Modelling and control by neural network of hybrid electric vehicle traction system is presented in this paper. The electric drive is composed by a battery bank and an ultracapacitor connected in parallel through bidirectional DC converters and a Brushless DC Motor driven by a three-phase inverter. In the electric drive control loop is implemented a NARMA neural network. The mechanical model comprises a gearbox and a model of the road-wheel friction force and vehicle aerodynamics. All the masses and inertia are expressed relative to the rotor of the motor. The model is studied by simulations with two driving cycles and an assessment of the available energy from regenerative braking is performed. The percentage of recycled energy from regenerative braking is assessed.
This paper presents a numerical study of the effect of the transmission configuration on the energy consumption of an electric vehicle. The first part of this study is related to a vehicle simulation model that takes into consideration vehicle resistances such as aerodynamic, rolling and inertial resistance as well as the traction force. The model was then validated by means of vehicle acceleration time, from 0 to 100 km/h in the case of a single-speed gearbox. Vehicle power demand and electrical energy consumption were then evaluated over three standardized test cycles: WLTC-Class 3, NEDC and FTP-75. For each cycle, two cases were studied: a single-speed and dual-speed gearbox. Very different power demand was observed between the cycles in terms of maximum and average driving power. The most power-demanding cycle was WLTC, while NEDC was less power demanding. However, the specific driving energy per kilometer was very similar for NEDC and FTP-75, as it respectively accounted to 0.118/0.116 kWh/km and 0.117/0.115 kWh/km. WLTC led to a higher specific consumption of 0.127/0.124 kWh/km. A dual-speed gearbox led to better efficiency, within the range of 1.7% to 2.4%. The higher value was obtained for highly dynamic WLTC.
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