Predicting hybrid vehicle fuel economy and emissions with neural network models trained with real world data
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Physics-based hybrid vehicle simulation models for fuel economy (FE) exist but are computationally and financially expensive. These models simulate aspects of real-world drive cycles that include the driving environment, thermal management, driver input, and powertrain component behavior. In this study, an alternative method of hybrid vehicle FE simulation is developed by training and testing a time series neural network (NN) model using real world, on-road data. This enables NN models to model many aspects of on-road vehicle dynamics, like regular traffic stops, turning, and irregular accelerations ...