012.6295115
Filename :
6295115
Link To Document :
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