Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System

Qian, Tao; Shao, Chengcheng; Wang, Xiuli; Shahidehpour, Mohammad

doi:10.1109/tsg.2019.2942593

Cited by 163 publications

(60 citation statements)

References 29 publications

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“…The system was able to optimize the total charging rates of electric vehicles and fulfill the charging demand before departure. Authors in [22] proposed a deep reinforcement learning for EV charging navigation with the aim to minimize charging cost (at charging station) and total travel time. The proposed system adaptively learns the optimal strategy without any prior knowledge of system data uncertainties (traffic condition, charging prices, and waiting time at charging stations).…”

Section: Related Workmentioning

confidence: 99%

“…Most of the above-mentioned studies are aimed at managing energy and minimizing costs in EVs, charging stations and smart buildings [16][17][18][19][20][21]. In [22], The proposed system selects the optimum route and charging station without prior knowledge of traffic condition, charging price, and charging waiting time. Due to the extraction of features using optimization techniques from inter-node movements, the proposed system can significantly increase complexity to calculate the feature states in the large-size network.…”

Section: Related Workmentioning

confidence: 99%

“…With respect to charging navigation of electric vehicle on the move, the main challenges are the location of the electric vehicle (the selected route and charging station are different based on electric vehicle location), charging mode (slow/fast charging), battery state of charge (the travel distance is proportional to remain battery status). Other factors include the randomness of user behaviors, traffic conditions, waiting time at the charging station, and charging prices [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…There are few studies for charging station selection. To minimize charging cost and time, the EV charging navigation using reinforcement learning is proposed in [22]. The proposed system selects the optimum route and charging station without prior knowledge of traffic conditions, charging price, and charging waiting time.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

et al. 2020

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This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the total travel time of electric vehicles (EV) charging requests from origin to destination using the selection of the optimal route and charging station considering dynamically changing traffic conditions and unknown future requests. In this paper, we formulate this RCS problem as a Markov decision process model with unknown transition probability. A Deep Q network has been adopted with function approximation to find the optimal electric vehicle charging station (EVCS) selection policy. To obtain the feature states for each EVCS, we define the traffic preprocess module, charging preprocess module and feature extract module. The proposed DRL based RCS algorithm is compared with conventional strategies such as minimum distance, minimum travel time, and minimum waiting time. The performance is evaluated in terms of travel time, waiting time, charging time, driving time, and distance under the various distributions and number of EV charging requests.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

et al. 2020

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“…Reference [52] formulated an empirical model to approximate the degradation probabilities of grid components, and applied reinforcement learning to make maintenance decisions. Reference [53] used deep reinforcement learning to navigate the electric vehicles that need recharging, and the total travel time and cost were greatly minimized.…”

Section: Category 3 Surrogate Modelmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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