Abstract
A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed.
Highlights
In the last decade, there has been a trend of connecting the electric energy and transport systems through the appearance of electric vehicles (EV) and the need for their charging
This paper proposes a novel data-driven approach of EV energy demand modeling based on deep neural networks
The procedure for generating inputs for neural networks (NN)-based energy demand models for the purpose of their training and testing is as follows: (i) splitting of overall driving cycle dataset into two distinctive groups, where 85% of data is used for training of NNs, while remaining
Summary
There has been a trend of connecting the electric energy and transport systems through the appearance of electric vehicles (EV) and the need for their charging. In order to provide optimal EV fleet charging management within smart grids, there is a necessity for accurate models aimed at predicting the energy demand of each individual EV in the fleet, including prediction of battery state of charge (SoC) at destination (e.g., at the charging station). Performing EV simulations within an optimization-based charging/routing management framework can be impractical from the standpoint of computational efficiency Another possibility for modeling the EV energy demand relates to use of computationally efficient response surface-based method [5], where the model parameterization can be conducted off-line based on precise EV model simulations. Two energy demand-related models are derived based on the generated data, in order to predict: (i) the battery SoC and fuel consumption at the end of driving cycle (i.e., at destination), and (ii) all-electric range
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