Abstract
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.DOI: http://dx.doi.org/10.5755/j01.eie.24.3.20974
Highlights
The main advantage of electric vehicles, compared to internal combustion engine-driven vehicles is the fact that no local pollution is produced by their propulsion system
In this paper is presented the modelling of a hybrid electric vehicle propulsion system and control by a NARMA neural network of the electric motor
The electric drive is composed by a battery bank and an UltraCapacitor (UC) connected in parallel through bidirectional DC converters
Summary
The main advantage of electric vehicles, compared to internal combustion engine-driven vehicles is the fact that no local pollution is produced by their propulsion system. The control strategies proposed in [4] and [5] use neural network for recognition and prediction of the driving cycles to help reducing the equivalent consumption of the vehicle propulsion system. In this paper is presented the modelling of a hybrid electric vehicle propulsion system and control by a NARMA neural network of the electric motor. The power split between the UC and the electric motor is performed by hierarchical control algorithm that can be implemented into a microcontroller in the vehicle. Such a realization has the advantage of being less complex than predictive control techniques and is less sensitive to the uncertainty in vehicle usage. One of the driving cycles used for simulations is an urban cycle with frequent accelerations and stops and the other is a suburban cycle without any stops
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