Context-Aware Optimal Charging Distribution using Deep Reinforcement Learning
Paper in proceeding, 2020

The expansion of charging infrastructure and the optimal utilization of existing infrastructure are key influencing factors for the future growth of electric mobility. The main objective of this paper is to present a novel methodology which identifies the necessary stakeholders, processes their contextual information and meets their optimality criteria using a constraint satisfaction strategy. A deep reinforcement learning algorithm is used for optimally distributing the electric vehicle charging resources in a smart-mobility ecosystem. The algorithm performs context-aware, constrained-optimization such that the on-demand requests of each stakeholder, e.g., vehicle owner as end-user, grid-operator, fleet-operator, charging-station service operator, is fulfilled. In the proposed methodology, the system learns from the surrounding environment until the optimal charging resource allocation strategy within the limitations of the system constraints is reached. We look at the concept of optimality from the perspective of multiple stakeholders who participate in the smart-mobility ecosystem. A simple use case is presented in detail. Finally, we discuss the potential to develop this concept further to enable more complex digital interactions between the actors of a smart-mobility eco-system.

Deep Reinforcement Learning



Electric Vehicle

Optimal Charging resource distribution


Muddsair Sharif

Fachhochschule Münster - Abteilung Steinfurt

Charitha Buddhika Heendeniya

Fachhochschule Münster - Abteilung Steinfurt

Muhammad Azam Sheikh

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd

Gero Lückemeyer

Fachhochschule Münster - Abteilung Steinfurt

ACM International Conference Proceeding Series

9781450375504 (ISBN)

4th International Conference on Big Data and Internet of Things, BDIOT 2020
Virtual, Online, Singapore,

Subject Categories

Communication Systems

Information Science

Computer Systems



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