Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 11, Issue 2, Pages 1714-1723Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2019.2942593
Keywords
Electric vehicle charging; Roads; Navigation; Feature extraction; Uncertainty; Batteries; Intelligent transportation system; deep reinforcement learning; EV charging navigation; Markov decision process; smart grid
Categories
Funding
- National Natural Science Foundation of China [U1766205]
- State Grid Science and Technology Project
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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.
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