Abstract
Battery-powered electric vehicles (EVs) have a limited on-board energy storage and present the problem of driving mileage anxiety. Moreover, battery energy storage density cannot be effectively improved in a short time, which is a technical bottleneck of EVs. By considering the impact of traffic information on energy consumption forecasting, an energy-saving path planning method for EVs that takes traffic information into account is proposed. The modeling process of the EV model and the construction process of the traffic simulation model are expounded. In addition, the long-term, short-term memory neural network (LSTM) model is selected to predict the energy consumption of EVs, and the sequence to sequence technology is used in the model to integrate the driving condition data of EVs with traffic information. In order to apply the predicted energy consumption to travel guidance, a road planning method with the optimal coupling of energy consumption and distance is proposed. The experimental results show that the energy-based economic path uses 9.9% lower energy consumption and 40.2% shorter travel time than the distance-based path, and a 1.5% lower energy consumption and 18.6% longer travel time than the time-based path.
Highlights
The development of electric vehicles (EVs) can effectively solve environmental pollution, improve urban air quality, and alleviate energy shortage pressure [1,2]
The driving range of the remaining energy of the power battery is generally estimated based on the new european driving cycle (NEDC) [4] and the path planning method commonly used in EVs in travel navigation is based on the shortest travel route [5]
To effectively reduce energy consumption and improve driving range, previous research on the energy management of EVs have investigated nonlinear model predictive control [9], multi-objective optimization [10], RBF-neural network [11], estimation of preceding vehicle future movements [12], global optimization [13], and deep learning [14]; the above-mentioned papers neglect the influence of the actual traffic conditions
Summary
The development of electric vehicles (EVs) can effectively solve environmental pollution, improve urban air quality, and alleviate energy shortage pressure [1,2]. To effectively reduce energy consumption and improve driving range, previous research on the energy management of EVs have investigated nonlinear model predictive control [9], multi-objective optimization [10], RBF-neural network [11], estimation of preceding vehicle future movements [12], global optimization [13], and deep learning [14]; the above-mentioned papers neglect the influence of the actual traffic conditions. To solve such problems, a new modal activity framework for Energies 2019, 12, 3601; doi:10.3390/en12193601 www.mdpi.com/journal/energies.
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