Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163230
Title: Design of the deep learning based electric vehicle charging forecasting network and navigation system
Authors: Song, Yaofeng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Song, Y. (2022). Design of the deep learning based electric vehicle charging forecasting network and navigation system. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163230
Project: ISM-DISS-02885 
Abstract: In the modern city, the number of Electric Vehicle (EV) is increasing rapidly for its low emission and better dynamic performance, leading to an increasing demand of EV charging. However, due to the limited number of EV charging facilities, catering the huge demand of the time consuming EV charging becomes an unignorable problem. In this paper, we aim to improve the efficiency of the EV charging station usage and save time for EV users by designing a station availability forecasting network and an EV navigation system. On one hand, there are multiple works focusing on time series forecasting using Deep Learning (DL) methods for traffic speed, traffic flow or public transportation demand. However, EV charging availability forecasting is barely mentioned and a big number of DL model designs in the traffic area ignore the importance of spatial information and external factors such as weather and Places of Interest (POI) information. Our design, Attribute-Augmented Spatiotemporal Graph Informer Network (AST-GIN), fully intakes the spatial information and external factors, and outperforms many other state-of-art time series forecasting methods. On the other hand, we build an EV navigation system on the basis of the traffic simulator SUMO for Deep Reinforcement Learning (DRL) experiments.
URI: https://hdl.handle.net/10356/163230
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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