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- W4384207169 abstract "In the present study, the predictive tool based on an artificial neural network is developed by means of the experimental data of two series hybrid electric vehicles. The experiments have been conducted on different driving conditions, including highways, traffic, and combined driving conditions. Then, the artificial neural network is developed and predicts an arbitrary series hybrid electric vehicle's required power. The instantaneous required power is divided into dynamic and steady power to size the combustion engine, electric motor, and high voltage battery of the series hybrid electric vehicle. The effects of different ambient conditions (including ambient temperature and altitude), the inverter and high voltage battery efficiencies, and the coast-down coefficients on the component sizing of the series hybrid electric vehicle are then investigated in different driving conditions. The results revealed that the maximum instantaneous power of the electric motor is affected by the rapid acceleration in low-speed conditions, and the suburban driving route determines the combustion engine's maximum power. Notably, the WLTC driving cycle is the most comprehensive among the available driving cycles, and most of the component's sizes are determined by this cycle except the combustion engine's maximum power. It is also appeared that the cycle-wise investigation can be summarized into the Isfahan-Tehran route and WLTC calculations." @default.
- W4384207169 created "2023-07-14" @default.
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- W4384207169 date "2023-07-01" @default.
- W4384207169 modified "2023-10-02" @default.
- W4384207169 title "WITHDRAWN: Sensitivity analysis of boundary conditions on a series hybrid electric vehicle components through the artificial neural network" @default.
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- W4384207169 doi "https://doi.org/10.1016/j.treng.2023.100194" @default.
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