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

In: iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

Author

Listed:
  • Muddsair Sharif

    (Hochschule für Technik Stuttgart)

  • Charitha Buddhika Heendeniya

    (Scuola universitaria professionale della Svizzera italiana)

  • Gero Lückemeyer

    (Hochschule für Technik Stuttgart)

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.

Suggested Citation

  • Muddsair Sharif & Charitha Buddhika Heendeniya & Gero Lückemeyer, 2022. "ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning," Springer Books, in: Volker Coors & Dirk Pietruschka & Berndt Zeitler (ed.), iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, chapter 12, pages 199-209, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-92096-8_12
    DOI: 10.1007/978-3-030-92096-8_12
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