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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.
With the increase of Beijing urban rail transport network, the structure
of the road network is becoming more complex, and passengers have more travel
options. Together with the complex paths and different timetables, taking the last train is
becoming much more difficult and unsuccessful. To avoid losses, we
propose feasible suggestions to the last train with reasonable selling tickets system.