scholarly journals Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6255
Author(s):  
Ki-Beom Lee ◽  
Mohamed A. Ahmed ◽  
Dong-Ki Kang ◽  
Young-Chon Kim
Keyword(s):  
Reinforcement Learning ◽  
Travel Time ◽  
Waiting Time ◽  
Process Model ◽  
Transition Probability ◽  
Optimal Route ◽  
Charging Station ◽  
Traffic Conditions ◽  
Model Free ◽  
Ev Charging

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.

scholarly journals Intelligent Ramp Control for Incident Response Using Dyna-QArchitecture

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Chao Lu ◽  
Yanan Zhao ◽  
Jianwei Gong
Keyword(s):  
Reinforcement Learning ◽  
Travel Time ◽  
Single Agent ◽  
Superior Performance ◽  
Model Free ◽  
Road Users ◽  
Total Travel Time ◽  
The Uk ◽  
Traffic Operation ◽  
Ramp Control

Reinforcement learning (RL) has shown great potential for motorway ramp control, especially under the congestion caused by incidents. However, existing applications limited to single-agent tasks and based onQ-learning have inherent drawbacks for dealing with coordinated ramp control problems. For solving these problems, a Dyna-Qbased multiagent reinforcement learning (MARL) system named Dyna-MARL has been developed in this paper. Dyna-Qis an extension ofQ-learning, which combines model-free and model-based methods to obtain benefits from both sides. The performance of Dyna-MARL is tested in a simulated motorway segment in the UK with the real traffic data collected from AM peak hours. The test results compared with Isolated RL and noncontrolled situations show that Dyna-MARL can achieve a superior performance on improving the traffic operation with respect to increasing total throughput, reducing total travel time and CO2emission. Moreover, with a suitable coordination strategy, Dyna-MARL can maintain a highly equitable motorway system by balancing the travel time of road users from different on-ramps.

Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning

2019 ◽  
Vol 10 (5) ◽  
pp. 5246-5257 ◽  
Author(s):  
Zhiqiang Wan ◽  
Hepeng Li ◽  
Haibo He ◽  
Danil Prokhorov
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Model Free ◽  
Ev Charging

Multi-Agent Deep Reinforcement Learning Method for EV Charging Station Game

2021 ◽  
pp. 1-1
Author(s):  
Mohammad Shahidehpour ◽  
Tao Qian ◽  
Chengcheng Shao ◽  
Xuliang Li ◽  
Xiuli Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Method ◽  
Charging Station ◽  
Multi Agent ◽  
Ev Charging

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

scholarly journals The Architecture Design of Electrical Vehicle Infrastructure Using Viable System Model Approach

Systems ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 19
Author(s):  
Mahdi Boucetta ◽  
Niamat Ullah Ibne Hossain ◽  
Raed Jaradat ◽  
Charles Keating ◽  
Siham Tazzit ◽  
...  
Keyword(s):  
Urban Space ◽  
Electric Vehicles ◽  
Reference Model ◽  
System Model ◽  
Ecosystem Structure ◽  
Viable System Model ◽  
Charging Station ◽  
Viable System ◽  
Station Location ◽  
Ev Charging

Exponential technological-based growth in industrialization and urbanization, and the ease of mobility that modern motorization offers have significantly transformed social structures and living standards. As a result, electric vehicles (EVs) have gained widespread popularity as a mode of sustainable transport. The increasing demand for of electric vehicles (EVs) has reduced the some of the environmental issues and urban space requirements for parking and road usage. The current body of EV literature is replete with different optimization and empirical approaches pertaining to the design and analysis of the EV ecosystem; however, probing the EV ecosystem from a management perspective has not been analyzed. To address this gap, this paper develops a systems-based framework to offer rigorous design and analysis of the EV ecosystem, with a focus on charging station location problems. The study framework includes: (1) examination of the EV charging station location problem through the lens of a systems perspective; (2) a systems view of EV ecosystem structure; and (3) development of a reference model for EV charging stations by adopting the viable system model. The paper concludes with the methodological implications and utility of the reference model to offer managerial insights for practitioners and stakeholders.

Sign in / Sign up

Export Citation Format

Share Document