ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning

Muddsair Sharif, Charitha Buddhika Heendeniya () and Gero Lückemeyer
Additional contact information
Muddsair Sharif: Hochschule für Technik Stuttgart
Charitha Buddhika Heendeniya: Scuola universitaria professionale della Svizzera italiana
Gero Lückemeyer: Hochschule für Technik Stuttgart

Chapter 12 in iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 2022, pp 199-209 from Springer

Abstract: Abstract Electromobility has profound economic and ecological impacts on human society. Much of the mobility sector’s transformation is catalyzed by digitalization, enabling many stakeholders, such as vehicle users and infrastructure owners, to interact with each other in real time. This article presents a new concept based on deep reinforcement learning to optimize agent interactions and decision-making in a smart mobility ecosystem. The algorithm performs context-aware, constrained optimization that fulfills on-demand requests from each agent. The algorithm can learn from the surrounding environment until the agent interactions reach an optimal equilibrium point in a given context. The methodology implements an automatic template-based approach via a continuous integration and delivery (CI/CD) framework using a GitLab runner and transfers highly computationally intensive tasks over a high-performance computing cluster automatically without manual intervention.

Keywords: Continuous integration and delivery (CI/CD); Electric vehicles (EVs); Autonomous vehicles (AS); Deep reinforcement learning (DRL); Charging station (CS); Context-aware (CA) (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-92096-8_12

Ordering information: This item can be ordered from
http://www.springer.com/9783030920968

DOI: 10.1007/978-3-030-92096-8_12

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().