With aggravation of environmental contamination and energy resource exhaustion, Hybrid Electric Vehicles (HEV) that can be economically operated with low fuel consumption are receiving greater attention. For performance improvement of such HEV, the development of efficient transmission can be seen as one of core technologies such as performance of components and driving strategy. Dual clutch transmission (DCT) is actively studied as a transmission type for HEV due to its advantages of having excellent power transmission efficiency based on manual transmission characteristic, resolving the problem of power interruption, and realizing driving convenience of automatic transmission (AT). In this paper, one diesel HEV equipped with 6-Speed DCT, modelled using MATLAB/Simulink, and a performance simulator developed for this vehicle are introduced. Driving simulation with driving cycles such as FTP75 and NYCC was performed using the developed performance simulator, and the simulated results regarding state of charge and fuel economy, when AT and DCT are applied to this diesel hybrid vehicle respectively, are compared. This performance simulator can be utilized to develop a control algorithm for improving the fuel economy of HEV with DCT.
The plug-in hybrid electric vehicle has a high fuel economy and can be driven long distances. Its different modes include the electric vehicle, hybrid electric vehicle, and only engine operating mode. A power management strategy is important to determine which mode should be selected. The strategy makes the vehicle more efficient using appropriate power sources for driving. However, the strategy usually needs a driving speed profile which is future driving cycle. If the profile is known, the strategy easily determines which mode is driven efficiently. However, it is difficult to estimate the speed profile for a real system. To address this problem, this paper proposes a new power distribution strategy using a neural network. The average speed and driving range are used as input parameters to train the neural network system. The strategy determines a limit for the use of the battery and the desired power is distributed between the engine and the motor simultaneously. Its fuel economy can increase by improving the basic strategy.
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