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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 596326, 11 pages
Research Article

Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle

School of Transportation and Vehicle Engineering, Shandong University of Technology, No. 12 Zhangzhou Road, Zibo, Shandong 255049, China

Received 15 April 2014; Revised 30 June 2014; Accepted 1 July 2014; Published 26 August 2014

Academic Editor: H. R. Karimi

Copyright © 2014 Ruijun Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.