TITLE:
Intelligent HEV Fuzzy Logic Control Strategy Based on Identification and Prediction of Drive Cycle and Driving Trend
AUTHORS:
Limin Niu, Hongyuan Yang, Yuhua Zhang
KEYWORDS:
HEV, Neural Network, Drive Cycle Prediction, Driving Trend Prediction
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.3 No.3C,
October
23,
2015
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.