Intelligent HEV Fuzzy Logic Control Strategy Based on Identification and Prediction of Drive Cycle and Driving Trend

Limin Niu, Hongyuan Yang, Yuhua Zhang

School of Mechanical Engineering, Anhui University of Technology, Maanshan, China.

DOI: 10.4236/wjet.2015.33C032                

Abstract

Real-time drive cycles and driving trends have a vital impact on fuel consumption and emissions in a vehicle. To address this issue, an original and alternative approach which incorporates the knowledge about real-time drive cycles and driving trends into fuzzy logic control strategy was proposed. A machine learning framework called MC_FRAME was established, which includes two neural networks for self-learning and making predictions. An intelligent fuzzy logic control strategy based on the MC_FRAME was then developed in a hybrid electric vehicle system, which is called FLCS_MODEL. Simulations were conducted to evaluate the FLCS_MODEL using ADVISOR. The simulation results indicated that comparing with the default controller on the drive cycle NEDC, the FLCS_MODEL saves 12.25% fuel per hundred kilometers, with the HC emissions increasing by 22.7%, the CO emissions reducing by 16.5%, the NOx emissions reducing by 37.5% and with the PM emissions reducing by 12.9%. A conclusion can be drawn that the proposed approach realizes fewer fuel consumption and less emissions.

Keywords

HEV, Neural Network, Drive Cycle Prediction, Driving Trend Prediction

Conflicts of Interest

The authors declare no conflicts of interest.

References

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