Paper Info
Title
Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System
Abstract
A coordinated operation of smart grid (SG) and intelligent transportation system (ITS) provides electric vehicle (EV) owners with a myriad of power and transportation network data for EV charging navigation. However, the optimal charging navigation would be a challenging task owing to the randomness of traffic conditions, charging prices and waiting time at EV charging station (EVCS). In this paper, we propose a deep reinforcement learning (DRL)-based EV charging navigation, aiming at minimizing the total travel time and the charging cost at EVCS. First, we utilize the deterministic shortest charging route model (DSCRM) to extract feature states out of collected stochastic data and then formulate EV charging navigation as a Markov Decision Process (MDP) with an unknown transition probability. The proposed DRL-based approach will approximate the solution, which can adaptively learn the optimal strategy without any prior knowledge of uncertainties. Case studies are carried out within a practical zone in Xi’an city, China. Numerous experimental results verity the effectiveness of the proposed approach and illustrate its adaptation to EV driver preferences. The coordination effect of SG and ITS on reducing the waiting time and the charging cost in EV charging navigations is also analyzed.
Year
DOI
Venue
2020
10.1109/TSG.2019.2942593
IEEE Transactions on Smart Grid
Keywords
Field
DocType
Electric vehicle charging,Roads,Navigation,Feature extraction,Uncertainty,Batteries
Smart grid,Control engineering,Engineering,Intelligent transportation system,Reinforcement learning
Journal
Volume
Issue
ISSN
11
2
1949-3053
Citations 
PageRank 
References 
7
0.47
0
Authors
4
Name
Order
Citations
PageRank
Qian Tao15914.00
Chengcheng Shao2152.17
Xiuli Wang3193.42
Mohammad Shahidehpour435575.28