ARTIFICIAL NEURAL NETWORKS CONTROL STRATEGY OF A PARALLEL THROUGH-THE-ROAD PLUG-IN HYBRID VEHICLE
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The increasing amounts of vehicle emissions and vehicle energy consumption are major problems for the environment and energy conservation. Hybrid vehicles, which have less emissions and energy consumption, play more and more important roles in energy efficiency and sustainable development.
The power management strategies of a parallel-through-the-road hybrid architecture vehicle are different from traditional hybrid electric vehicles since one additional dimension is added. To study power management strategies, a simplified model of the vehicle is developed. Four types of power management strategies have been discovered previously based on the simplified model, including dynamic programming model, equivalent consumption minimization strategy, proportional state-of-charge algorithm, and regression model. A new power management strategy, which is artificial neural network model, is developed. All these five power management strategies are compared, and the artificial neural network model is proven to have the best results among the implementable strategies.